Access Sentinel-3 in Native Mode¶

xarray-eopf is a Python package that extends xarray with a custom backend called "eopf-zarr". This backend enables seamless access to ESA EOPF data products stored in the Zarr format, presenting them as analysis-ready data structures.

In this notebook, we demonstrate how to use the xarray-eopf backend to access Sentinel-3 EOPF Zarr products in native mode. All data access is lazy, meaning that data is only loaded when required—for example, during plotting or when writing to storage.

For a general introduction to the xarray EOPF backend, see the introduction notebook.

• 🐙 GitHub: EOPF Sample Service – xarray-eopf
• ❗ Issue Tracker: Submit or view issues
• 📘 Documentation: xarray-eopf Docs

---

Import Modules¶

The xarray-eopf backend is implemented as a plugin for xarray. Once installed, it registers automatically and requires no additional import. You can simply import xarray as usual:

In [1]:

import matplotlib.pyplot as plt
import pystac_client
import xarray as xr

import matplotlib.pyplot as plt import pystac_client import xarray as xr

---

Open a Sentinel-3 OLCI L1 EFR¶

We begin with an example that accesses a Sentinel-3 OLCI L1 EFR product in native mode.

Find a Sentinel-3 OLCI L1 EFR Zarr Sample via STAC¶

To obtain a product URL, you can use the STAC Browser to search for a Sentinel-3 OLCI Level-1 EFR tile.

In [2]:

catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu")
items = list(
    catalog.search(
        collections=["sentinel-3-olci-l1-efr"],
        bbox=[7.2, 44.5, 7.4, 44.7],
        datetime=["2026-03-13", "2026-03-13"],
    ).items()
)
items

catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu") items = list( catalog.search( collections=["sentinel-3-olci-l1-efr"], bbox=[7.2, 44.5, 7.4, 44.7], datetime=["2026-03-13", "2026-03-13"], ).items() ) items

Out[2]:

[<Item id=S3B_OL_1_EFR____20260313T100048_20260313T100348_20260313T120352_0179_117_350_2160_ESA_O_NR_004>,
 <Item id=S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004>]

Next, we can inspect the item’s contents. The asset "product" links to the entire Zarr store. The additional field xarray:open_datatree_kwargs has been included in the asset "product", which provides the arguments needed to open the product using Xarray’s eopf-zarr engine.

In [3]:

item = items[1]
item

item = items[1] item

Out[3]:

<Item id=S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004>

• type "Feature"
• stac_version "1.1.0"
• stac_extensions[] 7 items

  • 0 "https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"

  • 1 "https://stac-extensions.github.io/sat/v1.0.0/schema.json"

  • 2 "https://stac-extensions.github.io/eo/v2.0.0/schema.json"

  • 3 "https://stac-extensions.github.io/processing/v1.2.0/schema.json"

  • 4 "https://stac-extensions.github.io/product/v0.1.0/schema.json"

  • 5 "https://cs-si.github.io/eopf-stac-extension/v1.2.0/schema.json"

  • 6 "https://stac-extensions.github.io/version/v1.2.0/schema.json"
• id "S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004"
• geometry

  • type "Polygon"
  • coordinates[] 1 items

    • 0[] 47 items

      • 0[] 2 items

        • 0 -4.7861

        • 1 41.9789

      • 1[] 2 items

        • 0 -3.93914

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        • 0 -3.96588

        • 1 47.2211

      • 45[] 2 items

        • 0 -4.37342

        • 1 44.5983

      • 46[] 2 items

        • 0 -4.7861

        • 1 41.9789
• bbox[] 4 items

  • 0 -4.7861

  • 1 39.5543

  • 2 15.2194

  • 3 52.4622
• properties

  • gsd 300.0
  • created "2026-03-14T11:00:54.764536Z"
  • mission "Sentinel-3"
  • updated "2026-03-14T11:00:54.764536Z"
  • datetime "2026-03-13T10:02:18.256763Z"
  • platform "sentinel-3b"
  • providers[] 3 items

    • 0

      • url "https://commission.europa.eu/"
      • name "European Commission"
      • roles[] 1 items

        • 0 "licensor"

    • 1

      • url "https://sentinel.esa.int/web/sentinel/missions/sentinel-3"
      • name "ESA"
      • roles[] 2 items

        • 0 "producer"

        • 1 "processor"

    • 2

      • url "https://zarr.eopf.copernicus.eu/"
      • name "EOPF Sentinel Zarr Samples Service"
      • roles[] 2 items

        • 0 "host"

        • 1 "processor"
  • published "2026-03-14T11:00:54.764536Z"
  • deprecated False
  • instruments[] 1 items

    • 0 "olci"
  • end_datetime "2026-03-13T10:03:48.256763Z"
  • product:type "S03OLCEFR"
  • constellation "sentinel-3"
  • start_datetime "2026-03-13T10:00:48.256763Z"
  • sat:orbit_state "descending"
  • processing:level "L1"
  • processing:lineage "systematic"
  • processing:version "03.50"
  • product:timeliness "P1M"
  • sat:absolute_orbit 41047
  • sat:relative_orbit 350
  • processing:facility "OPE"
  • processing:software

    • PUG "03.50"
    • EOPF-CPM "2.6.2"
  • product:timeliness_category "NT"
  • sat:platform_international_designator "2018-039A"
• links[] 5 items

  • 0

    • rel "collection"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-3-olci-l1-efr"
    • type "application/json"

  • 1

    • rel "parent"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-3-olci-l1-efr"
    • type "application/json"

  • 2

    • rel "root"
    • href "https://stac.core.eopf.eodc.eu"
    • type "application/json"
    • title "EOPF Sentinel Zarr Samples Service STAC API"

  • 3

    • rel "self"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-3-olci-l1-efr/items/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004"
    • type "application/geo+json"

  • 4

    • rel "license"
    • href "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice"
    • type "application/pdf"
    • title "Legal notice on the use of Copernicus Sentinel Data and Service Information"
• assets

  • product

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr"
    • type "application/vnd+zarr"
    • title "EOPF Product"
    • description "The full Zarr store of the EOPF product"
    • xarray:open_datatree_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 2 items

      • 0 "data"

      • 1 "metadata"
  • radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition bands 01 to 21"
    • bands[] 21 items

      • 0

        • name "Oa01"
        • eo:center_wavelength 400
        • eo:full_width_half_max 15

      • 1

        • name "Oa02"
        • eo:center_wavelength 412.5
        • eo:full_width_half_max 10

      • 2

        • name "Oa03"
        • eo:common_name "coastal"
        • eo:center_wavelength 442.5
        • eo:full_width_half_max 10

      • 3

        • name "Oa04"
        • eo:common_name "blue"
        • eo:center_wavelength 490
        • eo:full_width_half_max 10

      • 4

        • name "Oa05"
        • eo:common_name "green05"
        • eo:center_wavelength 510
        • eo:full_width_half_max 10

      • 5

        • name "Oa06"
        • eo:common_name "green"
        • eo:center_wavelength 560
        • eo:full_width_half_max 10

      • 6

        • name "Oa07"
        • eo:common_name "yellow"
        • eo:center_wavelength 620
        • eo:full_width_half_max 10

      • 7

        • name "Oa08"
        • eo:center_wavelength 665
        • eo:full_width_half_max 10

      • 8

        • name "Oa09"
        • eo:common_name "red"
        • eo:center_wavelength 673.75
        • eo:full_width_half_max 7.5

      • 9

        • name "Oa10"
        • eo:center_wavelength 681.25
        • eo:full_width_half_max 7.5

      • 10

        • name "Oa11"
        • eo:common_name "rededge071"
        • eo:center_wavelength 708.75
        • eo:full_width_half_max 10

      • 11

        • name "Oa12"
        • eo:common_name "rededge075"
        • eo:center_wavelength 753.75
        • eo:full_width_half_max 7.5

      • 12

        • name "Oa13"
        • eo:center_wavelength 761.25
        • eo:full_width_half_max 2.5

      • 13

        • name "Oa14"
        • eo:center_wavelength 764.375
        • eo:full_width_half_max 3.75

      • 14

        • name "Oa15"
        • eo:center_wavelength 767.5
        • eo:full_width_half_max 2.5

      • 15

        • name "Oa16"
        • eo:common_name "rededge078"
        • eo:center_wavelength 778.75
        • eo:full_width_half_max 15

      • 16

        • name "Oa17"
        • eo:common_name "nir08"
        • eo:center_wavelength 865
        • eo:full_width_half_max 20

      • 17

        • name "Oa18"
        • eo:center_wavelength 885
        • eo:full_width_half_max 10

      • 18

        • name "Oa19"
        • eo:center_wavelength 900
        • eo:full_width_half_max 10

      • 19

        • name "Oa20"
        • eo:common_name "nir09"
        • eo:center_wavelength 940
        • eo:full_width_half_max 20

      • 20

        • name "Oa21"
        • eo:center_wavelength 1020
        • eo:full_width_half_max 40
    • xarray:open_dataset_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 2 items

      • 0 "data"

      • 1 "dataset"
  • zipped_product

    • href "https://download.user.eopf.eodc.eu/zip/collections/sentinel-3-olci-l1-efr/items/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zip"
    • type "application/zip"
    • title "Zipped EOPF Product"
    • description "The full EOPF Zarr store as zip archive"
    • roles[] 3 items

      • 0 "data"

      • 1 "metadata"

      • 2 "archive"
  • product_metadata

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/.zmetadata"
    • type "application/json"
    • title "Consolidated Metadata"
    • description "Consolidated metadata of the EOPF product"
    • roles[] 1 items

      • 0 "metadata"
  • Oa01_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa01_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa01"
    • bands[] 1 items

      • 0

        • name "Oa01"
        • eo:center_wavelength 400
        • eo:full_width_half_max 15
    • roles[] 1 items

      • 0 "data"
  • Oa02_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa02_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa02"
    • bands[] 1 items

      • 0

        • name "Oa02"
        • eo:center_wavelength 412.5
        • eo:full_width_half_max 10
    • roles[] 1 items

      • 0 "data"
  • Oa03_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa03_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa03"
    • bands[] 1 items

      • 0

        • name "Oa03"
        • eo:common_name "coastal"
        • eo:center_wavelength 442.5
        • eo:full_width_half_max 10
    • roles[] 1 items

      • 0 "data"
  • Oa04_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa04_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa04"
    • bands[] 1 items

      • 0

        • name "Oa04"
        • eo:common_name "blue"
        • eo:center_wavelength 490
        • eo:full_width_half_max 10
    • roles[] 1 items

      • 0 "data"
  • Oa05_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa05_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa05"
    • bands[] 1 items

      • 0

        • name "Oa05"
        • eo:common_name "green05"
        • eo:center_wavelength 510
        • eo:full_width_half_max 10
    • roles[] 1 items

      • 0 "data"
  • Oa06_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa06_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa06"
    • bands[] 1 items

      • 0

        • name "Oa06"
        • eo:common_name "green"
        • eo:center_wavelength 560
        • eo:full_width_half_max 10
    • roles[] 1 items

      • 0 "data"
  • Oa07_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa07_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa07"
    • bands[] 1 items

      • 0

        • name "Oa07"
        • eo:common_name "yellow"
        • eo:center_wavelength 620
        • eo:full_width_half_max 10
    • roles[] 1 items

      • 0 "data"
  • Oa08_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa08_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa08"
    • bands[] 1 items

      • 0

        • name "Oa08"
        • eo:center_wavelength 665
        • eo:full_width_half_max 10
    • roles[] 1 items

      • 0 "data"
  • Oa09_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa09_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa09"
    • bands[] 1 items

      • 0

        • name "Oa09"
        • eo:common_name "red"
        • eo:center_wavelength 673.75
        • eo:full_width_half_max 7.5
    • roles[] 1 items

      • 0 "data"
  • Oa10_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa10_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa10"
    • bands[] 1 items

      • 0

        • name "Oa10"
        • eo:center_wavelength 681.25
        • eo:full_width_half_max 7.5
    • roles[] 1 items

      • 0 "data"
  • Oa11_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa11_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa11"
    • bands[] 1 items

      • 0

        • name "Oa11"
        • eo:common_name "rededge071"
        • eo:center_wavelength 708.75
        • eo:full_width_half_max 10
    • roles[] 1 items

      • 0 "data"
  • Oa12_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa12_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa12"
    • bands[] 1 items

      • 0

        • name "Oa12"
        • eo:common_name "rededge075"
        • eo:center_wavelength 753.75
        • eo:full_width_half_max 7.5
    • roles[] 1 items

      • 0 "data"
  • Oa13_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa13_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa13"
    • bands[] 1 items

      • 0

        • name "Oa13"
        • eo:center_wavelength 761.25
        • eo:full_width_half_max 2.5
    • roles[] 1 items

      • 0 "data"
  • Oa14_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa14_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa14"
    • bands[] 1 items

      • 0

        • name "Oa14"
        • eo:center_wavelength 764.375
        • eo:full_width_half_max 3.75
    • roles[] 1 items

      • 0 "data"
  • Oa15_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa15_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa15"
    • bands[] 1 items

      • 0

        • name "Oa15"
        • eo:center_wavelength 767.5
        • eo:full_width_half_max 2.5
    • roles[] 1 items

      • 0 "data"
  • Oa16_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa16_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa16"
    • bands[] 1 items

      • 0

        • name "Oa16"
        • eo:common_name "rededge078"
        • eo:center_wavelength 778.75
        • eo:full_width_half_max 15
    • roles[] 1 items

      • 0 "data"
  • Oa17_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa17_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa17"
    • bands[] 1 items

      • 0

        • name "Oa17"
        • eo:common_name "nir08"
        • eo:center_wavelength 865
        • eo:full_width_half_max 20
    • roles[] 1 items

      • 0 "data"
  • Oa18_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa18_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa18"
    • bands[] 1 items

      • 0

        • name "Oa18"
        • eo:center_wavelength 885
        • eo:full_width_half_max 10
    • roles[] 1 items

      • 0 "data"
  • Oa19_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa19_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa19"
    • bands[] 1 items

      • 0

        • name "Oa19"
        • eo:center_wavelength 900
        • eo:full_width_half_max 10
    • roles[] 1 items

      • 0 "data"
  • Oa20_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa20_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa20"
    • bands[] 1 items

      • 0

        • name "Oa20"
        • eo:common_name "nir09"
        • eo:center_wavelength 940
        • eo:full_width_half_max 20
    • roles[] 1 items

      • 0 "data"
  • Oa21_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcefr-eu/13/products/cpm_v262/S3B_OL_1_EFR____20260313T100048_20260313T100348_20260314T100820_0180_117_350_2160_ESA_O_NT_004.zarr/measurements/oa21_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa21"
    • bands[] 1 items

      • 0

        • name "Oa21"
        • eo:center_wavelength 1020
        • eo:full_width_half_max 40
    • roles[] 1 items

      • 0 "data"
• collection "sentinel-3-olci-l1-efr"

Open Sentinel-3 OLCI L1 EFR as DataTree¶

We can use the "product" asset to obtain the href and xarray:open_datatree_kwargs from the STAC item, and open the product as an xarray.DataTree as shown below:

In [4]:

dt = xr.open_datatree(
    item.assets["product"].href,
    **item.assets["product"].extra_fields["xarray:open_datatree_kwargs"]
)
dt

dt = xr.open_datatree( item.assets["product"].href, **item.assets["product"].extra_fields["xarray:open_datatree_kwargs"] ) dt

Out[4]:

<xarray.DatasetView> Size: 0B
Dimensions:  ()
Data variables:
    *empty*
Attributes: (3)

xarray.DataTree

• Groups: (3)
  <xarray.DatasetView> Size: 0B
  Dimensions:  ()
  Data variables:
      *empty*

  conditions

  • Groups: (5)
    <xarray.DatasetView> Size: 15MB
    Dimensions:    (rows: 4091, columns: 77)
    Coordinates:
        latitude   (rows, columns) float64 3MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        longitude  (rows, columns) float64 3MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
    Dimensions without coordinates: rows, columns
    Data variables:
        oaa        (rows, columns) float64 3MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        oza        (rows, columns) float64 3MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        saa        (rows, columns) float64 3MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        sza        (rows, columns) float64 3MB dask.array<chunksize=(1024, 77), meta=np.ndarray>

    geometry

    • Groups: (0)
    • Dimensions:
      • rows: 4091
      • columns: 77
    • Coordinates: (2)
      • latitude
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        latitude of tie points

        standard_name :

        latitude

        units :

        degrees_north

        valid_max :

        90000000

        valid_min :

        -90000000

        Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • longitude
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        longitude of tie points

        standard_name :

        longitude

        units :

        degrees_east

        valid_max :

        180000000

        valid_min :

        -180000000

        Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

    • Data variables: (4)
      • oaa
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<i4', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': -2147483648, 'long_name': 'observation (viewing) azimuth angle', 'scale_factor': 1e-06, 'short_name': 'oaa', 'units': 'degrees', 'valid_max': 180000000, 'valid_min': -180000000}

        dtype :

        <i4

        long_name :

        observation (viewing) azimuth angle

        units :

        degrees

        Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oza
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u4', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 4294967295, 'long_name': 'observation (viewing) zenith angle', 'scale_factor': 1e-06, 'short_name': 'oza', 'units': 'degrees', 'valid_max': 180000000, 'valid_min': 0}

        dtype :

        <u4

        long_name :

        observation (viewing) zenith angle

        units :

        degrees

        Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • saa
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<i4', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': -2147483648, 'long_name': 'sun azimuth angle', 'scale_factor': 1e-06, 'short_name': 'saa', 'units': 'degrees', 'valid_max': 180000000, 'valid_min': -180000000}

        dtype :

        <i4

        long_name :

        sun azimuth angle

        units :

        degrees

        Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • sza
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u4', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 4294967295, 'long_name': 'sun zenith angle', 'scale_factor': 1e-06, 'short_name': 'sza', 'units': 'degrees', 'valid_max': 180000000, 'valid_min': 0}

        dtype :

        <u4

        long_name :

        sun zenith angle

        units :

        degrees

        Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

    • Attributes: (0)

    <xarray.DatasetView> Size: 518MB
    Dimensions:         (rows: 4091, columns: 4865)
    Coordinates:
        altitude        (rows, columns) int16 40MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
        latitude        (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
        longitude       (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
        time_stamp      (rows) datetime64[ns] 33kB dask.array<chunksize=(1024,), meta=np.ndarray>
    Dimensions without coordinates: rows, columns
    Data variables:
        detector_index  (rows, columns) float32 80MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
        frame_offset    (rows, columns) float32 80MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    image

    • Groups: (0)
    • Dimensions:
      • rows: 4091
      • columns: 4865
    • Coordinates: (4)
      • altitude
        (rows, columns)
        int16
        dask.array<chunksize=(1024, 1024), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <i2

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f4

        fill_value :

        -32768

        long_name :

        DEM corrected altitude

        standard_name :

        altitude

        units :

        m

        valid_max :

        9000

        valid_min :

        -1000

        Array Chunk Bytes 37.96 MiB 2.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type int16 numpy.ndarray

      • latitude
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 1024), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        DEM corrected latitude

        standard_name :

        latitude

        units :

        degrees_north

        valid_max :

        90000000

        valid_min :

        -90000000

        Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

      • longitude
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 1024), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        DEM corrected longitude

        standard_name :

        longitude

        units :

        degrees_east

        valid_max :

        180000000

        valid_min :

        -180000000

        Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

      • time_stamp
        (rows)
        datetime64[ns]
        dask.array<chunksize=(1024,), meta=np.ndarray>

        dimensions :

        ['rows']

        dtype :

        <M8[ns]

        fill_value :

        -1

        long_name :

        elapsed time since 01 Jan 2000 00:00:00 as time stamp for each row

        standard_name :

        time

        Array Chunk Bytes 31.96 kiB 8.00 kiB Shape (4091,) (1024,) Dask graph 4 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

    • Data variables: (2)
      • detector_index
        (rows, columns)
        float32
        dask.array<chunksize=(1024, 1024), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'fill_value': -1, 'long_name': 'detector index for each pixel', 'short_name': 'detector_index', 'valid_max': 3699, 'valid_min': 0}

        dtype :

        <i2

        long_name :

        detector index for each pixel

        Array Chunk Bytes 75.92 MiB 4.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float32 numpy.ndarray

      • frame_offset
        (rows, columns)
        float32
        dask.array<chunksize=(1024, 1024), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<i1', 'eopf_is_masked': True, 'fill_value': -128, 'long_name': 'resampling along-track frame offset', 'short_name': 'frame_offset', 'valid_max': 15, 'valid_min': -15}

        dtype :

        <i1

        long_name :

        resampling along-track frame offset

        Array Chunk Bytes 75.92 MiB 4.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float32 numpy.ndarray

    • Attributes: (0)

    <xarray.DatasetView> Size: 964kB
    Dimensions:                       (bands: 21, detectors: 3700, bands2: 21)
    Coordinates:
      * detectors                     (detectors) int64 30kB 0 1 2 ... 3698 3699
    Dimensions without coordinates: bands, bands2
    Data variables:
        fwhm                          (bands, detectors) float32 311kB dask.array<chunksize=(21, 3700), meta=np.ndarray>
        lambda0                       (bands, detectors) float32 311kB dask.array<chunksize=(21, 3700), meta=np.ndarray>
        relative_spectral_covariance  (bands, bands2) float32 2kB dask.array<chunksize=(21, 21), meta=np.ndarray>
        solar_flux                    (bands, detectors) float32 311kB dask.array<chunksize=(21, 3700), meta=np.ndarray>

    instrument

    • Groups: (0)
    • Dimensions:
      • bands: 21
      • detectors: 3700
      • bands2: 21
    • Coordinates: (1)
      • detectors
        (detectors)
        int64
        0 1 2 3 4 ... 3696 3697 3698 3699

        dimensions :

        ['detectors']

        dtype :

        <i8

        long_name :

        instrument detectors

        array([   0,    1,    2, ..., 3697, 3698, 3699], shape=(3700,))

    • Data variables: (4)
      • fwhm
        (bands, detectors)
        float32
        dask.array<chunksize=(21, 3700), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['detectors'], 'dimensions': ['bands', 'detectors'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'OLCI bandwidth (Full Width at Half Maximum)', 'short_name': 'fwhm', 'units': 'nm', 'valid_max': 650.0, 'valid_min': 0.0}

        dtype :

        <f4

        long_name :

        OLCI bandwidth (Full Width at Half Maximum)

        units :

        nm

        Array Chunk Bytes 303.52 kiB 303.52 kiB Shape (21, 3700) (21, 3700) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • lambda0
        (bands, detectors)
        float32
        dask.array<chunksize=(21, 3700), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['detectors'], 'dimensions': ['bands', 'detectors'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'OLCI characterised central wavelength', 'short_name': 'lambda0', 'units': 'nm', 'valid_max': 1040.0, 'valid_min': 390.0}

        dtype :

        <f4

        long_name :

        OLCI characterised central wavelength

        units :

        nm

        Array Chunk Bytes 303.52 kiB 303.52 kiB Shape (21, 3700) (21, 3700) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • relative_spectral_covariance
        (bands, bands2)
        float32
        dask.array<chunksize=(21, 21), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['bands', 'bands2'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': 0, 'long_name': 'relative spectral covariance matrix', 'short_name': 'relative_spectral_covariance'}

        dtype :

        <f4

        long_name :

        relative spectral covariance matrix

        Array Chunk Bytes 1.72 kiB 1.72 kiB Shape (21, 21) (21, 21) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • solar_flux
        (bands, detectors)
        float32
        dask.array<chunksize=(21, 3700), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['detectors'], 'dimensions': ['bands', 'detectors'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'in-band solar irradiance, seasonally corrected', 'short_name': 'solar_flux', 'units': 'mW.m-2.nm-1', 'valid_max': 2500.0, 'valid_min': 500.0}

        dtype :

        <f4

        long_name :

        in-band solar irradiance, seasonally corrected

        units :

        mW.m-2.nm-1

        Array Chunk Bytes 303.52 kiB 303.52 kiB Shape (21, 3700) (21, 3700) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    • Attributes: (0)

    <xarray.DatasetView> Size: 44MB
    Dimensions:                          (pressure_level: 25, rows: 4091,
                                          columns: 77, wind_vector: 2)
    Coordinates:
        latitude                         (rows, columns) float64 3MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        longitude                        (rows, columns) float64 3MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
      * pressure_level                   (pressure_level) int64 200B 0 1 2 ... 23 24
      * wind_vector                      (wind_vector) int64 16B 0 1
    Dimensions without coordinates: rows, columns
    Data variables:
        atmospheric_temperature_profile  (pressure_level, rows, columns) float32 32MB dask.array<chunksize=(9, 1024, 77), meta=np.ndarray>
        horizontal_wind                  (wind_vector, rows, columns) float32 3MB dask.array<chunksize=(2, 1024, 77), meta=np.ndarray>
        humidity                         (rows, columns) float32 1MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        sea_level_pressure               (rows, columns) float32 1MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        total_columnar_water_vapour      (rows, columns) float32 1MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        total_ozone                      (rows, columns) float32 1MB dask.array<chunksize=(1024, 77), meta=np.ndarray>

    meteorology

    • Groups: (0)
    • Dimensions:
      • pressure_level: 25
      • rows: 4091
      • columns: 77
      • wind_vector: 2
    • Coordinates: (4)
      • latitude
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        latitude of tie points

        standard_name :

        latitude

        units :

        degrees_north

        valid_max :

        90000000

        valid_min :

        -90000000

        Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • longitude
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        longitude of tie points

        standard_name :

        longitude

        units :

        degrees_east

        valid_max :

        180000000

        valid_min :

        -180000000

        Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • pressure_level
        (pressure_level)
        int64
        0 1 2 3 4 5 6 ... 19 20 21 22 23 24

        dimensions :

        ['pressure_level']

        dtype :

        <i8

        long_name :

        coordinates of the vertical temperature profile

        array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
               18, 19, 20, 21, 22, 23, 24])

      • wind_vector
        (wind_vector)
        int64
        0 1

        dimensions :

        ['wind_vector']

        dtype :

        <i8

        long_name :

        dimensions of horizontal wind vector

        array([0, 1])

    • Data variables: (6)
      • atmospheric_temperature_profile
        (pressure_level, rows, columns)
        float32
        dask.array<chunksize=(9, 1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'pressure_level', 'longitude'], 'dimensions': ['pressure_level', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'air temperature vertical profile', 'short_name': 'atmospheric_temperature_profile', 'standard_name': 'air_temperature', 'units': 'K', 'valid_max': 400.0, 'valid_min': 0.0}

        dtype :

        <f4

        long_name :

        air temperature vertical profile

        standard_name :

        air_temperature

        units :

        K

        Array Chunk Bytes 30.04 MiB 2.71 MiB Shape (25, 4091, 77) (9, 1024, 77) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

      • horizontal_wind
        (wind_vector, rows, columns)
        float32
        dask.array<chunksize=(2, 1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['wind_vector', 'latitude', 'longitude'], 'dimensions': ['wind_vector', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': 9.969209968386869e+36, 'long_name': 'horizontal wind vector at 10m altitude', 'short_name': 'horizontal_wind', 'units': 'm.s-1', 'valid_max': 100.0, 'valid_min': -100.0}

        dtype :

        <f4

        long_name :

        horizontal wind vector at 10m altitude

        units :

        m.s-1

        Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (2, 4091, 77) (2, 1024, 77) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

      • humidity
        (rows, columns)
        float32
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'relative humidity', 'short_name': 'humidity', 'standard_name': 'relative_humidity', 'units': '%', 'valid_max': 100.0, 'valid_min': 0.0}

        dtype :

        <f4

        long_name :

        relative humidity

        standard_name :

        relative_humidity

        units :

        %

        Array Chunk Bytes 1.20 MiB 308.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

      • sea_level_pressure
        (rows, columns)
        float32
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'mean sea level pressure', 'short_name': 'sea_level_pressure', 'standard_name': 'air_pressure_at_sea_level', 'units': 'hPa', 'valid_max': 1500.0, 'valid_min': 0.0}

        dtype :

        <f4

        long_name :

        mean sea level pressure

        standard_name :

        air_pressure_at_sea_level

        units :

        hPa

        Array Chunk Bytes 1.20 MiB 308.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

      • total_columnar_water_vapour
        (rows, columns)
        float32
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'total column water vapour content', 'short_name': 'total_columnar_water_vapour', 'standard_name': 'atmosphere_water_vapor_content', 'units': 'kg.m-2', 'valid_max': 100.0, 'valid_min': 0.0}

        dtype :

        <f4

        long_name :

        total column water vapour content

        standard_name :

        atmosphere_water_vapor_content

        units :

        kg.m-2

        Array Chunk Bytes 1.20 MiB 308.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

      • total_ozone
        (rows, columns)
        float32
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'total columnar ozone content', 'short_name': 'total_ozone', 'standard_name': 'atmosphere_mass_content_of_ozone', 'units': 'kg.m-2', 'valid_max': 100.0, 'valid_min': 0.0}

        dtype :

        <f4

        long_name :

        total columnar ozone content

        standard_name :

        atmosphere_mass_content_of_ozone

        units :

        kg.m-2

        Array Chunk Bytes 1.20 MiB 308.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

    • Attributes: (0)

    <xarray.DatasetView> Size: 21MB
    Dimensions:            (rows: 4091, removed_pixels: 150)
    Coordinates:
        altitude           (rows, removed_pixels) int16 1MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        latitude           (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        longitude          (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
      * removed_pixels     (removed_pixels) int64 1kB 0 1 2 3 4 ... 146 147 148 149
    Dimensions without coordinates: rows
    Data variables:
        detector_index     (rows, removed_pixels) float32 2MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        frame_offset       (rows, removed_pixels) float32 2MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        nb_removed_pixels  (rows) uint16 8kB dask.array<chunksize=(1024,), meta=np.ndarray>
        sza                (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>

    orphans

    • Groups: (0)
    • Dimensions:
      • rows: 4091
      • removed_pixels: 150
    • Coordinates: (4)
      • altitude
        (rows, removed_pixels)
        int16
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        dimensions :

        ['rows', 'removed_pixels']

        dtype :

        <i2

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f4

        fill_value :

        -32768

        long_name :

        altitude above reference ellipsoid for removed pixels

        standard_name :

        altitude

        units :

        m

        valid_max :

        9000

        valid_min :

        -1000

        Array Chunk Bytes 1.17 MiB 300.00 kiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type int16 numpy.ndarray

      • latitude
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        dimensions :

        ['rows', 'removed_pixels']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        DEM corrected latitude for removed pixels

        standard_name :

        latitude

        units :

        degrees_north

        valid_max :

        90000000

        valid_min :

        -90000000

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • longitude
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        dimensions :

        ['rows', 'removed_pixels']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        DEM corrected longitude for removed pixels

        standard_name :

        longitude

        units :

        degrees_east

        valid_max :

        180000000

        valid_min :

        -180000000

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • removed_pixels
        (removed_pixels)
        int64
        0 1 2 3 4 5 ... 145 146 147 148 149

        dimensions :

        ['removed_pixels']

        dtype :

        <i8

        long_name :

        maximum removed pixels per line

        array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
                14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
                28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
                42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
                56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
                70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
                84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
                98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
               112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
               126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
               140, 141, 142, 143, 144, 145, 146, 147, 148, 149])

    • Data variables: (4)
      • detector_index
        (rows, removed_pixels)
        float32
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<i2', 'eopf_is_masked': True, 'fill_value': -1, 'long_name': 'detector index for removed pixels', 'short_name': 'detector_index_orphans', 'valid_max': 3699, 'valid_min': 0}

        dtype :

        <i2

        long_name :

        detector index for removed pixels

        Array Chunk Bytes 2.34 MiB 600.00 kiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

      • frame_offset
        (rows, removed_pixels)
        float32
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<i1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -128, 'long_name': 'resampling along-track frame offset', 'short_name': 'frame_offset_orphans', 'valid_max': 15, 'valid_min': -15}

        dtype :

        <i1

        long_name :

        resampling along-track frame offset

        Array Chunk Bytes 2.34 MiB 600.00 kiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

      • nb_removed_pixels
        (rows)
        uint16
        dask.array<chunksize=(1024,), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows'], 'dtype': '<u2', 'long_name': 'number of removed pixels per frame', 'short_name': 'nb_removed_pixels'}

        dtype :

        <u2

        long_name :

        number of removed pixels per frame

        Array Chunk Bytes 7.99 kiB 2.00 kiB Shape (4091,) (1024,) Dask graph 4 chunks in 2 graph layers Data type uint16 numpy.ndarray

      • sza
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u4', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 4294967295, 'long_name': 'sun zenith angle for removed pixels', 'scale_factor': 1e-06, 'short_name': 'sza_orphans', 'units': 'degrees', 'valid_max': 180000000, 'valid_min': 0}

        dtype :

        <u4

        long_name :

        sun zenith angle for removed pixels

        units :

        degrees

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

    • Attributes: (0)

  • Dimensions:
  • Coordinates: (0)
  • Data variables: (0)
  • Attributes: (0)

  <xarray.DatasetView> Size: 4GB
  Dimensions:        (rows: 4091, columns: 4865)
  Coordinates:
      altitude       (rows, columns) int16 40MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      latitude       (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      longitude      (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      time_stamp     (rows) datetime64[ns] 33kB dask.array<chunksize=(1024,), meta=np.ndarray>
  Dimensions without coordinates: rows, columns
  Data variables: (12/21)
      oa01_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa02_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa03_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa04_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa05_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa06_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      ...             ...
      oa16_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa17_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa18_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa19_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa20_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa21_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>

  measurements

  • Groups: (1)
    <xarray.DatasetView> Size: 114MB
    Dimensions:         (rows: 4091, columns: 4865, removed_pixels: 150)
    Coordinates:
        altitude        (rows, removed_pixels) int16 1MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        latitude        (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        longitude       (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
      * removed_pixels  (removed_pixels) int64 1kB 0 1 2 3 4 ... 145 146 147 148 149
    Dimensions without coordinates: rows, columns
    Data variables: (12/21)
        oa01_radiance   (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa02_radiance   (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa03_radiance   (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa04_radiance   (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa05_radiance   (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa06_radiance   (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        ...              ...
        oa16_radiance   (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa17_radiance   (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa18_radiance   (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa19_radiance   (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa20_radiance   (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa21_radiance   (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>

    orphans

    • Groups: (0)
    • Dimensions:
      • rows: 4091
      • columns: 4865
      • removed_pixels: 150
    • Coordinates: (4)
      • altitude
        (rows, removed_pixels)
        int16
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        dimensions :

        ['rows', 'removed_pixels']

        dtype :

        <i2

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f4

        fill_value :

        -32768

        long_name :

        altitude above reference ellipsoid for removed pixels

        standard_name :

        altitude

        units :

        m

        valid_max :

        9000

        valid_min :

        -1000

        Array Chunk Bytes 1.17 MiB 300.00 kiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type int16 numpy.ndarray

      • latitude
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        dimensions :

        ['rows', 'removed_pixels']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        DEM corrected latitude for removed pixels

        standard_name :

        latitude

        units :

        degrees_north

        valid_max :

        90000000

        valid_min :

        -90000000

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • longitude
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        dimensions :

        ['rows', 'removed_pixels']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        DEM corrected longitude for removed pixels

        standard_name :

        longitude

        units :

        degrees_east

        valid_max :

        180000000

        valid_min :

        -180000000

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • removed_pixels
        (removed_pixels)
        int64
        0 1 2 3 4 5 ... 145 146 147 148 149

        dimensions :

        ['removed_pixels']

        dtype :

        <i8

        long_name :

        maximum removed pixels per line

        array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
                14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
                28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
                42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
                56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
                70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
                84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
                98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
               112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
               126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
               140, 141, 142, 143, 144, 145, 146, 147, 148, 149])

    • Data variables: (21)
      • oa01_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 01', 'scale_factor': 0.013634907081723213, 'short_name': 'oa01_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 01

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa02_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 02', 'scale_factor': 0.011490973643958569, 'short_name': 'oa02_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 02

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa03_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 03', 'scale_factor': 0.012588496319949627, 'short_name': 'oa03_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 03

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa04_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 04', 'scale_factor': 0.011396283283829689, 'short_name': 'oa04_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 04

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa05_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 05', 'scale_factor': 0.012233924120664597, 'short_name': 'oa05_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 05

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa06_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 06', 'scale_factor': 0.010038239881396294, 'short_name': 'oa06_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 06

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa07_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 07', 'scale_factor': 0.01056649163365364, 'short_name': 'oa07_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 07

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa08_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 08', 'scale_factor': 0.008974685333669186, 'short_name': 'oa08_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 08

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa09_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 09', 'scale_factor': 0.009690877981483936, 'short_name': 'oa09_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 09

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa10_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 10', 'scale_factor': 0.008986380882561207, 'short_name': 'oa10_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 10

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa11_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 11', 'scale_factor': 0.008361034095287323, 'short_name': 'oa11_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 11

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa12_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 12', 'scale_factor': 0.007491027936339378, 'short_name': 'oa12_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 12

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa13_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 13', 'scale_factor': 0.007861301302909851, 'short_name': 'oa13_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 13

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa14_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 14', 'scale_factor': 0.007529641035944223, 'short_name': 'oa14_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 14

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa15_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 15', 'scale_factor': 0.00788309145718813, 'short_name': 'oa15_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 15

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa16_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 16', 'scale_factor': 0.007152478210628033, 'short_name': 'oa16_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 16

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa17_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 17', 'scale_factor': 0.006055756937712431, 'short_name': 'oa17_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 17

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa18_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 18', 'scale_factor': 0.0060341269709169865, 'short_name': 'oa18_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 18

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa19_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 19', 'scale_factor': 0.0055409627966582775, 'short_name': 'oa19_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 19

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa20_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 20', 'scale_factor': 0.005095385946333408, 'short_name': 'oa20_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 20

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa21_radiance
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'radiance in removed pixels for OLCI acquisition band 21', 'scale_factor': 0.004291551187634468, 'short_name': 'oa21_radiance_orphans', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        radiance in removed pixels for OLCI acquisition band 21

        standard_name :

        toa_upwelling_spectral_radiance

        units :

        mW.m-2.sr-1.nm-1

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

    • Attributes: (0)

  • Dimensions:
    • rows: 4091
    • columns: 4865
  • Coordinates: (4)
    • altitude
      (rows, columns)
      int16
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i2

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f4

      fill_value :

      -32768

      long_name :

      DEM corrected altitude

      standard_name :

      altitude

      units :

      m

      valid_max :

      9000

      valid_min :

      -1000

      Array Chunk Bytes 37.96 MiB 2.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type int16 numpy.ndarray

    • latitude
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i4

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f8

      fill_value :

      -2147483648

      long_name :

      DEM corrected latitude

      standard_name :

      latitude

      units :

      degrees_north

      valid_max :

      90000000

      valid_min :

      -90000000

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • longitude
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i4

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f8

      fill_value :

      -2147483648

      long_name :

      DEM corrected longitude

      standard_name :

      longitude

      units :

      degrees_east

      valid_max :

      180000000

      valid_min :

      -180000000

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • time_stamp
      (rows)
      datetime64[ns]
      dask.array<chunksize=(1024,), meta=np.ndarray>

      dimensions :

      ['rows']

      dtype :

      <M8[ns]

      fill_value :

      -1

      long_name :

      elapsed time since 01 Jan 2000 00:00:00 as time stamp for each row

      standard_name :

      time

      Array Chunk Bytes 31.96 kiB 8.00 kiB Shape (4091,) (1024,) Dask graph 4 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • Data variables: (21)
    • oa01_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 01', 'scale_factor': 0.013634907081723213, 'short_name': 'oa01_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 01

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa02_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 02', 'scale_factor': 0.011490973643958569, 'short_name': 'oa02_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 02

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa03_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 03', 'scale_factor': 0.012588496319949627, 'short_name': 'oa03_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 03

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa04_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 04', 'scale_factor': 0.011396283283829689, 'short_name': 'oa04_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 04

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa05_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 05', 'scale_factor': 0.012233924120664597, 'short_name': 'oa05_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 05

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa06_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 06', 'scale_factor': 0.010038239881396294, 'short_name': 'oa06_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 06

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa07_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 07', 'scale_factor': 0.01056649163365364, 'short_name': 'oa07_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 07

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa08_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 08', 'scale_factor': 0.008974685333669186, 'short_name': 'oa08_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 08

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa09_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 09', 'scale_factor': 0.009690877981483936, 'short_name': 'oa09_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 09

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa10_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 10', 'scale_factor': 0.008986380882561207, 'short_name': 'oa10_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 10

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa11_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 11', 'scale_factor': 0.008361034095287323, 'short_name': 'oa11_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 11

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa12_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 12', 'scale_factor': 0.007491027936339378, 'short_name': 'oa12_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 12

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa13_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 13', 'scale_factor': 0.007861301302909851, 'short_name': 'oa13_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 13

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa14_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 14', 'scale_factor': 0.007529641035944223, 'short_name': 'oa14_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 14

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa15_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 15', 'scale_factor': 0.00788309145718813, 'short_name': 'oa15_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 15

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa16_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 16', 'scale_factor': 0.007152478210628033, 'short_name': 'oa16_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 16

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa17_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 17', 'scale_factor': 0.006055756937712431, 'short_name': 'oa17_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 17

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa18_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 18', 'scale_factor': 0.0060341269709169865, 'short_name': 'oa18_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 18

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa19_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 19', 'scale_factor': 0.0055409627966582775, 'short_name': 'oa19_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 19

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa20_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 20', 'scale_factor': 0.005095385946333408, 'short_name': 'oa20_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 20

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa21_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 21', 'scale_factor': 0.004291551187634468, 'short_name': 'oa21_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 21

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • Attributes: (0)

  <xarray.DatasetView> Size: 4GB
  Dimensions:            (rows: 4091, columns: 4865)
  Coordinates:
      altitude           (rows, columns) int16 40MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      latitude           (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      longitude          (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      time_stamp         (rows) datetime64[ns] 33kB dask.array<chunksize=(1024,), meta=np.ndarray>
  Dimensions without coordinates: rows, columns
  Data variables: (12/22)
      oa01_radiance_unc  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa02_radiance_unc  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa03_radiance_unc  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa04_radiance_unc  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa05_radiance_unc  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa06_radiance_unc  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      ...                 ...
      oa17_radiance_unc  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa18_radiance_unc  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa19_radiance_unc  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa20_radiance_unc  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa21_radiance_unc  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      quality_flags      (rows, columns) uint32 80MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>

  quality

  • Groups: (1)
    <xarray.DatasetView> Size: 117MB
    Dimensions:            (rows: 4091, columns: 4865, removed_pixels: 150,
                            dim_0: 4091, dim_1: 150)
    Coordinates:
        altitude           (rows, removed_pixels) int16 1MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        latitude           (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        longitude          (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
      * removed_pixels     (removed_pixels) int64 1kB 0 1 2 3 4 ... 146 147 148 149
    Dimensions without coordinates: rows, columns, dim_0, dim_1
    Data variables: (12/22)
        oa01_radiance_unc  (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa02_radiance_unc  (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa03_radiance_unc  (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa04_radiance_unc  (dim_0, dim_1) float64 5MB dask.array<chunksize=(4091, 150), meta=np.ndarray>
        oa05_radiance_unc  (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa06_radiance_unc  (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        ...                 ...
        oa17_radiance_unc  (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa18_radiance_unc  (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa19_radiance_unc  (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa20_radiance_unc  (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        oa21_radiance_unc  (rows, removed_pixels) float64 5MB dask.array<chunksize=(1024, 150), meta=np.ndarray>
        quality_flags      (rows, removed_pixels) uint32 2MB dask.array<chunksize=(1024, 150), meta=np.ndarray>

    orphans

    • Groups: (0)
    • Dimensions:
      • rows: 4091
      • columns: 4865
      • removed_pixels: 150
      • dim_0: 4091
      • dim_1: 150
    • Coordinates: (4)
      • altitude
        (rows, removed_pixels)
        int16
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        dimensions :

        ['rows', 'removed_pixels']

        dtype :

        <i2

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f4

        fill_value :

        -32768

        long_name :

        altitude above reference ellipsoid for removed pixels

        standard_name :

        altitude

        units :

        m

        valid_max :

        9000

        valid_min :

        -1000

        Array Chunk Bytes 1.17 MiB 300.00 kiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type int16 numpy.ndarray

      • latitude
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        dimensions :

        ['rows', 'removed_pixels']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        DEM corrected latitude for removed pixels

        standard_name :

        latitude

        units :

        degrees_north

        valid_max :

        90000000

        valid_min :

        -90000000

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • longitude
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        dimensions :

        ['rows', 'removed_pixels']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        DEM corrected longitude for removed pixels

        standard_name :

        longitude

        units :

        degrees_east

        valid_max :

        180000000

        valid_min :

        -180000000

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • removed_pixels
        (removed_pixels)
        int64
        0 1 2 3 4 5 ... 145 146 147 148 149

        dimensions :

        ['removed_pixels']

        dtype :

        <i8

        long_name :

        maximum removed pixels per line

        array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
                14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
                28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
                42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
                56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
                70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
                84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
                98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
               112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
               126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
               140, 141, 142, 143, 144, 145, 146, 147, 148, 149])

    • Data variables: (22)
      • oa01_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.19358299672603607, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 01', 'scale_factor': 0.0054720002226531506, 'short_name': 'oa01_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 01

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa02_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.21523700654506683, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 02', 'scale_factor': 0.005102999974042177, 'short_name': 'oa02_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 02

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa03_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.2714740037918091, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 03', 'scale_factor': 0.005386999808251858, 'short_name': 'oa03_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 03

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa04_radiance_unc
        (dim_0, dim_1)
        float64
        dask.array<chunksize=(4091, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.30964401364326477, 'dimensions': ['dim_0', 'dim_1'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 04', 'scale_factor': 0.005243999883532524, 'short_name': 'oa04_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 04

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 4.68 MiB Shape (4091, 150) (4091, 150) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa05_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.3706839978694916, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 05', 'scale_factor': 0.005743000190705061, 'short_name': 'oa05_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 05

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa06_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.4443579912185669, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 06', 'scale_factor': 0.006068000104278326, 'short_name': 'oa06_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 06

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa07_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.5721830129623413, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 07', 'scale_factor': 0.00686699990183115, 'short_name': 'oa07_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 07

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa08_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.6707040071487427, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 08', 'scale_factor': 0.007137000095099211, 'short_name': 'oa08_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 08

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa09_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.6211689710617065, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 09', 'scale_factor': 0.006550000049173832, 'short_name': 'oa09_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 09

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa10_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.6792640089988708, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 10', 'scale_factor': 0.006779999937862158, 'short_name': 'oa10_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 10

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa11_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.7377849817276001, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 11', 'scale_factor': 0.007145000156015158, 'short_name': 'oa11_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 11

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa12_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.7818710207939148, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 12', 'scale_factor': 0.006775000132620335, 'short_name': 'oa12_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 12

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa13_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.7220579981803894, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 13', 'scale_factor': 0.005898999981582165, 'short_name': 'oa13_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 13

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa14_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.6917920112609863, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 14', 'scale_factor': 0.006147999782115221, 'short_name': 'oa14_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 14

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa15_radiance_unc
        (dim_0, dim_1)
        float64
        dask.array<chunksize=(4091, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.7225130200386047, 'dimensions': ['dim_0', 'dim_1'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 15', 'scale_factor': 0.006641000043600798, 'short_name': 'oa15_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 15

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 4.68 MiB Shape (4091, 150) (4091, 150) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa16_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.9848459959030151, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 16', 'scale_factor': 0.008098999969661236, 'short_name': 'oa16_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 16

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa17_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.9873489737510681, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 17', 'scale_factor': 0.008390000090003014, 'short_name': 'oa17_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 17

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa18_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.7908260226249695, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 18', 'scale_factor': 0.007825000211596489, 'short_name': 'oa18_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 18

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa19_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.8344600200653076, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 19', 'scale_factor': 0.007402999792248011, 'short_name': 'oa19_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 19

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa20_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.9243649840354919, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 20', 'scale_factor': 0.008166000247001648, 'short_name': 'oa20_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 20

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oa21_radiance_unc
        (rows, removed_pixels)
        float64
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': -0.5929380059242249, 'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 21', 'scale_factor': 0.0057830000296235085, 'short_name': 'oa21_radiance_unc_orphans', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

        dtype :

        <u1

        long_name :

        log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 21

        units :

        lg(re mW.m-2.sr-1.nm-1)

        Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • quality_flags
        (rows, removed_pixels)
        uint32
        dask.array<chunksize=(1024, 150), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u4', 'eopf_is_masked': True, 'flag_masks': [2147483648, 1073741824, 536870912, 268435456, 134217728, 67108864, 33554432, 16777216, 8388608, 4194304, 2097152, 1048576, 524288], 'flag_meanings': 'land coastline fresh_inland_water tidal_region bright straylight_risk invalid cosmetic duplicated sun-glint_risk dubious saturated partially_saturated', 'long_name': 'classification and quality flags for removed pixels', 'short_name': 'quality_flags_orphans'}

        dtype :

        <u4

        flag_masks :

        [2147483648, 1073741824, 536870912, 268435456, 134217728, 67108864, 33554432, 16777216, 8388608, 4194304, 2097152, 1048576, 524288]

        flag_meanings :

        land coastline fresh_inland_water tidal_region bright straylight_risk invalid cosmetic duplicated sun-glint_risk dubious saturated partially_saturated

        long_name :

        classification and quality flags for removed pixels

        Array Chunk Bytes 2.34 MiB 600.00 kiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type uint32 numpy.ndarray

    • Attributes: (0)

  • Dimensions:
    • rows: 4091
    • columns: 4865
  • Coordinates: (4)
    • altitude
      (rows, columns)
      int16
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i2

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f4

      fill_value :

      -32768

      long_name :

      DEM corrected altitude

      standard_name :

      altitude

      units :

      m

      valid_max :

      9000

      valid_min :

      -1000

      Array Chunk Bytes 37.96 MiB 2.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type int16 numpy.ndarray

    • latitude
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i4

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f8

      fill_value :

      -2147483648

      long_name :

      DEM corrected latitude

      standard_name :

      latitude

      units :

      degrees_north

      valid_max :

      90000000

      valid_min :

      -90000000

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • longitude
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i4

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f8

      fill_value :

      -2147483648

      long_name :

      DEM corrected longitude

      standard_name :

      longitude

      units :

      degrees_east

      valid_max :

      180000000

      valid_min :

      -180000000

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • time_stamp
      (rows)
      datetime64[ns]
      dask.array<chunksize=(1024,), meta=np.ndarray>

      dimensions :

      ['rows']

      dtype :

      <M8[ns]

      fill_value :

      -1

      long_name :

      elapsed time since 01 Jan 2000 00:00:00 as time stamp for each row

      standard_name :

      time

      Array Chunk Bytes 31.96 kiB 8.00 kiB Shape (4091,) (1024,) Dask graph 4 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • Data variables: (22)
    • oa01_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.19358299672603607, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 01', 'scale_factor': 0.0054720002226531506, 'short_name': 'oa01_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 01

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa02_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.21523700654506683, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 02', 'scale_factor': 0.005102999974042177, 'short_name': 'oa02_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 02

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa03_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.2714740037918091, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 03', 'scale_factor': 0.005386999808251858, 'short_name': 'oa03_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 03

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa04_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.30964401364326477, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 04', 'scale_factor': 0.005243999883532524, 'short_name': 'oa04_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 04

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa05_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.3706839978694916, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 05', 'scale_factor': 0.005743000190705061, 'short_name': 'oa05_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 05

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa06_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.4443579912185669, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 06', 'scale_factor': 0.006068000104278326, 'short_name': 'oa06_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 06

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa07_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.5721830129623413, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 07', 'scale_factor': 0.00686699990183115, 'short_name': 'oa07_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 07

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa08_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.6707040071487427, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 08', 'scale_factor': 0.007137000095099211, 'short_name': 'oa08_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 08

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa09_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.6211689710617065, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 09', 'scale_factor': 0.006550000049173832, 'short_name': 'oa09_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 09

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa10_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.6792640089988708, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 10', 'scale_factor': 0.006779999937862158, 'short_name': 'oa10_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 10

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa11_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.7377849817276001, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 11', 'scale_factor': 0.007145000156015158, 'short_name': 'oa11_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 11

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa12_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.7818710207939148, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 12', 'scale_factor': 0.006775000132620335, 'short_name': 'oa12_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 12

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa13_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.7220579981803894, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 13', 'scale_factor': 0.005898999981582165, 'short_name': 'oa13_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 13

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa14_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.6917920112609863, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 14', 'scale_factor': 0.006147999782115221, 'short_name': 'oa14_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 14

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa15_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.7225130200386047, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 15', 'scale_factor': 0.006641000043600798, 'short_name': 'oa15_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 15

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa16_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.9848459959030151, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 16', 'scale_factor': 0.008098999969661236, 'short_name': 'oa16_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 16

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa17_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.9873489737510681, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 17', 'scale_factor': 0.008390000090003014, 'short_name': 'oa17_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 17

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa18_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.7908260226249695, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 18', 'scale_factor': 0.007825000211596489, 'short_name': 'oa18_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 18

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa19_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.8344600200653076, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 19', 'scale_factor': 0.007402999792248011, 'short_name': 'oa19_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 19

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa20_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.9243649840354919, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 20', 'scale_factor': 0.008166000247001648, 'short_name': 'oa20_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 20

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa21_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.5929380059242249, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 21', 'scale_factor': 0.0057830000296235085, 'short_name': 'oa21_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 21

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

    • quality_flags
      (rows, columns)
      uint32
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u4', 'eopf_is_masked': True, 'flag_masks': [2147483648, 1073741824, 536870912, 268435456, 134217728, 67108864, 33554432, 16777216, 8388608, 4194304, 2097152, 1048576, 524288], 'flag_meanings': 'land coastline fresh_inland_water tidal_region bright straylight_risk invalid cosmetic duplicated sun-glint_risk dubious saturated partially_saturated', 'long_name': 'classification and quality flags', 'short_name': 'quality_flags'}

      dtype :

      <u4

      flag_masks :

      [2147483648, 1073741824, 536870912, 268435456, 134217728, 67108864, 33554432, 16777216, 8388608, 4194304, 2097152, 1048576, 524288]

      flag_meanings :

      land coastline fresh_inland_water tidal_region bright straylight_risk invalid cosmetic duplicated sun-glint_risk dubious saturated partially_saturated

      long_name :

      classification and quality flags

      Array Chunk Bytes 75.92 MiB 4.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type uint32 numpy.ndarray

  • Attributes: (0)

• Dimensions:
• Coordinates: (0)
• Inherited coordinates: (0)
• Data variables: (0)
• Attributes: (3)

  other_metadata :

  {'absolute_pass_number': 82094, 'cycle_number': 117, 'data_information': {'accuracy_reference': '0', 'earth_sun_distance': 148692226821, 'measurement_accuracy': '2.000000', 'ocl_status': 'true', 'sampling': {'across_track_distance': 270, 'along_track_distance': 294, 'along_track_time': 44002, 'columns_per_tiepoint': 64, 'rows_per_tiepoint': 1}}, 'eopf_category': 'eoproduct', 'ephemeris': {'start': {'TAI': '2026-03-13T09:25:25.256763', 'UT1': '2026-03-13T09:24:48.319326', 'UTC': '2026-03-13T09:24:48.256763Z', 'position': {'x': -7094686.732, 'y': -1129221.277, 'z': 0.001}, 'velocity': {'x': -249.56373, 'y': 1621.140793, 'z': 7366.211176}}, 'stop': {'TAI': '2026-03-13T11:06:24.413833', 'UT1': '2026-03-13T11:05:47.476392', 'UTC': '2026-03-13T11:05:47.413833Z', 'position': {'x': -6898641.864, 'y': 2004695.114, 'z': 0.003}, 'velocity': {'x': 465.705986, 'y': 1572.789106, 'z': 7366.200009}}}, 'meteo': {'missing_time': 0, 'source': 'ECMWF', 'type': 'ANALYSIS'}, 'phase_identifier': 4, 'product_unit': {'alongtrack_coordinate': 2160, 'duration': 180, 'type': 'FRAME'}, 'relative_pass_number': 700}

  processing_history :

  {'Level-0 Product': [{'adfs': ['S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_TM_0_NAT__G_20260310T192718_20260310T210759_20260310T211703_6041______________SVL_O_NR_OPE.ISIP', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_TM_0_NAT____20260310T192700_20260310T210741_20260310T211958_6041_117_313______ESA_O_AL_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-10T21:20:06.608260Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_TM_0_NAT__G_20260310T192718_20260310T210759_20260310T211703_6041______________SVL_O_NR_OPE.ISIP', 'S3IPF PDS 001 - 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i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T101239_20260313T101439_20260313T114426_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:44:33.920556Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T101457_20260313T101657_20260313T113938_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T101439_20260313T101639_20260313T114455_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:45:03.371903Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T101657_20260313T101857_20260313T113944_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T101639_20260313T101839_20260313T114502_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:45:09.982449Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T101857_20260313T102057_20260313T113945_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T101839_20260313T102039_20260313T114521_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:45:29.324817Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T102257_20260313T102457_20260313T113947_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - 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  stac_discovery :

  {'assets': {'atmospheric_temperature_profile': {'href': '/conditions/meteorology/atmospheric_temperature_profile', 'title': 'atmospheric_temperature_profile'}, 'detector_index': {'href': '/conditions/image/detector_index', 'title': 'detector_index'}, 'detector_index_orphans': {'href': '/conditions/orphans/detector_index', 'title': 'detector_index_orphans'}, 'frame_offset': {'href': '/conditions/image/frame_offset', 'title': 'frame_offset'}, 'frame_offset_orphans': {'href': '/conditions/orphans/frame_offset', 'title': 'frame_offset_orphans'}, 'fwhm': {'href': '/conditions/instrument/fwhm', 'title': 'fwhm'}, 'horizontal_wind': {'href': '/conditions/meteorology/horizontal_wind', 'title': 'horizontal_wind'}, 'humidity': {'href': '/conditions/meteorology/humidity', 'title': 'humidity'}, 'lambda0': {'href': '/conditions/instrument/lambda0', 'title': 'lambda0'}, 'nb_removed_pixels': {'href': '/conditions/orphans/nb_removed_pixels', 'title': 'nb_removed_pixels'}, 'oa01_radiance': {'href': '/measurements/oa01_radiance', 'title': 'oa01_radiance'}, 'oa01_radiance_orphans': {'href': '/measurements/orphans/oa01_radiance', 'title': 'oa01_radiance_orphans'}, 'oa01_radiance_unc': {'href': '/quality/oa01_radiance_unc', 'title': 'oa01_radiance_unc'}, 'oa01_radiance_unc_orphans': {'href': '/quality/orphans/oa01_radiance_unc', 'title': 'oa01_radiance_unc_orphans'}, 'oa02_radiance': {'href': '/measurements/oa02_radiance', 'title': 'oa02_radiance'}, 'oa02_radiance_orphans': {'href': '/measurements/orphans/oa02_radiance', 'title': 'oa02_radiance_orphans'}, 'oa02_radiance_unc': {'href': '/quality/oa02_radiance_unc', 'title': 'oa02_radiance_unc'}, 'oa02_radiance_unc_orphans': {'href': '/quality/orphans/oa02_radiance_unc', 'title': 'oa02_radiance_unc_orphans'}, 'oa03_radiance': {'href': '/measurements/oa03_radiance', 'title': 'oa03_radiance'}, 'oa03_radiance_orphans': {'href': '/measurements/orphans/oa03_radiance', 'title': 'oa03_radiance_orphans'}, 'oa03_radiance_unc': {'href': '/quality/oa03_radiance_unc', 'title': 'oa03_radiance_unc'}, 'oa03_radiance_unc_orphans': {'href': '/quality/orphans/oa03_radiance_unc', 'title': 'oa03_radiance_unc_orphans'}, 'oa04_radiance': {'href': '/measurements/oa04_radiance', 'title': 'oa04_radiance'}, 'oa04_radiance_orphans': {'href': '/measurements/orphans/oa04_radiance', 'title': 'oa04_radiance_orphans'}, 'oa04_radiance_unc': {'href': '/quality/oa04_radiance_unc', 'title': 'oa04_radiance_unc'}, 'oa04_radiance_unc_orphans': {'href': '/quality/orphans/oa04_radiance_unc', 'title': 'oa04_radiance_unc_orphans'}, 'oa05_radiance': {'href': '/measurements/oa05_radiance', 'title': 'oa05_radiance'}, 'oa05_radiance_orphans': {'href': '/measurements/orphans/oa05_radiance', 'title': 'oa05_radiance_orphans'}, 'oa05_radiance_unc': {'href': '/quality/oa05_radiance_unc', 'title': 'oa05_radiance_unc'}, 'oa05_radiance_unc_orphans': {'href': '/quality/orphans/oa05_radiance_unc', 'title': 'oa05_radiance_unc_orphans'}, 'oa06_radiance': {'href': '/measurements/oa06_radiance', 'title': 'oa06_radiance'}, 'oa06_radiance_orphans': {'href': '/measurements/orphans/oa06_radiance', 'title': 'oa06_radiance_orphans'}, 'oa06_radiance_unc': {'href': '/quality/oa06_radiance_unc', 'title': 'oa06_radiance_unc'}, 'oa06_radiance_unc_orphans': {'href': '/quality/orphans/oa06_radiance_unc', 'title': 'oa06_radiance_unc_orphans'}, 'oa07_radiance': {'href': '/measurements/oa07_radiance', 'title': 'oa07_radiance'}, 'oa07_radiance_orphans': {'href': '/measurements/orphans/oa07_radiance', 'title': 'oa07_radiance_orphans'}, 'oa07_radiance_unc': {'href': '/quality/oa07_radiance_unc', 'title': 'oa07_radiance_unc'}, 'oa07_radiance_unc_orphans': {'href': '/quality/orphans/oa07_radiance_unc', 'title': 'oa07_radiance_unc_orphans'}, 'oa08_radiance': {'href': '/measurements/oa08_radiance', 'title': 'oa08_radiance'}, 'oa08_radiance_orphans': {'href': '/measurements/orphans/oa08_radiance', 'title': 'oa08_radiance_orphans'}, 'oa08_radiance_unc': {'href': '/quality/oa08_radiance_unc', 'title': 'oa08_radiance_unc'}, 'oa08_radiance_unc_orphans': {'href': '/quality/orphans/oa08_radiance_unc', 'title': 'oa08_radiance_unc_orphans'}, 'oa09_radiance': {'href': '/measurements/oa09_radiance', 'title': 'oa09_radiance'}, 'oa09_radiance_orphans': {'href': '/measurements/orphans/oa09_radiance', 'title': 'oa09_radiance_orphans'}, 'oa09_radiance_unc': {'href': '/quality/oa09_radiance_unc', 'title': 'oa09_radiance_unc'}, 'oa09_radiance_unc_orphans': {'href': '/quality/orphans/oa09_radiance_unc', 'title': 'oa09_radiance_unc_orphans'}, 'oa10_radiance': {'href': '/measurements/oa10_radiance', 'title': 'oa10_radiance'}, 'oa10_radiance_orphans': {'href': '/measurements/orphans/oa10_radiance', 'title': 'oa10_radiance_orphans'}, 'oa10_radiance_unc': {'href': '/quality/oa10_radiance_unc', 'title': 'oa10_radiance_unc'}, 'oa10_radiance_unc_orphans': {'href': '/quality/orphans/oa10_radiance_unc', 'title': 'oa10_radiance_unc_orphans'}, 'oa11_radiance': {'href': '/measurements/oa11_radiance', 'title': 'oa11_radiance'}, 'oa11_radiance_orphans': {'href': '/measurements/orphans/oa11_radiance', 'title': 'oa11_radiance_orphans'}, 'oa11_radiance_unc': {'href': '/quality/oa11_radiance_unc', 'title': 'oa11_radiance_unc'}, 'oa11_radiance_unc_orphans': {'href': '/quality/orphans/oa11_radiance_unc', 'title': 'oa11_radiance_unc_orphans'}, 'oa12_radiance': {'href': '/measurements/oa12_radiance', 'title': 'oa12_radiance'}, 'oa12_radiance_orphans': {'href': '/measurements/orphans/oa12_radiance', 'title': 'oa12_radiance_orphans'}, 'oa12_radiance_unc': {'href': '/quality/oa12_radiance_unc', 'title': 'oa12_radiance_unc'}, 'oa12_radiance_unc_orphans': {'href': '/quality/orphans/oa12_radiance_unc', 'title': 'oa12_radiance_unc_orphans'}, 'oa13_radiance': {'href': '/measurements/oa13_radiance', 'title': 'oa13_radiance'}, 'oa13_radiance_orphans': {'href': '/measurements/orphans/oa13_radiance', 'title': 'oa13_radiance_orphans'}, 'oa13_radiance_unc': {'href': '/quality/oa13_radiance_unc', 'title': 'oa13_radiance_unc'}, 'oa13_radiance_unc_orphans': {'href': '/quality/orphans/oa13_radiance_unc', 'title': 'oa13_radiance_unc_orphans'}, 'oa14_radiance': {'href': '/measurements/oa14_radiance', 'title': 'oa14_radiance'}, 'oa14_radiance_orphans': {'href': '/measurements/orphans/oa14_radiance', 'title': 'oa14_radiance_orphans'}, 'oa14_radiance_unc': {'href': '/quality/oa14_radiance_unc', 'title': 'oa14_radiance_unc'}, 'oa14_radiance_unc_orphans': {'href': '/quality/orphans/oa14_radiance_unc', 'title': 'oa14_radiance_unc_orphans'}, 'oa15_radiance': {'href': '/measurements/oa15_radiance', 'title': 'oa15_radiance'}, 'oa15_radiance_orphans': {'href': '/measurements/orphans/oa15_radiance', 'title': 'oa15_radiance_orphans'}, 'oa15_radiance_unc': {'href': '/quality/oa15_radiance_unc', 'title': 'oa15_radiance_unc'}, 'oa15_radiance_unc_orphans': {'href': '/quality/orphans/oa15_radiance_unc', 'title': 'oa15_radiance_unc_orphans'}, 'oa16_radiance': {'href': '/measurements/oa16_radiance', 'title': 'oa16_radiance'}, 'oa16_radiance_orphans': {'href': '/measurements/orphans/oa16_radiance', 'title': 'oa16_radiance_orphans'}, 'oa16_radiance_unc': {'href': '/quality/oa16_radiance_unc', 'title': 'oa16_radiance_unc'}, 'oa16_radiance_unc_orphans': {'href': '/quality/orphans/oa16_radiance_unc', 'title': 'oa16_radiance_unc_orphans'}, 'oa17_radiance': {'href': '/measurements/oa17_radiance', 'title': 'oa17_radiance'}, 'oa17_radiance_orphans': {'href': '/measurements/orphans/oa17_radiance', 'title': 'oa17_radiance_orphans'}, 'oa17_radiance_unc': {'href': '/quality/oa17_radiance_unc', 'title': 'oa17_radiance_unc'}, 'oa17_radiance_unc_orphans': {'href': '/quality/orphans/oa17_radiance_unc', 'title': 'oa17_radiance_unc_orphans'}, 'oa18_radiance': {'href': '/measurements/oa18_radiance', 'title': 'oa18_radiance'}, 'oa18_radiance_orphans': {'href': '/measurements/orphans/oa18_radiance', 'title': 'oa18_radiance_orphans'}, 'oa18_radiance_unc': {'href': '/quality/oa18_radiance_unc', 'title': 'oa18_radiance_unc'}, 'oa18_radiance_unc_orphans': {'href': '/quality/orphans/oa18_radiance_unc', 'title': 'oa18_radiance_unc_orphans'}, 'oa19_radiance': {'href': '/measurements/oa19_radiance', 'title': 'oa19_radiance'}, 'oa19_radiance_orphans': {'href': '/measurements/orphans/oa19_radiance', 'title': 'oa19_radiance_orphans'}, 'oa19_radiance_unc': {'href': '/quality/oa19_radiance_unc', 'title': 'oa19_radiance_unc'}, 'oa19_radiance_unc_orphans': {'href': '/quality/orphans/oa19_radiance_unc', 'title': 'oa19_radiance_unc_orphans'}, 'oa20_radiance': {'href': '/measurements/oa20_radiance', 'title': 'oa20_radiance'}, 'oa20_radiance_orphans': {'href': '/measurements/orphans/oa20_radiance', 'title': 'oa20_radiance_orphans'}, 'oa20_radiance_unc': {'href': '/quality/oa20_radiance_unc', 'title': 'oa20_radiance_unc'}, 'oa20_radiance_unc_orphans': {'href': '/quality/orphans/oa20_radiance_unc', 'title': 'oa20_radiance_unc_orphans'}, 'oa21_radiance': {'href': '/measurements/oa21_radiance', 'title': 'oa21_radiance'}, 'oa21_radiance_orphans': {'href': '/measurements/orphans/oa21_radiance', 'title': 'oa21_radiance_orphans'}, 'oa21_radiance_unc': {'href': '/quality/oa21_radiance_unc', 'title': 'oa21_radiance_unc'}, 'oa21_radiance_unc_orphans': {'href': '/quality/orphans/oa21_radiance_unc', 'title': 'oa21_radiance_unc_orphans'}, 'oaa': {'href': '/conditions/geometry/oaa', 'title': 'oaa'}, 'oza': {'href': '/conditions/geometry/oza', 'title': 'oza'}, 'quality_flags': {'href': '/quality/quality_flags', 'title': 'quality_flags'}, 'quality_flags_orphans': {'href': '/quality/orphans/quality_flags', 'title': 'quality_flags_orphans'}, 'relative_spectral_covariance': {'href': '/conditions/instrument/relative_spectral_covariance', 'title': 'relative_spectral_covariance'}, 'saa': {'href': '/conditions/geometry/saa', 'title': 'saa'}, 'sea_level_pressure': {'href': '/conditions/meteorology/sea_level_pressure', 'title': 'sea_level_pressure'}, 'solar_flux': {'href': '/conditions/instrument/solar_flux', 'title': 'solar_flux'}, 'sza': {'href': '/conditions/geometry/sza', 'title': 'sza'}, 'sza_orphans': {'href': '/conditions/orphans/sza', 'title': 'sza_orphans'}, 'total_columnar_water_vapour': {'href': '/conditions/meteorology/total_columnar_water_vapour', 'title': 'total_columnar_water_vapour'}, 'total_ozone': {'href': '/conditions/meteorology/total_ozone', 'title': 'total_ozone'}}, 'bbox': [15.2194, 39.5543, -4.7861, 52.4622], 'collection': 'OL__L1_.004.01.00', 'geometry': {'coordinates': [[[-4.7861, 41.9789], [-3.93914, 41.902], [-3.11661, 41.8194], [-2.29536, 41.7309], [-1.47832, 41.6368], [-0.666506, 41.54], [0.148404, 41.4342], [0.963169, 41.3217], [1.76618, 41.2051], [2.5758, 41.0815], [3.38023, 40.9537], [4.18015, 40.8197], [4.97545, 40.6805], [5.77131, 40.5359], [6.56315, 40.3853], [7.35038, 40.2295], [8.13158, 40.0685], [8.9109, 39.9022], [9.688, 39.7302], [10.4609, 39.5543], [11.509, 42.152], [12.6418, 44.7451], [13.8723, 47.327], [15.2194, 49.8949], [14.3049, 50.0997], [13.3893, 50.2972], [12.4644, 50.4876], [11.5335, 50.6708], [10.5952, 50.8461], [9.64863, 51.0141], [8.69541, 51.1736], [7.73658, 51.326], [6.76786, 51.4711], [5.79814, 51.6067], [4.82094, 51.7355], [3.83576, 51.8565], [2.85121, 51.969], [1.85279, 52.0735], [0.863084, 52.1658], [-0.141309, 52.2533], [-1.14494, 52.332], [-2.14863, 52.4019], [-3.16831, 52.4622], [-3.56442, 49.8426], [-3.96588, 47.2211], [-4.37342, 44.5983], [-4.7861, 41.9789]]], 'type': 'Polygon'}, 'id': 'S03OLCEFR_20260313T100048_0180_B350_S633', 'links': [{'href': './.zattrs.json', 'rel': 'collection', 'type': 'application/json'}], 'properties': {'bands': [{'eo:center_wavelength': 0.4, 'eo:full_width_half_max': 0.015, 'name': 'oa01'}, {'eo:center_wavelength': 0.41250000000000003, 'eo:full_width_half_max': 0.01, 'name': 'oa02'}, {'eo:center_wavelength': 0.4425, 'eo:full_width_half_max': 0.01, 'name': 'oa03'}, {'eo:center_wavelength': 0.49, 'eo:common_name': 'blue', 'eo:full_width_half_max': 0.01, 'name': 'oa04'}, {'eo:center_wavelength': 0.51, 'eo:full_width_half_max': 0.01, 'name': 'oa05'}, {'eo:center_wavelength': 0.56, 'eo:common_name': 'green', 'eo:full_width_half_max': 0.01, 'name': 'oa06'}, {'eo:center_wavelength': 0.62, 'eo:common_name': 'yellow', 'eo:full_width_half_max': 0.01, 'name': 'oa07'}, {'eo:center_wavelength': 0.665, 'eo:full_width_half_max': 0.01, 'name': 'oa08'}, {'eo:center_wavelength': 0.67375, 'eo:full_width_half_max': 0.0075, 'name': 'oa09'}, {'eo:center_wavelength': 0.68125, 'eo:full_width_half_max': 0.0075, 'name': 'oa10'}, {'eo:center_wavelength': 0.70875, 'eo:full_width_half_max': 0.01, 'name': 'oa11'}, {'eo:center_wavelength': 0.75375, 'eo:full_width_half_max': 0.0075, 'name': 'oa12'}, {'eo:center_wavelength': 0.76125, 'eo:full_width_half_max': 0.0025, 'name': 'oa13'}, {'eo:center_wavelength': 0.764375, 'eo:full_width_half_max': 0.00375, 'name': 'oa14'}, {'eo:center_wavelength': 0.7675000000000001, 'eo:full_width_half_max': 0.0025, 'name': 'oa15'}, {'eo:center_wavelength': 0.77875, 'eo:full_width_half_max': 0.015, 'name': 'oa16'}, {'eo:center_wavelength': 0.865, 'eo:full_width_half_max': 0.02, 'name': 'oa17'}, {'eo:center_wavelength': 0.885, 'eo:full_width_half_max': 0.01, 'name': 'oa18'}, {'eo:center_wavelength': 0.9, 'eo:full_width_half_max': 0.01, 'name': 'oa19'}, {'eo:center_wavelength': 0.9400000000000001, 'eo:full_width_half_max': 0.02, 'name': 'oa20'}, {'eo:center_wavelength': 1.02, 'eo:full_width_half_max': 0.04, 'name': 'oa21'}], 'classification:classes': [{'count': 742117, 'name': 'invalid', 'percentage': 4.0}, {'count': 4091, 'name': 'cosmetic', 'percentage': 0.0}, {'count': 4489249, 'name': 'duplicated', 'percentage': 23.0}, {'count': 2, 'name': 'saturated', 'percentage': 0.0}, {'count': 11, 'name': 'dubious', 'percentage': 0.0}, {'name': 'saline_water', 'percentage': 26.0}, {'name': 'bright', 'percentage': 46.0}, {'name': 'coastal', 'percentage': 0.0}, {'name': 'fresh_water', 'percentage': 1.0}, {'name': 'tidal', 'percentage': 2.0}], 'constellation': 'sentinel-3', 'created': '2026-03-14T10:08:20Z', 'datetime': '2026-03-13T10:02:18.256763Z', 'end_datetime': '2026-03-13T10:03:48.256763Z', 'eopf:instrument_mode': 'Earth Observation', 'gsd': 270, 'instruments': ['olci'], 'mission': 'copernicus', 'platform': 'sentinel-3b', 'processing:facility': 'OPE', 'processing:level': 'L1', 'processing:lineage': 'systematic', 'processing:software': {'PUG': '03.50'}, 'processing:version': 'TODO', 'product:timeliness': 'P1M', 'product:timeliness_category': 'NT', 'product:type': 'S03OLCEFR', 'proj:shape': [{'columns': 4865, 'name': 'FR', 'rows': 4091}], 'providers': [{'name': 'Copernicus Ground Segment', 'roles': ['processor']}, {'name': 'ESA', 'roles': ['producer']}], 'sat:absolute_orbit': 41047, 'sat:orbit_state': 'descending', 'sat:platform_international_designator': '2018-039A', 'sat:relative_orbit': 350, 'start_datetime': '2026-03-13T10:00:48.256763Z'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.2.0/schema.json', 'https://stac-extensions.github.io/classification/v2.0.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/sat/v1.1.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.2.0/schema.json', 'https://stac-extensions.github.io/projection/v2.0.0/schema.json', 'https://stac-extensions.github.io/product/v0.1.0/schema.json'], 'stac_version': '1.1.0', 'type': 'Feature'}

As an example, we plot the red band (oa08_radiance), which will trigger loading and visualization of the data. We also plot the 2D curvilinear latitude and longitude grids, which can be used for geolocation.

Note: To speed up rendering of large datasets in Matplotlib, we plot the data at a lower resolution (every 4th pixel).

In [5]:

fig, ax = plt.subplots(1, 3, figsize=(15, 4))
dt.measurements.oa08_radiance[::4, ::4].plot.imshow(ax=ax[0], vmin=0, vmax=300)
dt.measurements.latitude[::4, ::4].plot.imshow(ax=ax[1], cmap="viridis")
dt.measurements.longitude[::4, ::4].plot.imshow(ax=ax[2], cmap="viridis")
plt.tight_layout()

fig, ax = plt.subplots(1, 3, figsize=(15, 4)) dt.measurements.oa08_radiance[::4, ::4].plot.imshow(ax=ax[0], vmin=0, vmax=300) dt.measurements.latitude[::4, ::4].plot.imshow(ax=ax[1], cmap="viridis") dt.measurements.longitude[::4, ::4].plot.imshow(ax=ax[2], cmap="viridis") plt.tight_layout()

Open Sentinel-3 OLCI L1 EFR Radiance Group as Dataset¶

We can directly access the individual reflectance group (radianceData) as xarray.Dataset objects. The opening parameters are stored in the asset’s extra field "xarray:open_dataset_kwargs".

In [6]:

ds = xr.open_dataset(
    item.assets["radianceData"].href,
    **item.assets["radianceData"].extra_fields["xarray:open_dataset_kwargs"],
)
ds

ds = xr.open_dataset( item.assets["radianceData"].href, **item.assets["radianceData"].extra_fields["xarray:open_dataset_kwargs"], ) ds

Out[6]:

<xarray.Dataset> Size: 4GB
Dimensions:        (rows: 4091, columns: 4865)
Coordinates:
    altitude       (rows, columns) int16 40MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    latitude       (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    longitude      (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    time_stamp     (rows) datetime64[ns] 33kB dask.array<chunksize=(1024,), meta=np.ndarray>
Dimensions without coordinates: rows, columns
Data variables: (12/21)
    oa01_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa02_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa03_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa04_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa05_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa06_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    ...             ...
    oa16_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa17_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa18_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa19_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa20_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa21_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • rows: 4091
  • columns: 4865
• Coordinates: (4)
  • altitude
    (rows, columns)
    int16
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i2

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f4

    fill_value :

    -32768

    long_name :

    DEM corrected altitude

    standard_name :

    altitude

    units :

    m

    valid_max :

    9000

    valid_min :

    -1000

    Array Chunk Bytes 37.96 MiB 2.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type int16 numpy.ndarray

  • latitude
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    DEM corrected latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90000000

    valid_min :

    -90000000

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • longitude
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    DEM corrected longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180000000

    valid_min :

    -180000000

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • time_stamp
    (rows)
    datetime64[ns]
    dask.array<chunksize=(1024,), meta=np.ndarray>

    dimensions :

    ['rows']

    dtype :

    <M8[ns]

    fill_value :

    -1

    long_name :

    elapsed time since 01 Jan 2000 00:00:00 as time stamp for each row

    standard_name :

    time

    Array Chunk Bytes 31.96 kiB 8.00 kiB Shape (4091,) (1024,) Dask graph 4 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

• Data variables: (21)
  • oa01_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 01', 'scale_factor': 0.013634907081723213, 'short_name': 'oa01_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 01

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa02_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 02', 'scale_factor': 0.011490973643958569, 'short_name': 'oa02_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 02

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa03_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 03', 'scale_factor': 0.012588496319949627, 'short_name': 'oa03_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 03

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa04_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 04', 'scale_factor': 0.011396283283829689, 'short_name': 'oa04_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 04

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa05_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 05', 'scale_factor': 0.012233924120664597, 'short_name': 'oa05_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 05

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa06_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 06', 'scale_factor': 0.010038239881396294, 'short_name': 'oa06_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 06

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa07_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 07', 'scale_factor': 0.01056649163365364, 'short_name': 'oa07_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 07

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa08_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 08', 'scale_factor': 0.008974685333669186, 'short_name': 'oa08_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 08

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa09_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 09', 'scale_factor': 0.009690877981483936, 'short_name': 'oa09_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 09

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa10_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 10', 'scale_factor': 0.008986380882561207, 'short_name': 'oa10_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 10

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa11_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 11', 'scale_factor': 0.008361034095287323, 'short_name': 'oa11_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 11

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa12_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 12', 'scale_factor': 0.007491027936339378, 'short_name': 'oa12_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 12

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa13_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 13', 'scale_factor': 0.007861301302909851, 'short_name': 'oa13_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 13

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa14_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 14', 'scale_factor': 0.007529641035944223, 'short_name': 'oa14_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 14

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa15_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 15', 'scale_factor': 0.00788309145718813, 'short_name': 'oa15_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 15

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa16_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 16', 'scale_factor': 0.007152478210628033, 'short_name': 'oa16_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 16

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa17_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 17', 'scale_factor': 0.006055756937712431, 'short_name': 'oa17_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 17

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa18_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 18', 'scale_factor': 0.0060341269709169865, 'short_name': 'oa18_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 18

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa19_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 19', 'scale_factor': 0.0055409627966582775, 'short_name': 'oa19_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 19

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa20_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 20', 'scale_factor': 0.005095385946333408, 'short_name': 'oa20_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 20

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa21_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 21', 'scale_factor': 0.004291551187634468, 'short_name': 'oa21_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 21

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (0)
• Attributes: (0)

We can also filter the varaibles by band names as shown below:

In [7]:

ds = xr.open_dataset(
    item.assets["radianceData"].href,
    **item.assets["radianceData"].extra_fields["xarray:open_dataset_kwargs"],
    variables="oa0[468]",
)
ds

ds = xr.open_dataset( item.assets["radianceData"].href, **item.assets["radianceData"].extra_fields["xarray:open_dataset_kwargs"], variables="oa0[468]", ) ds

Out[7]:

<xarray.Dataset> Size: 478MB
Dimensions:        (rows: 4091, columns: 4865)
Dimensions without coordinates: rows, columns
Data variables:
    oa04_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa06_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa08_radiance  (rows, columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • rows: 4091
  • columns: 4865
• Coordinates: (0)
• Data variables: (3)
  • oa04_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 04', 'scale_factor': 0.011396283283829689, 'short_name': 'oa04_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 04

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa06_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 06', 'scale_factor': 0.010038239881396294, 'short_name': 'oa06_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 06

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa08_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 08', 'scale_factor': 0.008974685333669186, 'short_name': 'oa08_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 08

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (0)
• Attributes: (0)

And we can again plot the red band, which triggers loading the data.

In [8]:

ds.oa08_radiance[::4, ::4].plot(vmax=300)

ds.oa08_radiance[::4, ::4].plot(vmax=300)

Out[8]:

<matplotlib.collections.QuadMesh at 0x7b2c704a8690>

Open Sentinel-3 OLCI L1 EFR as Dataset¶

The xarray.DataTree model was introduced in xarray v2024.10.0 (October 2024). To maintain compatibility with workflows based on xr.Dataset, the function xarray.open_dataset(..., engine="eopf-zarr", op_mode="native") is provided, which flattens the DataTree into a single dataset.

During this process, hierarchical groups in the Zarr product are merged, and variable as well as dimension names are prefixed with their group paths (using _ by default) to ensure uniqueness. For example, measurements/oa08_radiance becomes measurements_oa08_radiance.

In [9]:

ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    op_mode="native",
    chunks={},
)
ds

ds = xr.open_dataset( item.assets["product"].href, engine="eopf-zarr", op_mode="native", chunks={}, ) ds

Out[9]:

<xarray.Dataset> Size: 8GB
Dimensions:                                                 (
                                                             conditions_geometry_rows: 4091,
                                                             conditions_geometry_columns: 77,
                                                             conditions_image_rows: 4091,
                                                             conditions_image_columns: 4865,
                                                             conditions_instrument_bands: 21,
                                                             ...
                                                             conditions_orphans_rows: 4091,
                                                             conditions_orphans_removed_pixels: 150,
                                                             measurements_rows: 4091,
                                                             measurements_columns: 4865,
                                                             quality_rows: 4091,
                                                             quality_columns: 4865)
Coordinates: (12/23)
    conditions_geometry_latitude                            (conditions_geometry_rows, conditions_geometry_columns) float64 3MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
    conditions_geometry_longitude                           (conditions_geometry_rows, conditions_geometry_columns) float64 3MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
    conditions_image_altitude                               (conditions_image_rows, conditions_image_columns) int16 40MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    conditions_image_latitude                               (conditions_image_rows, conditions_image_columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    conditions_image_longitude                              (conditions_image_rows, conditions_image_columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    conditions_image_time_stamp                             (conditions_image_rows) datetime64[ns] 33kB dask.array<chunksize=(1024,), meta=np.ndarray>
    ...                                                      ...
    measurements_longitude                                  (measurements_rows, measurements_columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    measurements_time_stamp                                 (measurements_rows) datetime64[ns] 33kB dask.array<chunksize=(1024,), meta=np.ndarray>
    quality_altitude                                        (quality_rows, quality_columns) int16 40MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    quality_latitude                                        (quality_rows, quality_columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    quality_longitude                                       (quality_rows, quality_columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    quality_time_stamp                                      (quality_rows) datetime64[ns] 33kB dask.array<chunksize=(1024,), meta=np.ndarray>
Dimensions without coordinates: conditions_geometry_rows,
                                conditions_geometry_columns,
                                conditions_image_rows,
                                conditions_image_columns,
                                conditions_instrument_bands,
                                conditions_instrument_bands2,
                                conditions_meteorology_rows,
                                conditions_meteorology_columns,
                                conditions_orphans_rows, measurements_rows,
                                measurements_columns, quality_rows,
                                quality_columns
Data variables: (12/63)
    conditions_geometry_oaa                                 (conditions_geometry_rows, conditions_geometry_columns) float64 3MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
    conditions_geometry_oza                                 (conditions_geometry_rows, conditions_geometry_columns) float64 3MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
    conditions_geometry_saa                                 (conditions_geometry_rows, conditions_geometry_columns) float64 3MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
    conditions_geometry_sza                                 (conditions_geometry_rows, conditions_geometry_columns) float64 3MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
    conditions_image_detector_index                         (conditions_image_rows, conditions_image_columns) float32 80MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    conditions_image_frame_offset                           (conditions_image_rows, conditions_image_columns) float32 80MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    ...                                                      ...
    quality_oa17_radiance_unc                               (quality_rows, quality_columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    quality_oa18_radiance_unc                               (quality_rows, quality_columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    quality_oa19_radiance_unc                               (quality_rows, quality_columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    quality_oa20_radiance_unc                               (quality_rows, quality_columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    quality_oa21_radiance_unc                               (quality_rows, quality_columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    quality_quality_flags                                   (quality_rows, quality_columns) uint32 80MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
Attributes: (3)

xarray.Dataset

• Dimensions:
  • conditions_geometry_rows: 4091
  • conditions_geometry_columns: 77
  • conditions_image_rows: 4091
  • conditions_image_columns: 4865
  • conditions_instrument_bands: 21
  • conditions_instrument_detectors: 3700
  • conditions_instrument_bands2: 21
  • conditions_meteorology_pressure_level: 25
  • conditions_meteorology_rows: 4091
  • conditions_meteorology_columns: 77
  • conditions_meteorology_wind_vector: 2
  • conditions_orphans_rows: 4091
  • conditions_orphans_removed_pixels: 150
  • measurements_rows: 4091
  • measurements_columns: 4865
  • quality_rows: 4091
  • quality_columns: 4865
• Coordinates: (23)
  • conditions_geometry_latitude
    (conditions_geometry_rows, conditions_geometry_columns)
    float64
    dask.array<chunksize=(1024, 77), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    latitude of tie points

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90000000

    valid_min :

    -90000000

    Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_geometry_longitude
    (conditions_geometry_rows, conditions_geometry_columns)
    float64
    dask.array<chunksize=(1024, 77), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    longitude of tie points

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180000000

    valid_min :

    -180000000

    Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_image_altitude
    (conditions_image_rows, conditions_image_columns)
    int16
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i2

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f4

    fill_value :

    -32768

    long_name :

    DEM corrected altitude

    standard_name :

    altitude

    units :

    m

    valid_max :

    9000

    valid_min :

    -1000

    Array Chunk Bytes 37.96 MiB 2.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type int16 numpy.ndarray

  • conditions_image_latitude
    (conditions_image_rows, conditions_image_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    DEM corrected latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90000000

    valid_min :

    -90000000

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_image_longitude
    (conditions_image_rows, conditions_image_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    DEM corrected longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180000000

    valid_min :

    -180000000

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_image_time_stamp
    (conditions_image_rows)
    datetime64[ns]
    dask.array<chunksize=(1024,), meta=np.ndarray>

    dimensions :

    ['rows']

    dtype :

    <M8[ns]

    fill_value :

    -1

    long_name :

    elapsed time since 01 Jan 2000 00:00:00 as time stamp for each row

    standard_name :

    time

    Array Chunk Bytes 31.96 kiB 8.00 kiB Shape (4091,) (1024,) Dask graph 4 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • conditions_instrument_detectors
    (conditions_instrument_detectors)
    int64
    0 1 2 3 4 ... 3696 3697 3698 3699

    dimensions :

    ['detectors']

    dtype :

    <i8

    long_name :

    instrument detectors

    array([   0,    1,    2, ..., 3697, 3698, 3699], shape=(3700,))

  • conditions_meteorology_latitude
    (conditions_meteorology_rows, conditions_meteorology_columns)
    float64
    dask.array<chunksize=(1024, 77), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    latitude of tie points

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90000000

    valid_min :

    -90000000

    Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_meteorology_longitude
    (conditions_meteorology_rows, conditions_meteorology_columns)
    float64
    dask.array<chunksize=(1024, 77), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    longitude of tie points

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180000000

    valid_min :

    -180000000

    Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_meteorology_pressure_level
    (conditions_meteorology_pressure_level)
    int64
    0 1 2 3 4 5 6 ... 19 20 21 22 23 24

    dimensions :

    ['pressure_level']

    dtype :

    <i8

    long_name :

    coordinates of the vertical temperature profile

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24])

  • conditions_meteorology_wind_vector
    (conditions_meteorology_wind_vector)
    int64
    0 1

    dimensions :

    ['wind_vector']

    dtype :

    <i8

    long_name :

    dimensions of horizontal wind vector

    array([0, 1])

  • conditions_orphans_altitude
    (conditions_orphans_rows, conditions_orphans_removed_pixels)
    int16
    dask.array<chunksize=(1024, 150), meta=np.ndarray>

    dimensions :

    ['rows', 'removed_pixels']

    dtype :

    <i2

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f4

    fill_value :

    -32768

    long_name :

    altitude above reference ellipsoid for removed pixels

    standard_name :

    altitude

    units :

    m

    valid_max :

    9000

    valid_min :

    -1000

    Array Chunk Bytes 1.17 MiB 300.00 kiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type int16 numpy.ndarray

  • conditions_orphans_latitude
    (conditions_orphans_rows, conditions_orphans_removed_pixels)
    float64
    dask.array<chunksize=(1024, 150), meta=np.ndarray>

    dimensions :

    ['rows', 'removed_pixels']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    DEM corrected latitude for removed pixels

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90000000

    valid_min :

    -90000000

    Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_orphans_longitude
    (conditions_orphans_rows, conditions_orphans_removed_pixels)
    float64
    dask.array<chunksize=(1024, 150), meta=np.ndarray>

    dimensions :

    ['rows', 'removed_pixels']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    DEM corrected longitude for removed pixels

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180000000

    valid_min :

    -180000000

    Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_orphans_removed_pixels
    (conditions_orphans_removed_pixels)
    int64
    0 1 2 3 4 5 ... 145 146 147 148 149

    dimensions :

    ['removed_pixels']

    dtype :

    <i8

    long_name :

    maximum removed pixels per line

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149])

  • measurements_altitude
    (measurements_rows, measurements_columns)
    int16
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i2

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f4

    fill_value :

    -32768

    long_name :

    DEM corrected altitude

    standard_name :

    altitude

    units :

    m

    valid_max :

    9000

    valid_min :

    -1000

    Array Chunk Bytes 37.96 MiB 2.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type int16 numpy.ndarray

  • measurements_latitude
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    DEM corrected latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90000000

    valid_min :

    -90000000

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_longitude
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    DEM corrected longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180000000

    valid_min :

    -180000000

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_time_stamp
    (measurements_rows)
    datetime64[ns]
    dask.array<chunksize=(1024,), meta=np.ndarray>

    dimensions :

    ['rows']

    dtype :

    <M8[ns]

    fill_value :

    -1

    long_name :

    elapsed time since 01 Jan 2000 00:00:00 as time stamp for each row

    standard_name :

    time

    Array Chunk Bytes 31.96 kiB 8.00 kiB Shape (4091,) (1024,) Dask graph 4 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • quality_altitude
    (quality_rows, quality_columns)
    int16
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i2

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f4

    fill_value :

    -32768

    long_name :

    DEM corrected altitude

    standard_name :

    altitude

    units :

    m

    valid_max :

    9000

    valid_min :

    -1000

    Array Chunk Bytes 37.96 MiB 2.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type int16 numpy.ndarray

  • quality_latitude
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    DEM corrected latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90000000

    valid_min :

    -90000000

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_longitude
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    DEM corrected longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180000000

    valid_min :

    -180000000

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_time_stamp
    (quality_rows)
    datetime64[ns]
    dask.array<chunksize=(1024,), meta=np.ndarray>

    dimensions :

    ['rows']

    dtype :

    <M8[ns]

    fill_value :

    -1

    long_name :

    elapsed time since 01 Jan 2000 00:00:00 as time stamp for each row

    standard_name :

    time

    Array Chunk Bytes 31.96 kiB 8.00 kiB Shape (4091,) (1024,) Dask graph 4 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

• Data variables: (63)
  • conditions_geometry_oaa
    (conditions_geometry_rows, conditions_geometry_columns)
    float64
    dask.array<chunksize=(1024, 77), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<i4', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': -2147483648, 'long_name': 'observation (viewing) azimuth angle', 'scale_factor': 1e-06, 'short_name': 'oaa', 'units': 'degrees', 'valid_max': 180000000, 'valid_min': -180000000}

    dtype :

    <i4

    long_name :

    observation (viewing) azimuth angle

    units :

    degrees

    Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_geometry_oza
    (conditions_geometry_rows, conditions_geometry_columns)
    float64
    dask.array<chunksize=(1024, 77), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u4', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 4294967295, 'long_name': 'observation (viewing) zenith angle', 'scale_factor': 1e-06, 'short_name': 'oza', 'units': 'degrees', 'valid_max': 180000000, 'valid_min': 0}

    dtype :

    <u4

    long_name :

    observation (viewing) zenith angle

    units :

    degrees

    Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_geometry_saa
    (conditions_geometry_rows, conditions_geometry_columns)
    float64
    dask.array<chunksize=(1024, 77), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<i4', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': -2147483648, 'long_name': 'sun azimuth angle', 'scale_factor': 1e-06, 'short_name': 'saa', 'units': 'degrees', 'valid_max': 180000000, 'valid_min': -180000000}

    dtype :

    <i4

    long_name :

    sun azimuth angle

    units :

    degrees

    Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_geometry_sza
    (conditions_geometry_rows, conditions_geometry_columns)
    float64
    dask.array<chunksize=(1024, 77), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u4', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 4294967295, 'long_name': 'sun zenith angle', 'scale_factor': 1e-06, 'short_name': 'sza', 'units': 'degrees', 'valid_max': 180000000, 'valid_min': 0}

    dtype :

    <u4

    long_name :

    sun zenith angle

    units :

    degrees

    Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • conditions_image_detector_index
    (conditions_image_rows, conditions_image_columns)
    float32
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'fill_value': -1, 'long_name': 'detector index for each pixel', 'short_name': 'detector_index', 'valid_max': 3699, 'valid_min': 0}

    dtype :

    <i2

    long_name :

    detector index for each pixel

    Array Chunk Bytes 75.92 MiB 4.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_image_frame_offset
    (conditions_image_rows, conditions_image_columns)
    float32
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<i1', 'eopf_is_masked': True, 'fill_value': -128, 'long_name': 'resampling along-track frame offset', 'short_name': 'frame_offset', 'valid_max': 15, 'valid_min': -15}

    dtype :

    <i1

    long_name :

    resampling along-track frame offset

    Array Chunk Bytes 75.92 MiB 4.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_instrument_fwhm
    (conditions_instrument_bands, conditions_instrument_detectors)
    float32
    dask.array<chunksize=(21, 3700), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['detectors'], 'dimensions': ['bands', 'detectors'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'OLCI bandwidth (Full Width at Half Maximum)', 'short_name': 'fwhm', 'units': 'nm', 'valid_max': 650.0, 'valid_min': 0.0}

    dtype :

    <f4

    long_name :

    OLCI bandwidth (Full Width at Half Maximum)

    units :

    nm

    Array Chunk Bytes 303.52 kiB 303.52 kiB Shape (21, 3700) (21, 3700) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_instrument_lambda0
    (conditions_instrument_bands, conditions_instrument_detectors)
    float32
    dask.array<chunksize=(21, 3700), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['detectors'], 'dimensions': ['bands', 'detectors'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'OLCI characterised central wavelength', 'short_name': 'lambda0', 'units': 'nm', 'valid_max': 1040.0, 'valid_min': 390.0}

    dtype :

    <f4

    long_name :

    OLCI characterised central wavelength

    units :

    nm

    Array Chunk Bytes 303.52 kiB 303.52 kiB Shape (21, 3700) (21, 3700) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_instrument_relative_spectral_covariance
    (conditions_instrument_bands, conditions_instrument_bands2)
    float32
    dask.array<chunksize=(21, 21), meta=np.ndarray>

    _eopf_attrs :

    {'dimensions': ['bands', 'bands2'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': 0, 'long_name': 'relative spectral covariance matrix', 'short_name': 'relative_spectral_covariance'}

    dtype :

    <f4

    long_name :

    relative spectral covariance matrix

    Array Chunk Bytes 1.72 kiB 1.72 kiB Shape (21, 21) (21, 21) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_instrument_solar_flux
    (conditions_instrument_bands, conditions_instrument_detectors)
    float32
    dask.array<chunksize=(21, 3700), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['detectors'], 'dimensions': ['bands', 'detectors'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'in-band solar irradiance, seasonally corrected', 'short_name': 'solar_flux', 'units': 'mW.m-2.nm-1', 'valid_max': 2500.0, 'valid_min': 500.0}

    dtype :

    <f4

    long_name :

    in-band solar irradiance, seasonally corrected

    units :

    mW.m-2.nm-1

    Array Chunk Bytes 303.52 kiB 303.52 kiB Shape (21, 3700) (21, 3700) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_atmospheric_temperature_profile
    (conditions_meteorology_pressure_level, conditions_meteorology_rows, conditions_meteorology_columns)
    float32
    dask.array<chunksize=(9, 1024, 77), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'pressure_level', 'longitude'], 'dimensions': ['pressure_level', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'air temperature vertical profile', 'short_name': 'atmospheric_temperature_profile', 'standard_name': 'air_temperature', 'units': 'K', 'valid_max': 400.0, 'valid_min': 0.0}

    dtype :

    <f4

    long_name :

    air temperature vertical profile

    standard_name :

    air_temperature

    units :

    K

    Array Chunk Bytes 30.04 MiB 2.71 MiB Shape (25, 4091, 77) (9, 1024, 77) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_horizontal_wind
    (conditions_meteorology_wind_vector, conditions_meteorology_rows, conditions_meteorology_columns)
    float32
    dask.array<chunksize=(2, 1024, 77), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['wind_vector', 'latitude', 'longitude'], 'dimensions': ['wind_vector', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': 9.969209968386869e+36, 'long_name': 'horizontal wind vector at 10m altitude', 'short_name': 'horizontal_wind', 'units': 'm.s-1', 'valid_max': 100.0, 'valid_min': -100.0}

    dtype :

    <f4

    long_name :

    horizontal wind vector at 10m altitude

    units :

    m.s-1

    Array Chunk Bytes 2.40 MiB 616.00 kiB Shape (2, 4091, 77) (2, 1024, 77) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_humidity
    (conditions_meteorology_rows, conditions_meteorology_columns)
    float32
    dask.array<chunksize=(1024, 77), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'relative humidity', 'short_name': 'humidity', 'standard_name': 'relative_humidity', 'units': '%', 'valid_max': 100.0, 'valid_min': 0.0}

    dtype :

    <f4

    long_name :

    relative humidity

    standard_name :

    relative_humidity

    units :

    %

    Array Chunk Bytes 1.20 MiB 308.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_sea_level_pressure
    (conditions_meteorology_rows, conditions_meteorology_columns)
    float32
    dask.array<chunksize=(1024, 77), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'mean sea level pressure', 'short_name': 'sea_level_pressure', 'standard_name': 'air_pressure_at_sea_level', 'units': 'hPa', 'valid_max': 1500.0, 'valid_min': 0.0}

    dtype :

    <f4

    long_name :

    mean sea level pressure

    standard_name :

    air_pressure_at_sea_level

    units :

    hPa

    Array Chunk Bytes 1.20 MiB 308.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_total_columnar_water_vapour
    (conditions_meteorology_rows, conditions_meteorology_columns)
    float32
    dask.array<chunksize=(1024, 77), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'total column water vapour content', 'short_name': 'total_columnar_water_vapour', 'standard_name': 'atmosphere_water_vapor_content', 'units': 'kg.m-2', 'valid_max': 100.0, 'valid_min': 0.0}

    dtype :

    <f4

    long_name :

    total column water vapour content

    standard_name :

    atmosphere_water_vapor_content

    units :

    kg.m-2

    Array Chunk Bytes 1.20 MiB 308.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_meteorology_total_ozone
    (conditions_meteorology_rows, conditions_meteorology_columns)
    float32
    dask.array<chunksize=(1024, 77), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'total columnar ozone content', 'short_name': 'total_ozone', 'standard_name': 'atmosphere_mass_content_of_ozone', 'units': 'kg.m-2', 'valid_max': 100.0, 'valid_min': 0.0}

    dtype :

    <f4

    long_name :

    total columnar ozone content

    standard_name :

    atmosphere_mass_content_of_ozone

    units :

    kg.m-2

    Array Chunk Bytes 1.20 MiB 308.00 kiB Shape (4091, 77) (1024, 77) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_orphans_detector_index
    (conditions_orphans_rows, conditions_orphans_removed_pixels)
    float32
    dask.array<chunksize=(1024, 150), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<i2', 'eopf_is_masked': True, 'fill_value': -1, 'long_name': 'detector index for removed pixels', 'short_name': 'detector_index_orphans', 'valid_max': 3699, 'valid_min': 0}

    dtype :

    <i2

    long_name :

    detector index for removed pixels

    Array Chunk Bytes 2.34 MiB 600.00 kiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_orphans_frame_offset
    (conditions_orphans_rows, conditions_orphans_removed_pixels)
    float32
    dask.array<chunksize=(1024, 150), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<i1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -128, 'long_name': 'resampling along-track frame offset', 'short_name': 'frame_offset_orphans', 'valid_max': 15, 'valid_min': -15}

    dtype :

    <i1

    long_name :

    resampling along-track frame offset

    Array Chunk Bytes 2.34 MiB 600.00 kiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

  • conditions_orphans_nb_removed_pixels
    (conditions_orphans_rows)
    uint16
    dask.array<chunksize=(1024,), meta=np.ndarray>

    _eopf_attrs :

    {'dimensions': ['rows'], 'dtype': '<u2', 'long_name': 'number of removed pixels per frame', 'short_name': 'nb_removed_pixels'}

    dtype :

    <u2

    long_name :

    number of removed pixels per frame

    Array Chunk Bytes 7.99 kiB 2.00 kiB Shape (4091,) (1024,) Dask graph 4 chunks in 2 graph layers Data type uint16 numpy.ndarray

  • conditions_orphans_sza
    (conditions_orphans_rows, conditions_orphans_removed_pixels)
    float64
    dask.array<chunksize=(1024, 150), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['removed_pixels', 'latitude', 'longitude', 'altitude'], 'dimensions': ['rows', 'removed_pixels'], 'dtype': '<u4', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 4294967295, 'long_name': 'sun zenith angle for removed pixels', 'scale_factor': 1e-06, 'short_name': 'sza_orphans', 'units': 'degrees', 'valid_max': 180000000, 'valid_min': 0}

    dtype :

    <u4

    long_name :

    sun zenith angle for removed pixels

    units :

    degrees

    Array Chunk Bytes 4.68 MiB 1.17 MiB Shape (4091, 150) (1024, 150) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa01_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 01', 'scale_factor': 0.013634907081723213, 'short_name': 'oa01_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 01

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa02_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 02', 'scale_factor': 0.011490973643958569, 'short_name': 'oa02_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 02

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa03_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 03', 'scale_factor': 0.012588496319949627, 'short_name': 'oa03_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 03

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa04_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 04', 'scale_factor': 0.011396283283829689, 'short_name': 'oa04_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 04

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa05_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 05', 'scale_factor': 0.012233924120664597, 'short_name': 'oa05_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 05

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa06_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 06', 'scale_factor': 0.010038239881396294, 'short_name': 'oa06_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 06

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa07_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 07', 'scale_factor': 0.01056649163365364, 'short_name': 'oa07_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 07

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa08_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 08', 'scale_factor': 0.008974685333669186, 'short_name': 'oa08_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 08

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa09_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 09', 'scale_factor': 0.009690877981483936, 'short_name': 'oa09_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 09

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa10_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 10', 'scale_factor': 0.008986380882561207, 'short_name': 'oa10_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 10

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa11_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 11', 'scale_factor': 0.008361034095287323, 'short_name': 'oa11_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 11

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa12_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 12', 'scale_factor': 0.007491027936339378, 'short_name': 'oa12_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 12

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa13_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 13', 'scale_factor': 0.007861301302909851, 'short_name': 'oa13_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 13

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa14_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 14', 'scale_factor': 0.007529641035944223, 'short_name': 'oa14_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 14

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa15_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 15', 'scale_factor': 0.00788309145718813, 'short_name': 'oa15_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 15

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa16_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 16', 'scale_factor': 0.007152478210628033, 'short_name': 'oa16_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 16

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa17_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 17', 'scale_factor': 0.006055756937712431, 'short_name': 'oa17_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 17

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa18_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 18', 'scale_factor': 0.0060341269709169865, 'short_name': 'oa18_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 18

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa19_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 19', 'scale_factor': 0.0055409627966582775, 'short_name': 'oa19_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 19

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa20_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 20', 'scale_factor': 0.005095385946333408, 'short_name': 'oa20_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 20

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements_oa21_radiance
    (measurements_rows, measurements_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 21', 'scale_factor': 0.004291551187634468, 'short_name': 'oa21_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 21

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa01_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.19358299672603607, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 01', 'scale_factor': 0.0054720002226531506, 'short_name': 'oa01_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 01

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa02_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.21523700654506683, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 02', 'scale_factor': 0.005102999974042177, 'short_name': 'oa02_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 02

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa03_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.2714740037918091, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 03', 'scale_factor': 0.005386999808251858, 'short_name': 'oa03_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 03

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa04_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.30964401364326477, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 04', 'scale_factor': 0.005243999883532524, 'short_name': 'oa04_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 04

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa05_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.3706839978694916, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 05', 'scale_factor': 0.005743000190705061, 'short_name': 'oa05_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 05

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa06_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.4443579912185669, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 06', 'scale_factor': 0.006068000104278326, 'short_name': 'oa06_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 06

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa07_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.5721830129623413, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 07', 'scale_factor': 0.00686699990183115, 'short_name': 'oa07_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 07

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa08_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.6707040071487427, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 08', 'scale_factor': 0.007137000095099211, 'short_name': 'oa08_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 08

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa09_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.6211689710617065, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 09', 'scale_factor': 0.006550000049173832, 'short_name': 'oa09_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 09

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa10_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.6792640089988708, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 10', 'scale_factor': 0.006779999937862158, 'short_name': 'oa10_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 10

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa11_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.7377849817276001, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 11', 'scale_factor': 0.007145000156015158, 'short_name': 'oa11_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 11

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa12_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.7818710207939148, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 12', 'scale_factor': 0.006775000132620335, 'short_name': 'oa12_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 12

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa13_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.7220579981803894, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 13', 'scale_factor': 0.005898999981582165, 'short_name': 'oa13_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 13

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa14_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.6917920112609863, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 14', 'scale_factor': 0.006147999782115221, 'short_name': 'oa14_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 14

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa15_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.7225130200386047, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 15', 'scale_factor': 0.006641000043600798, 'short_name': 'oa15_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 15

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa16_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.9848459959030151, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 16', 'scale_factor': 0.008098999969661236, 'short_name': 'oa16_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 16

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa17_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.9873489737510681, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 17', 'scale_factor': 0.008390000090003014, 'short_name': 'oa17_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 17

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa18_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.7908260226249695, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 18', 'scale_factor': 0.007825000211596489, 'short_name': 'oa18_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 18

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa19_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.8344600200653076, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 19', 'scale_factor': 0.007402999792248011, 'short_name': 'oa19_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 19

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa20_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.9243649840354919, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 20', 'scale_factor': 0.008166000247001648, 'short_name': 'oa20_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 20

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_oa21_radiance_unc
    (quality_rows, quality_columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': -0.5929380059242249, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 21', 'scale_factor': 0.0057830000296235085, 'short_name': 'oa21_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

    dtype :

    <u1

    long_name :

    log10 scaled radiometric uncertainty estimate for OLCI acquisition band 21

    units :

    lg(re mW.m-2.sr-1.nm-1)

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • quality_quality_flags
    (quality_rows, quality_columns)
    uint32
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u4', 'eopf_is_masked': True, 'flag_masks': [2147483648, 1073741824, 536870912, 268435456, 134217728, 67108864, 33554432, 16777216, 8388608, 4194304, 2097152, 1048576, 524288], 'flag_meanings': 'land coastline fresh_inland_water tidal_region bright straylight_risk invalid cosmetic duplicated sun-glint_risk dubious saturated partially_saturated', 'long_name': 'classification and quality flags', 'short_name': 'quality_flags'}

    dtype :

    <u4

    flag_masks :

    [2147483648, 1073741824, 536870912, 268435456, 134217728, 67108864, 33554432, 16777216, 8388608, 4194304, 2097152, 1048576, 524288]

    flag_meanings :

    land coastline fresh_inland_water tidal_region bright straylight_risk invalid cosmetic duplicated sun-glint_risk dubious saturated partially_saturated

    long_name :

    classification and quality flags

    Array Chunk Bytes 75.92 MiB 4.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type uint32 numpy.ndarray

• Indexes: (4)
  • conditions_instrument_detectors
    PandasIndex

    PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,
           ...
           3690, 3691, 3692, 3693, 3694, 3695, 3696, 3697, 3698, 3699],
          dtype='int64', name='conditions_instrument_detectors', length=3700))

  • conditions_meteorology_pressure_level
    PandasIndex

    PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24],
          dtype='int64', name='conditions_meteorology_pressure_level'))

  • conditions_meteorology_wind_vector
    PandasIndex

    PandasIndex(Index([0, 1], dtype='int64', name='conditions_meteorology_wind_vector'))

  • conditions_orphans_removed_pixels
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149],
          dtype='int64', name='conditions_orphans_removed_pixels', length=150))

• Attributes: (3)

  other_metadata :

  {'absolute_pass_number': 82094, 'cycle_number': 117, 'data_information': {'accuracy_reference': '0', 'earth_sun_distance': 148692226821, 'measurement_accuracy': '2.000000', 'ocl_status': 'true', 'sampling': {'across_track_distance': 270, 'along_track_distance': 294, 'along_track_time': 44002, 'columns_per_tiepoint': 64, 'rows_per_tiepoint': 1}}, 'eopf_category': 'eoproduct', 'ephemeris': {'start': {'TAI': '2026-03-13T09:25:25.256763', 'UT1': '2026-03-13T09:24:48.319326', 'UTC': '2026-03-13T09:24:48.256763Z', 'position': {'x': -7094686.732, 'y': -1129221.277, 'z': 0.001}, 'velocity': {'x': -249.56373, 'y': 1621.140793, 'z': 7366.211176}}, 'stop': {'TAI': '2026-03-13T11:06:24.413833', 'UT1': '2026-03-13T11:05:47.476392', 'UTC': '2026-03-13T11:05:47.413833Z', 'position': {'x': -6898641.864, 'y': 2004695.114, 'z': 0.003}, 'velocity': {'x': 465.705986, 'y': 1572.789106, 'z': 7366.200009}}}, 'meteo': {'missing_time': 0, 'source': 'ECMWF', 'type': 'ANALYSIS'}, 'phase_identifier': 4, 'product_unit': {'alongtrack_coordinate': 2160, 'duration': 180, 'type': 'FRAME'}, 'relative_pass_number': 700}

  processing_history :

  {'Level-0 Product': [{'adfs': ['S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_TM_0_NAT__G_20260310T192718_20260310T210759_20260310T211703_6041______________SVL_O_NR_OPE.ISIP', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_TM_0_NAT____20260310T192700_20260310T210741_20260310T211958_6041_117_313______ESA_O_AL_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-10T21:20:06.608260Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_TM_0_NAT__G_20260310T192718_20260310T210759_20260310T211703_6041______________SVL_O_NR_OPE.ISIP', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_TM_0_NAT____20260310T192700_20260310T210741_20260310T211958_6041_117_313______ESA_O_AL_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-10T21:20:06.608260Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_CR___G_20260310T193645_20260310T202218_20260310T211706_2733______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260310T192700_20260310T210741_20260310T211958_6041_117_313______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260310T192700_20260310T210741_20260310T211958_6041_117_313______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_CR0____20260310T202114_20260310T202200_20260310T212236_0045_117_313______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-10T21:22:43.551058Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_TM_0_NAT__G_20260313T095009_20260313T113027_20260313T113915_6018______________SVL_O_NR_OPE.ISIP', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_TM_0_NAT____20260313T094951_20260313T113009_20260313T114138_6018_117_350______ESA_O_AL_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:41:45.950214Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_TM_0_NAT__G_20260313T095009_20260313T113015_20260313T113201_6006______________SVL_O_NR_OPE.ISIP', 'S3IPF PDS 001 - 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i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T100439_20260313T100639_20260313T114409_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:44:16.339558Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T100257_20260313T100457_20260313T113924_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T100239_20260313T100439_20260313T114412_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:44:19.991648Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T100857_20260313T101057_20260313T113928_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - 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i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T101239_20260313T101439_20260313T114426_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:44:33.920556Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T101457_20260313T101657_20260313T113938_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T101439_20260313T101639_20260313T114455_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:45:03.371903Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T101657_20260313T101857_20260313T113944_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - 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i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T102639_20260313T102839_20260313T114611_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:46:19.496479Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T102057_20260313T102257_20260313T113946_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T102039_20260313T102239_20260313T114611_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:46:19.606522Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T101057_20260313T101257_20260313T113932_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T101039_20260313T101239_20260313T114642_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:46:50.469877Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T095257_20260313T095457_20260313T114000_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T095241_20260313T095439_20260313T114648_0118_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:46:55.930771Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T100657_20260313T100857_20260313T113927_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T100639_20260313T100839_20260313T114651_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:46:59.118110Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T095457_20260313T095657_20260313T113920_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T095439_20260313T095639_20260313T114719_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:47:27.547967Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T095857_20260313T100057_20260313T113922_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - 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i2r7 - Product Data Format Specification - OLCI Level 1'], 'outputs': ['S3B_OL_1_EFR____20260313T095241_20260313T103636_20260314T083420_2635_117_350______ESA_O_NT_004.SEN3'], 'processor': 'IPF-OL-1-EO', 'time': '2026-03-14T09:39:03.386657Z', 'version': '06.19'}, {'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3IPF PDS 004.1 - Product Data Format Specification - OLCI Level 1', 'Metadata Specification, Excel document S3IPF.PDS.008', 'S3B_OL_1_EFR____20260313T095241_20260313T103636_20260314T083420_2635_117_350______ESA_O_NT_004.SEN3'], 'outputs': [], 'processor': 'PUG', 'time': '2026-03-14T10:14:41.614589Z', 'version': '03.50'}]}

  stac_discovery :

  {'assets': {'atmospheric_temperature_profile': {'href': '/conditions/meteorology/atmospheric_temperature_profile', 'title': 'atmospheric_temperature_profile'}, 'detector_index': {'href': '/conditions/image/detector_index', 'title': 'detector_index'}, 'detector_index_orphans': {'href': '/conditions/orphans/detector_index', 'title': 'detector_index_orphans'}, 'frame_offset': {'href': '/conditions/image/frame_offset', 'title': 'frame_offset'}, 'frame_offset_orphans': {'href': '/conditions/orphans/frame_offset', 'title': 'frame_offset_orphans'}, 'fwhm': {'href': '/conditions/instrument/fwhm', 'title': 'fwhm'}, 'horizontal_wind': {'href': '/conditions/meteorology/horizontal_wind', 'title': 'horizontal_wind'}, 'humidity': {'href': '/conditions/meteorology/humidity', 'title': 'humidity'}, 'lambda0': {'href': '/conditions/instrument/lambda0', 'title': 'lambda0'}, 'nb_removed_pixels': {'href': '/conditions/orphans/nb_removed_pixels', 'title': 'nb_removed_pixels'}, 'oa01_radiance': {'href': '/measurements/oa01_radiance', 'title': 'oa01_radiance'}, 'oa01_radiance_orphans': {'href': '/measurements/orphans/oa01_radiance', 'title': 'oa01_radiance_orphans'}, 'oa01_radiance_unc': {'href': '/quality/oa01_radiance_unc', 'title': 'oa01_radiance_unc'}, 'oa01_radiance_unc_orphans': {'href': '/quality/orphans/oa01_radiance_unc', 'title': 'oa01_radiance_unc_orphans'}, 'oa02_radiance': {'href': '/measurements/oa02_radiance', 'title': 'oa02_radiance'}, 'oa02_radiance_orphans': {'href': '/measurements/orphans/oa02_radiance', 'title': 'oa02_radiance_orphans'}, 'oa02_radiance_unc': {'href': '/quality/oa02_radiance_unc', 'title': 'oa02_radiance_unc'}, 'oa02_radiance_unc_orphans': {'href': '/quality/orphans/oa02_radiance_unc', 'title': 'oa02_radiance_unc_orphans'}, 'oa03_radiance': {'href': '/measurements/oa03_radiance', 'title': 'oa03_radiance'}, 'oa03_radiance_orphans': {'href': '/measurements/orphans/oa03_radiance', 'title': 'oa03_radiance_orphans'}, 'oa03_radiance_unc': {'href': '/quality/oa03_radiance_unc', 'title': 'oa03_radiance_unc'}, 'oa03_radiance_unc_orphans': {'href': '/quality/orphans/oa03_radiance_unc', 'title': 'oa03_radiance_unc_orphans'}, 'oa04_radiance': {'href': '/measurements/oa04_radiance', 'title': 'oa04_radiance'}, 'oa04_radiance_orphans': {'href': '/measurements/orphans/oa04_radiance', 'title': 'oa04_radiance_orphans'}, 'oa04_radiance_unc': {'href': '/quality/oa04_radiance_unc', 'title': 'oa04_radiance_unc'}, 'oa04_radiance_unc_orphans': {'href': '/quality/orphans/oa04_radiance_unc', 'title': 'oa04_radiance_unc_orphans'}, 'oa05_radiance': {'href': '/measurements/oa05_radiance', 'title': 'oa05_radiance'}, 'oa05_radiance_orphans': {'href': '/measurements/orphans/oa05_radiance', 'title': 'oa05_radiance_orphans'}, 'oa05_radiance_unc': {'href': '/quality/oa05_radiance_unc', 'title': 'oa05_radiance_unc'}, 'oa05_radiance_unc_orphans': {'href': '/quality/orphans/oa05_radiance_unc', 'title': 'oa05_radiance_unc_orphans'}, 'oa06_radiance': {'href': '/measurements/oa06_radiance', 'title': 'oa06_radiance'}, 'oa06_radiance_orphans': {'href': '/measurements/orphans/oa06_radiance', 'title': 'oa06_radiance_orphans'}, 'oa06_radiance_unc': {'href': '/quality/oa06_radiance_unc', 'title': 'oa06_radiance_unc'}, 'oa06_radiance_unc_orphans': {'href': '/quality/orphans/oa06_radiance_unc', 'title': 'oa06_radiance_unc_orphans'}, 'oa07_radiance': {'href': '/measurements/oa07_radiance', 'title': 'oa07_radiance'}, 'oa07_radiance_orphans': {'href': '/measurements/orphans/oa07_radiance', 'title': 'oa07_radiance_orphans'}, 'oa07_radiance_unc': {'href': '/quality/oa07_radiance_unc', 'title': 'oa07_radiance_unc'}, 'oa07_radiance_unc_orphans': {'href': '/quality/orphans/oa07_radiance_unc', 'title': 'oa07_radiance_unc_orphans'}, 'oa08_radiance': {'href': '/measurements/oa08_radiance', 'title': 'oa08_radiance'}, 'oa08_radiance_orphans': {'href': '/measurements/orphans/oa08_radiance', 'title': 'oa08_radiance_orphans'}, 'oa08_radiance_unc': {'href': '/quality/oa08_radiance_unc', 'title': 'oa08_radiance_unc'}, 'oa08_radiance_unc_orphans': {'href': '/quality/orphans/oa08_radiance_unc', 'title': 'oa08_radiance_unc_orphans'}, 'oa09_radiance': {'href': '/measurements/oa09_radiance', 'title': 'oa09_radiance'}, 'oa09_radiance_orphans': {'href': '/measurements/orphans/oa09_radiance', 'title': 'oa09_radiance_orphans'}, 'oa09_radiance_unc': {'href': '/quality/oa09_radiance_unc', 'title': 'oa09_radiance_unc'}, 'oa09_radiance_unc_orphans': {'href': '/quality/orphans/oa09_radiance_unc', 'title': 'oa09_radiance_unc_orphans'}, 'oa10_radiance': {'href': '/measurements/oa10_radiance', 'title': 'oa10_radiance'}, 'oa10_radiance_orphans': {'href': '/measurements/orphans/oa10_radiance', 'title': 'oa10_radiance_orphans'}, 'oa10_radiance_unc': {'href': '/quality/oa10_radiance_unc', 'title': 'oa10_radiance_unc'}, 'oa10_radiance_unc_orphans': {'href': '/quality/orphans/oa10_radiance_unc', 'title': 'oa10_radiance_unc_orphans'}, 'oa11_radiance': {'href': '/measurements/oa11_radiance', 'title': 'oa11_radiance'}, 'oa11_radiance_orphans': {'href': '/measurements/orphans/oa11_radiance', 'title': 'oa11_radiance_orphans'}, 'oa11_radiance_unc': {'href': '/quality/oa11_radiance_unc', 'title': 'oa11_radiance_unc'}, 'oa11_radiance_unc_orphans': {'href': '/quality/orphans/oa11_radiance_unc', 'title': 'oa11_radiance_unc_orphans'}, 'oa12_radiance': {'href': '/measurements/oa12_radiance', 'title': 'oa12_radiance'}, 'oa12_radiance_orphans': {'href': '/measurements/orphans/oa12_radiance', 'title': 'oa12_radiance_orphans'}, 'oa12_radiance_unc': {'href': '/quality/oa12_radiance_unc', 'title': 'oa12_radiance_unc'}, 'oa12_radiance_unc_orphans': {'href': '/quality/orphans/oa12_radiance_unc', 'title': 'oa12_radiance_unc_orphans'}, 'oa13_radiance': {'href': '/measurements/oa13_radiance', 'title': 'oa13_radiance'}, 'oa13_radiance_orphans': {'href': '/measurements/orphans/oa13_radiance', 'title': 'oa13_radiance_orphans'}, 'oa13_radiance_unc': {'href': '/quality/oa13_radiance_unc', 'title': 'oa13_radiance_unc'}, 'oa13_radiance_unc_orphans': {'href': '/quality/orphans/oa13_radiance_unc', 'title': 'oa13_radiance_unc_orphans'}, 'oa14_radiance': {'href': '/measurements/oa14_radiance', 'title': 'oa14_radiance'}, 'oa14_radiance_orphans': {'href': '/measurements/orphans/oa14_radiance', 'title': 'oa14_radiance_orphans'}, 'oa14_radiance_unc': {'href': '/quality/oa14_radiance_unc', 'title': 'oa14_radiance_unc'}, 'oa14_radiance_unc_orphans': {'href': '/quality/orphans/oa14_radiance_unc', 'title': 'oa14_radiance_unc_orphans'}, 'oa15_radiance': {'href': '/measurements/oa15_radiance', 'title': 'oa15_radiance'}, 'oa15_radiance_orphans': {'href': '/measurements/orphans/oa15_radiance', 'title': 'oa15_radiance_orphans'}, 'oa15_radiance_unc': {'href': '/quality/oa15_radiance_unc', 'title': 'oa15_radiance_unc'}, 'oa15_radiance_unc_orphans': {'href': '/quality/orphans/oa15_radiance_unc', 'title': 'oa15_radiance_unc_orphans'}, 'oa16_radiance': {'href': '/measurements/oa16_radiance', 'title': 'oa16_radiance'}, 'oa16_radiance_orphans': {'href': '/measurements/orphans/oa16_radiance', 'title': 'oa16_radiance_orphans'}, 'oa16_radiance_unc': {'href': '/quality/oa16_radiance_unc', 'title': 'oa16_radiance_unc'}, 'oa16_radiance_unc_orphans': {'href': '/quality/orphans/oa16_radiance_unc', 'title': 'oa16_radiance_unc_orphans'}, 'oa17_radiance': {'href': '/measurements/oa17_radiance', 'title': 'oa17_radiance'}, 'oa17_radiance_orphans': {'href': '/measurements/orphans/oa17_radiance', 'title': 'oa17_radiance_orphans'}, 'oa17_radiance_unc': {'href': '/quality/oa17_radiance_unc', 'title': 'oa17_radiance_unc'}, 'oa17_radiance_unc_orphans': {'href': '/quality/orphans/oa17_radiance_unc', 'title': 'oa17_radiance_unc_orphans'}, 'oa18_radiance': {'href': '/measurements/oa18_radiance', 'title': 'oa18_radiance'}, 'oa18_radiance_orphans': {'href': '/measurements/orphans/oa18_radiance', 'title': 'oa18_radiance_orphans'}, 'oa18_radiance_unc': {'href': '/quality/oa18_radiance_unc', 'title': 'oa18_radiance_unc'}, 'oa18_radiance_unc_orphans': {'href': '/quality/orphans/oa18_radiance_unc', 'title': 'oa18_radiance_unc_orphans'}, 'oa19_radiance': {'href': '/measurements/oa19_radiance', 'title': 'oa19_radiance'}, 'oa19_radiance_orphans': {'href': '/measurements/orphans/oa19_radiance', 'title': 'oa19_radiance_orphans'}, 'oa19_radiance_unc': {'href': '/quality/oa19_radiance_unc', 'title': 'oa19_radiance_unc'}, 'oa19_radiance_unc_orphans': {'href': '/quality/orphans/oa19_radiance_unc', 'title': 'oa19_radiance_unc_orphans'}, 'oa20_radiance': {'href': '/measurements/oa20_radiance', 'title': 'oa20_radiance'}, 'oa20_radiance_orphans': {'href': '/measurements/orphans/oa20_radiance', 'title': 'oa20_radiance_orphans'}, 'oa20_radiance_unc': {'href': '/quality/oa20_radiance_unc', 'title': 'oa20_radiance_unc'}, 'oa20_radiance_unc_orphans': {'href': '/quality/orphans/oa20_radiance_unc', 'title': 'oa20_radiance_unc_orphans'}, 'oa21_radiance': {'href': '/measurements/oa21_radiance', 'title': 'oa21_radiance'}, 'oa21_radiance_orphans': {'href': '/measurements/orphans/oa21_radiance', 'title': 'oa21_radiance_orphans'}, 'oa21_radiance_unc': {'href': '/quality/oa21_radiance_unc', 'title': 'oa21_radiance_unc'}, 'oa21_radiance_unc_orphans': {'href': '/quality/orphans/oa21_radiance_unc', 'title': 'oa21_radiance_unc_orphans'}, 'oaa': {'href': '/conditions/geometry/oaa', 'title': 'oaa'}, 'oza': {'href': '/conditions/geometry/oza', 'title': 'oza'}, 'quality_flags': {'href': '/quality/quality_flags', 'title': 'quality_flags'}, 'quality_flags_orphans': {'href': '/quality/orphans/quality_flags', 'title': 'quality_flags_orphans'}, 'relative_spectral_covariance': {'href': '/conditions/instrument/relative_spectral_covariance', 'title': 'relative_spectral_covariance'}, 'saa': {'href': '/conditions/geometry/saa', 'title': 'saa'}, 'sea_level_pressure': {'href': '/conditions/meteorology/sea_level_pressure', 'title': 'sea_level_pressure'}, 'solar_flux': {'href': '/conditions/instrument/solar_flux', 'title': 'solar_flux'}, 'sza': {'href': '/conditions/geometry/sza', 'title': 'sza'}, 'sza_orphans': {'href': '/conditions/orphans/sza', 'title': 'sza_orphans'}, 'total_columnar_water_vapour': {'href': '/conditions/meteorology/total_columnar_water_vapour', 'title': 'total_columnar_water_vapour'}, 'total_ozone': {'href': '/conditions/meteorology/total_ozone', 'title': 'total_ozone'}}, 'bbox': [15.2194, 39.5543, -4.7861, 52.4622], 'collection': 'OL__L1_.004.01.00', 'geometry': {'coordinates': [[[-4.7861, 41.9789], [-3.93914, 41.902], [-3.11661, 41.8194], [-2.29536, 41.7309], [-1.47832, 41.6368], [-0.666506, 41.54], [0.148404, 41.4342], [0.963169, 41.3217], [1.76618, 41.2051], [2.5758, 41.0815], [3.38023, 40.9537], [4.18015, 40.8197], [4.97545, 40.6805], [5.77131, 40.5359], [6.56315, 40.3853], [7.35038, 40.2295], [8.13158, 40.0685], [8.9109, 39.9022], [9.688, 39.7302], [10.4609, 39.5543], [11.509, 42.152], [12.6418, 44.7451], [13.8723, 47.327], [15.2194, 49.8949], [14.3049, 50.0997], [13.3893, 50.2972], [12.4644, 50.4876], [11.5335, 50.6708], [10.5952, 50.8461], [9.64863, 51.0141], [8.69541, 51.1736], [7.73658, 51.326], [6.76786, 51.4711], [5.79814, 51.6067], [4.82094, 51.7355], [3.83576, 51.8565], [2.85121, 51.969], [1.85279, 52.0735], [0.863084, 52.1658], [-0.141309, 52.2533], [-1.14494, 52.332], [-2.14863, 52.4019], [-3.16831, 52.4622], [-3.56442, 49.8426], [-3.96588, 47.2211], [-4.37342, 44.5983], [-4.7861, 41.9789]]], 'type': 'Polygon'}, 'id': 'S03OLCEFR_20260313T100048_0180_B350_S633', 'links': [{'href': './.zattrs.json', 'rel': 'collection', 'type': 'application/json'}], 'properties': {'bands': [{'eo:center_wavelength': 0.4, 'eo:full_width_half_max': 0.015, 'name': 'oa01'}, {'eo:center_wavelength': 0.41250000000000003, 'eo:full_width_half_max': 0.01, 'name': 'oa02'}, {'eo:center_wavelength': 0.4425, 'eo:full_width_half_max': 0.01, 'name': 'oa03'}, {'eo:center_wavelength': 0.49, 'eo:common_name': 'blue', 'eo:full_width_half_max': 0.01, 'name': 'oa04'}, {'eo:center_wavelength': 0.51, 'eo:full_width_half_max': 0.01, 'name': 'oa05'}, {'eo:center_wavelength': 0.56, 'eo:common_name': 'green', 'eo:full_width_half_max': 0.01, 'name': 'oa06'}, {'eo:center_wavelength': 0.62, 'eo:common_name': 'yellow', 'eo:full_width_half_max': 0.01, 'name': 'oa07'}, {'eo:center_wavelength': 0.665, 'eo:full_width_half_max': 0.01, 'name': 'oa08'}, {'eo:center_wavelength': 0.67375, 'eo:full_width_half_max': 0.0075, 'name': 'oa09'}, {'eo:center_wavelength': 0.68125, 'eo:full_width_half_max': 0.0075, 'name': 'oa10'}, {'eo:center_wavelength': 0.70875, 'eo:full_width_half_max': 0.01, 'name': 'oa11'}, {'eo:center_wavelength': 0.75375, 'eo:full_width_half_max': 0.0075, 'name': 'oa12'}, {'eo:center_wavelength': 0.76125, 'eo:full_width_half_max': 0.0025, 'name': 'oa13'}, {'eo:center_wavelength': 0.764375, 'eo:full_width_half_max': 0.00375, 'name': 'oa14'}, {'eo:center_wavelength': 0.7675000000000001, 'eo:full_width_half_max': 0.0025, 'name': 'oa15'}, {'eo:center_wavelength': 0.77875, 'eo:full_width_half_max': 0.015, 'name': 'oa16'}, {'eo:center_wavelength': 0.865, 'eo:full_width_half_max': 0.02, 'name': 'oa17'}, {'eo:center_wavelength': 0.885, 'eo:full_width_half_max': 0.01, 'name': 'oa18'}, {'eo:center_wavelength': 0.9, 'eo:full_width_half_max': 0.01, 'name': 'oa19'}, {'eo:center_wavelength': 0.9400000000000001, 'eo:full_width_half_max': 0.02, 'name': 'oa20'}, {'eo:center_wavelength': 1.02, 'eo:full_width_half_max': 0.04, 'name': 'oa21'}], 'classification:classes': [{'count': 742117, 'name': 'invalid', 'percentage': 4.0}, {'count': 4091, 'name': 'cosmetic', 'percentage': 0.0}, {'count': 4489249, 'name': 'duplicated', 'percentage': 23.0}, {'count': 2, 'name': 'saturated', 'percentage': 0.0}, {'count': 11, 'name': 'dubious', 'percentage': 0.0}, {'name': 'saline_water', 'percentage': 26.0}, {'name': 'bright', 'percentage': 46.0}, {'name': 'coastal', 'percentage': 0.0}, {'name': 'fresh_water', 'percentage': 1.0}, {'name': 'tidal', 'percentage': 2.0}], 'constellation': 'sentinel-3', 'created': '2026-03-14T10:08:20Z', 'datetime': '2026-03-13T10:02:18.256763Z', 'end_datetime': '2026-03-13T10:03:48.256763Z', 'eopf:instrument_mode': 'Earth Observation', 'gsd': 270, 'instruments': ['olci'], 'mission': 'copernicus', 'platform': 'sentinel-3b', 'processing:facility': 'OPE', 'processing:level': 'L1', 'processing:lineage': 'systematic', 'processing:software': {'PUG': '03.50'}, 'processing:version': 'TODO', 'product:timeliness': 'P1M', 'product:timeliness_category': 'NT', 'product:type': 'S03OLCEFR', 'proj:shape': [{'columns': 4865, 'name': 'FR', 'rows': 4091}], 'providers': [{'name': 'Copernicus Ground Segment', 'roles': ['processor']}, {'name': 'ESA', 'roles': ['producer']}], 'sat:absolute_orbit': 41047, 'sat:orbit_state': 'descending', 'sat:platform_international_designator': '2018-039A', 'sat:relative_orbit': 350, 'start_datetime': '2026-03-13T10:00:48.256763Z'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.2.0/schema.json', 'https://stac-extensions.github.io/classification/v2.0.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/sat/v1.1.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.2.0/schema.json', 'https://stac-extensions.github.io/projection/v2.0.0/schema.json', 'https://stac-extensions.github.io/product/v0.1.0/schema.json'], 'stac_version': '1.1.0', 'type': 'Feature'}

The separator character used in flattened variable names can be customized via the group_sep parameter. Additionally, you can filter the returned variables using the variables keyword argument, which accepts a string, an iterable of names, or a regular expression (regex) pattern.

In [10]:

ds = xr.open_dataset(
    item.assets["product"].href,
    engine="eopf-zarr",
    op_mode="native",
    chunks={},
    group_sep="/",
    variables="measurements*.",
)
ds

ds = xr.open_dataset( item.assets["product"].href, engine="eopf-zarr", op_mode="native", chunks={}, group_sep="/", variables="measurements*.", ) ds

Out[10]:

<xarray.Dataset> Size: 3GB
Dimensions:                     (measurements/rows: 4091,
                                 measurements/columns: 4865)
Dimensions without coordinates: measurements/rows, measurements/columns
Data variables: (12/21)
    measurements/oa01_radiance  (measurements/rows, measurements/columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    measurements/oa02_radiance  (measurements/rows, measurements/columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    measurements/oa03_radiance  (measurements/rows, measurements/columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    measurements/oa04_radiance  (measurements/rows, measurements/columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    measurements/oa05_radiance  (measurements/rows, measurements/columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    measurements/oa06_radiance  (measurements/rows, measurements/columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    ...                          ...
    measurements/oa16_radiance  (measurements/rows, measurements/columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    measurements/oa17_radiance  (measurements/rows, measurements/columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    measurements/oa18_radiance  (measurements/rows, measurements/columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    measurements/oa19_radiance  (measurements/rows, measurements/columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    measurements/oa20_radiance  (measurements/rows, measurements/columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    measurements/oa21_radiance  (measurements/rows, measurements/columns) float64 159MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
Attributes: (3)

xarray.Dataset

• Dimensions:
  • measurements/rows: 4091
  • measurements/columns: 4865
• Coordinates: (0)
• Data variables: (21)
  • measurements/oa01_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 01', 'scale_factor': 0.013634907081723213, 'short_name': 'oa01_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 01

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa02_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 02', 'scale_factor': 0.011490973643958569, 'short_name': 'oa02_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 02

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa03_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 03', 'scale_factor': 0.012588496319949627, 'short_name': 'oa03_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 03

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa04_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 04', 'scale_factor': 0.011396283283829689, 'short_name': 'oa04_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 04

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa05_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 05', 'scale_factor': 0.012233924120664597, 'short_name': 'oa05_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 05

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa06_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 06', 'scale_factor': 0.010038239881396294, 'short_name': 'oa06_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 06

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa07_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 07', 'scale_factor': 0.01056649163365364, 'short_name': 'oa07_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 07

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa08_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 08', 'scale_factor': 0.008974685333669186, 'short_name': 'oa08_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 08

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa09_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 09', 'scale_factor': 0.009690877981483936, 'short_name': 'oa09_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 09

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa10_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 10', 'scale_factor': 0.008986380882561207, 'short_name': 'oa10_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 10

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa11_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 11', 'scale_factor': 0.008361034095287323, 'short_name': 'oa11_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 11

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa12_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 12', 'scale_factor': 0.007491027936339378, 'short_name': 'oa12_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 12

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa13_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 13', 'scale_factor': 0.007861301302909851, 'short_name': 'oa13_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 13

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa14_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 14', 'scale_factor': 0.007529641035944223, 'short_name': 'oa14_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 14

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa15_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 15', 'scale_factor': 0.00788309145718813, 'short_name': 'oa15_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 15

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa16_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 16', 'scale_factor': 0.007152478210628033, 'short_name': 'oa16_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 16

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa17_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 17', 'scale_factor': 0.006055756937712431, 'short_name': 'oa17_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 17

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa18_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 18', 'scale_factor': 0.0060341269709169865, 'short_name': 'oa18_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 18

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa19_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 19', 'scale_factor': 0.0055409627966582775, 'short_name': 'oa19_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 19

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa20_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 20', 'scale_factor': 0.005095385946333408, 'short_name': 'oa20_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 20

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • measurements/oa21_radiance
    (measurements/rows, measurements/columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'altitude', 'time_stamp'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 21', 'scale_factor': 0.004291551187634468, 'short_name': 'oa21_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 21

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 151.85 MiB 8.00 MiB Shape (4091, 4865) (1024, 1024) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (0)
• Attributes: (3)

  other_metadata :

  {'absolute_pass_number': 82094, 'cycle_number': 117, 'data_information': {'accuracy_reference': '0', 'earth_sun_distance': 148692226821, 'measurement_accuracy': '2.000000', 'ocl_status': 'true', 'sampling': {'across_track_distance': 270, 'along_track_distance': 294, 'along_track_time': 44002, 'columns_per_tiepoint': 64, 'rows_per_tiepoint': 1}}, 'eopf_category': 'eoproduct', 'ephemeris': {'start': {'TAI': '2026-03-13T09:25:25.256763', 'UT1': '2026-03-13T09:24:48.319326', 'UTC': '2026-03-13T09:24:48.256763Z', 'position': {'x': -7094686.732, 'y': -1129221.277, 'z': 0.001}, 'velocity': {'x': -249.56373, 'y': 1621.140793, 'z': 7366.211176}}, 'stop': {'TAI': '2026-03-13T11:06:24.413833', 'UT1': '2026-03-13T11:05:47.476392', 'UTC': '2026-03-13T11:05:47.413833Z', 'position': {'x': -6898641.864, 'y': 2004695.114, 'z': 0.003}, 'velocity': {'x': 465.705986, 'y': 1572.789106, 'z': 7366.200009}}}, 'meteo': {'missing_time': 0, 'source': 'ECMWF', 'type': 'ANALYSIS'}, 'phase_identifier': 4, 'product_unit': {'alongtrack_coordinate': 2160, 'duration': 180, 'type': 'FRAME'}, 'relative_pass_number': 700}

  processing_history :

  {'Level-0 Product': [{'adfs': ['S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_TM_0_NAT__G_20260310T192718_20260310T210759_20260310T211703_6041______________SVL_O_NR_OPE.ISIP', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_TM_0_NAT____20260310T192700_20260310T210741_20260310T211958_6041_117_313______ESA_O_AL_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-10T21:20:06.608260Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_TM_0_NAT__G_20260310T192718_20260310T210759_20260310T211703_6041______________SVL_O_NR_OPE.ISIP', 'S3IPF PDS 001 - 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  stac_discovery :

  {'assets': {'atmospheric_temperature_profile': {'href': '/conditions/meteorology/atmospheric_temperature_profile', 'title': 'atmospheric_temperature_profile'}, 'detector_index': {'href': '/conditions/image/detector_index', 'title': 'detector_index'}, 'detector_index_orphans': {'href': '/conditions/orphans/detector_index', 'title': 'detector_index_orphans'}, 'frame_offset': {'href': '/conditions/image/frame_offset', 'title': 'frame_offset'}, 'frame_offset_orphans': {'href': '/conditions/orphans/frame_offset', 'title': 'frame_offset_orphans'}, 'fwhm': {'href': '/conditions/instrument/fwhm', 'title': 'fwhm'}, 'horizontal_wind': {'href': '/conditions/meteorology/horizontal_wind', 'title': 'horizontal_wind'}, 'humidity': {'href': '/conditions/meteorology/humidity', 'title': 'humidity'}, 'lambda0': {'href': '/conditions/instrument/lambda0', 'title': 'lambda0'}, 'nb_removed_pixels': {'href': '/conditions/orphans/nb_removed_pixels', 'title': 'nb_removed_pixels'}, 'oa01_radiance': {'href': '/measurements/oa01_radiance', 'title': 'oa01_radiance'}, 'oa01_radiance_orphans': {'href': '/measurements/orphans/oa01_radiance', 'title': 'oa01_radiance_orphans'}, 'oa01_radiance_unc': {'href': '/quality/oa01_radiance_unc', 'title': 'oa01_radiance_unc'}, 'oa01_radiance_unc_orphans': {'href': '/quality/orphans/oa01_radiance_unc', 'title': 'oa01_radiance_unc_orphans'}, 'oa02_radiance': {'href': '/measurements/oa02_radiance', 'title': 'oa02_radiance'}, 'oa02_radiance_orphans': {'href': '/measurements/orphans/oa02_radiance', 'title': 'oa02_radiance_orphans'}, 'oa02_radiance_unc': {'href': '/quality/oa02_radiance_unc', 'title': 'oa02_radiance_unc'}, 'oa02_radiance_unc_orphans': {'href': '/quality/orphans/oa02_radiance_unc', 'title': 'oa02_radiance_unc_orphans'}, 'oa03_radiance': {'href': '/measurements/oa03_radiance', 'title': 'oa03_radiance'}, 'oa03_radiance_orphans': {'href': '/measurements/orphans/oa03_radiance', 'title': 'oa03_radiance_orphans'}, 'oa03_radiance_unc': {'href': '/quality/oa03_radiance_unc', 'title': 'oa03_radiance_unc'}, 'oa03_radiance_unc_orphans': {'href': '/quality/orphans/oa03_radiance_unc', 'title': 'oa03_radiance_unc_orphans'}, 'oa04_radiance': {'href': '/measurements/oa04_radiance', 'title': 'oa04_radiance'}, 'oa04_radiance_orphans': {'href': '/measurements/orphans/oa04_radiance', 'title': 'oa04_radiance_orphans'}, 'oa04_radiance_unc': {'href': '/quality/oa04_radiance_unc', 'title': 'oa04_radiance_unc'}, 'oa04_radiance_unc_orphans': {'href': '/quality/orphans/oa04_radiance_unc', 'title': 'oa04_radiance_unc_orphans'}, 'oa05_radiance': {'href': '/measurements/oa05_radiance', 'title': 'oa05_radiance'}, 'oa05_radiance_orphans': {'href': '/measurements/orphans/oa05_radiance', 'title': 'oa05_radiance_orphans'}, 'oa05_radiance_unc': {'href': '/quality/oa05_radiance_unc', 'title': 'oa05_radiance_unc'}, 'oa05_radiance_unc_orphans': {'href': '/quality/orphans/oa05_radiance_unc', 'title': 'oa05_radiance_unc_orphans'}, 'oa06_radiance': {'href': '/measurements/oa06_radiance', 'title': 'oa06_radiance'}, 'oa06_radiance_orphans': {'href': '/measurements/orphans/oa06_radiance', 'title': 'oa06_radiance_orphans'}, 'oa06_radiance_unc': {'href': '/quality/oa06_radiance_unc', 'title': 'oa06_radiance_unc'}, 'oa06_radiance_unc_orphans': {'href': '/quality/orphans/oa06_radiance_unc', 'title': 'oa06_radiance_unc_orphans'}, 'oa07_radiance': {'href': '/measurements/oa07_radiance', 'title': 'oa07_radiance'}, 'oa07_radiance_orphans': {'href': '/measurements/orphans/oa07_radiance', 'title': 'oa07_radiance_orphans'}, 'oa07_radiance_unc': {'href': '/quality/oa07_radiance_unc', 'title': 'oa07_radiance_unc'}, 'oa07_radiance_unc_orphans': {'href': '/quality/orphans/oa07_radiance_unc', 'title': 'oa07_radiance_unc_orphans'}, 'oa08_radiance': {'href': '/measurements/oa08_radiance', 'title': 'oa08_radiance'}, 'oa08_radiance_orphans': {'href': '/measurements/orphans/oa08_radiance', 'title': 'oa08_radiance_orphans'}, 'oa08_radiance_unc': {'href': '/quality/oa08_radiance_unc', 'title': 'oa08_radiance_unc'}, 'oa08_radiance_unc_orphans': {'href': '/quality/orphans/oa08_radiance_unc', 'title': 'oa08_radiance_unc_orphans'}, 'oa09_radiance': {'href': '/measurements/oa09_radiance', 'title': 'oa09_radiance'}, 'oa09_radiance_orphans': {'href': '/measurements/orphans/oa09_radiance', 'title': 'oa09_radiance_orphans'}, 'oa09_radiance_unc': {'href': '/quality/oa09_radiance_unc', 'title': 'oa09_radiance_unc'}, 'oa09_radiance_unc_orphans': {'href': '/quality/orphans/oa09_radiance_unc', 'title': 'oa09_radiance_unc_orphans'}, 'oa10_radiance': {'href': '/measurements/oa10_radiance', 'title': 'oa10_radiance'}, 'oa10_radiance_orphans': {'href': '/measurements/orphans/oa10_radiance', 'title': 'oa10_radiance_orphans'}, 'oa10_radiance_unc': {'href': '/quality/oa10_radiance_unc', 'title': 'oa10_radiance_unc'}, 'oa10_radiance_unc_orphans': {'href': '/quality/orphans/oa10_radiance_unc', 'title': 'oa10_radiance_unc_orphans'}, 'oa11_radiance': {'href': '/measurements/oa11_radiance', 'title': 'oa11_radiance'}, 'oa11_radiance_orphans': {'href': '/measurements/orphans/oa11_radiance', 'title': 'oa11_radiance_orphans'}, 'oa11_radiance_unc': {'href': '/quality/oa11_radiance_unc', 'title': 'oa11_radiance_unc'}, 'oa11_radiance_unc_orphans': {'href': '/quality/orphans/oa11_radiance_unc', 'title': 'oa11_radiance_unc_orphans'}, 'oa12_radiance': {'href': '/measurements/oa12_radiance', 'title': 'oa12_radiance'}, 'oa12_radiance_orphans': {'href': '/measurements/orphans/oa12_radiance', 'title': 'oa12_radiance_orphans'}, 'oa12_radiance_unc': {'href': '/quality/oa12_radiance_unc', 'title': 'oa12_radiance_unc'}, 'oa12_radiance_unc_orphans': {'href': '/quality/orphans/oa12_radiance_unc', 'title': 'oa12_radiance_unc_orphans'}, 'oa13_radiance': {'href': '/measurements/oa13_radiance', 'title': 'oa13_radiance'}, 'oa13_radiance_orphans': {'href': '/measurements/orphans/oa13_radiance', 'title': 'oa13_radiance_orphans'}, 'oa13_radiance_unc': {'href': '/quality/oa13_radiance_unc', 'title': 'oa13_radiance_unc'}, 'oa13_radiance_unc_orphans': {'href': '/quality/orphans/oa13_radiance_unc', 'title': 'oa13_radiance_unc_orphans'}, 'oa14_radiance': {'href': '/measurements/oa14_radiance', 'title': 'oa14_radiance'}, 'oa14_radiance_orphans': {'href': '/measurements/orphans/oa14_radiance', 'title': 'oa14_radiance_orphans'}, 'oa14_radiance_unc': {'href': '/quality/oa14_radiance_unc', 'title': 'oa14_radiance_unc'}, 'oa14_radiance_unc_orphans': {'href': '/quality/orphans/oa14_radiance_unc', 'title': 'oa14_radiance_unc_orphans'}, 'oa15_radiance': {'href': '/measurements/oa15_radiance', 'title': 'oa15_radiance'}, 'oa15_radiance_orphans': {'href': '/measurements/orphans/oa15_radiance', 'title': 'oa15_radiance_orphans'}, 'oa15_radiance_unc': {'href': '/quality/oa15_radiance_unc', 'title': 'oa15_radiance_unc'}, 'oa15_radiance_unc_orphans': {'href': '/quality/orphans/oa15_radiance_unc', 'title': 'oa15_radiance_unc_orphans'}, 'oa16_radiance': {'href': '/measurements/oa16_radiance', 'title': 'oa16_radiance'}, 'oa16_radiance_orphans': {'href': '/measurements/orphans/oa16_radiance', 'title': 'oa16_radiance_orphans'}, 'oa16_radiance_unc': {'href': '/quality/oa16_radiance_unc', 'title': 'oa16_radiance_unc'}, 'oa16_radiance_unc_orphans': {'href': '/quality/orphans/oa16_radiance_unc', 'title': 'oa16_radiance_unc_orphans'}, 'oa17_radiance': {'href': '/measurements/oa17_radiance', 'title': 'oa17_radiance'}, 'oa17_radiance_orphans': {'href': '/measurements/orphans/oa17_radiance', 'title': 'oa17_radiance_orphans'}, 'oa17_radiance_unc': {'href': '/quality/oa17_radiance_unc', 'title': 'oa17_radiance_unc'}, 'oa17_radiance_unc_orphans': {'href': '/quality/orphans/oa17_radiance_unc', 'title': 'oa17_radiance_unc_orphans'}, 'oa18_radiance': {'href': '/measurements/oa18_radiance', 'title': 'oa18_radiance'}, 'oa18_radiance_orphans': {'href': '/measurements/orphans/oa18_radiance', 'title': 'oa18_radiance_orphans'}, 'oa18_radiance_unc': {'href': '/quality/oa18_radiance_unc', 'title': 'oa18_radiance_unc'}, 'oa18_radiance_unc_orphans': {'href': '/quality/orphans/oa18_radiance_unc', 'title': 'oa18_radiance_unc_orphans'}, 'oa19_radiance': {'href': '/measurements/oa19_radiance', 'title': 'oa19_radiance'}, 'oa19_radiance_orphans': {'href': '/measurements/orphans/oa19_radiance', 'title': 'oa19_radiance_orphans'}, 'oa19_radiance_unc': {'href': '/quality/oa19_radiance_unc', 'title': 'oa19_radiance_unc'}, 'oa19_radiance_unc_orphans': {'href': '/quality/orphans/oa19_radiance_unc', 'title': 'oa19_radiance_unc_orphans'}, 'oa20_radiance': {'href': '/measurements/oa20_radiance', 'title': 'oa20_radiance'}, 'oa20_radiance_orphans': {'href': '/measurements/orphans/oa20_radiance', 'title': 'oa20_radiance_orphans'}, 'oa20_radiance_unc': {'href': '/quality/oa20_radiance_unc', 'title': 'oa20_radiance_unc'}, 'oa20_radiance_unc_orphans': {'href': '/quality/orphans/oa20_radiance_unc', 'title': 'oa20_radiance_unc_orphans'}, 'oa21_radiance': {'href': '/measurements/oa21_radiance', 'title': 'oa21_radiance'}, 'oa21_radiance_orphans': {'href': '/measurements/orphans/oa21_radiance', 'title': 'oa21_radiance_orphans'}, 'oa21_radiance_unc': {'href': '/quality/oa21_radiance_unc', 'title': 'oa21_radiance_unc'}, 'oa21_radiance_unc_orphans': {'href': '/quality/orphans/oa21_radiance_unc', 'title': 'oa21_radiance_unc_orphans'}, 'oaa': {'href': '/conditions/geometry/oaa', 'title': 'oaa'}, 'oza': {'href': '/conditions/geometry/oza', 'title': 'oza'}, 'quality_flags': {'href': '/quality/quality_flags', 'title': 'quality_flags'}, 'quality_flags_orphans': {'href': '/quality/orphans/quality_flags', 'title': 'quality_flags_orphans'}, 'relative_spectral_covariance': {'href': '/conditions/instrument/relative_spectral_covariance', 'title': 'relative_spectral_covariance'}, 'saa': {'href': '/conditions/geometry/saa', 'title': 'saa'}, 'sea_level_pressure': {'href': '/conditions/meteorology/sea_level_pressure', 'title': 'sea_level_pressure'}, 'solar_flux': {'href': '/conditions/instrument/solar_flux', 'title': 'solar_flux'}, 'sza': {'href': '/conditions/geometry/sza', 'title': 'sza'}, 'sza_orphans': {'href': '/conditions/orphans/sza', 'title': 'sza_orphans'}, 'total_columnar_water_vapour': {'href': '/conditions/meteorology/total_columnar_water_vapour', 'title': 'total_columnar_water_vapour'}, 'total_ozone': {'href': '/conditions/meteorology/total_ozone', 'title': 'total_ozone'}}, 'bbox': [15.2194, 39.5543, -4.7861, 52.4622], 'collection': 'OL__L1_.004.01.00', 'geometry': {'coordinates': [[[-4.7861, 41.9789], [-3.93914, 41.902], [-3.11661, 41.8194], [-2.29536, 41.7309], [-1.47832, 41.6368], [-0.666506, 41.54], [0.148404, 41.4342], [0.963169, 41.3217], [1.76618, 41.2051], [2.5758, 41.0815], [3.38023, 40.9537], [4.18015, 40.8197], [4.97545, 40.6805], [5.77131, 40.5359], [6.56315, 40.3853], [7.35038, 40.2295], [8.13158, 40.0685], [8.9109, 39.9022], [9.688, 39.7302], [10.4609, 39.5543], [11.509, 42.152], [12.6418, 44.7451], [13.8723, 47.327], [15.2194, 49.8949], [14.3049, 50.0997], [13.3893, 50.2972], [12.4644, 50.4876], [11.5335, 50.6708], [10.5952, 50.8461], [9.64863, 51.0141], [8.69541, 51.1736], [7.73658, 51.326], [6.76786, 51.4711], [5.79814, 51.6067], [4.82094, 51.7355], [3.83576, 51.8565], [2.85121, 51.969], [1.85279, 52.0735], [0.863084, 52.1658], [-0.141309, 52.2533], [-1.14494, 52.332], [-2.14863, 52.4019], [-3.16831, 52.4622], [-3.56442, 49.8426], [-3.96588, 47.2211], [-4.37342, 44.5983], [-4.7861, 41.9789]]], 'type': 'Polygon'}, 'id': 'S03OLCEFR_20260313T100048_0180_B350_S633', 'links': [{'href': './.zattrs.json', 'rel': 'collection', 'type': 'application/json'}], 'properties': {'bands': [{'eo:center_wavelength': 0.4, 'eo:full_width_half_max': 0.015, 'name': 'oa01'}, {'eo:center_wavelength': 0.41250000000000003, 'eo:full_width_half_max': 0.01, 'name': 'oa02'}, {'eo:center_wavelength': 0.4425, 'eo:full_width_half_max': 0.01, 'name': 'oa03'}, {'eo:center_wavelength': 0.49, 'eo:common_name': 'blue', 'eo:full_width_half_max': 0.01, 'name': 'oa04'}, {'eo:center_wavelength': 0.51, 'eo:full_width_half_max': 0.01, 'name': 'oa05'}, {'eo:center_wavelength': 0.56, 'eo:common_name': 'green', 'eo:full_width_half_max': 0.01, 'name': 'oa06'}, {'eo:center_wavelength': 0.62, 'eo:common_name': 'yellow', 'eo:full_width_half_max': 0.01, 'name': 'oa07'}, {'eo:center_wavelength': 0.665, 'eo:full_width_half_max': 0.01, 'name': 'oa08'}, {'eo:center_wavelength': 0.67375, 'eo:full_width_half_max': 0.0075, 'name': 'oa09'}, {'eo:center_wavelength': 0.68125, 'eo:full_width_half_max': 0.0075, 'name': 'oa10'}, {'eo:center_wavelength': 0.70875, 'eo:full_width_half_max': 0.01, 'name': 'oa11'}, {'eo:center_wavelength': 0.75375, 'eo:full_width_half_max': 0.0075, 'name': 'oa12'}, {'eo:center_wavelength': 0.76125, 'eo:full_width_half_max': 0.0025, 'name': 'oa13'}, {'eo:center_wavelength': 0.764375, 'eo:full_width_half_max': 0.00375, 'name': 'oa14'}, {'eo:center_wavelength': 0.7675000000000001, 'eo:full_width_half_max': 0.0025, 'name': 'oa15'}, {'eo:center_wavelength': 0.77875, 'eo:full_width_half_max': 0.015, 'name': 'oa16'}, {'eo:center_wavelength': 0.865, 'eo:full_width_half_max': 0.02, 'name': 'oa17'}, {'eo:center_wavelength': 0.885, 'eo:full_width_half_max': 0.01, 'name': 'oa18'}, {'eo:center_wavelength': 0.9, 'eo:full_width_half_max': 0.01, 'name': 'oa19'}, {'eo:center_wavelength': 0.9400000000000001, 'eo:full_width_half_max': 0.02, 'name': 'oa20'}, {'eo:center_wavelength': 1.02, 'eo:full_width_half_max': 0.04, 'name': 'oa21'}], 'classification:classes': [{'count': 742117, 'name': 'invalid', 'percentage': 4.0}, {'count': 4091, 'name': 'cosmetic', 'percentage': 0.0}, {'count': 4489249, 'name': 'duplicated', 'percentage': 23.0}, {'count': 2, 'name': 'saturated', 'percentage': 0.0}, {'count': 11, 'name': 'dubious', 'percentage': 0.0}, {'name': 'saline_water', 'percentage': 26.0}, {'name': 'bright', 'percentage': 46.0}, {'name': 'coastal', 'percentage': 0.0}, {'name': 'fresh_water', 'percentage': 1.0}, {'name': 'tidal', 'percentage': 2.0}], 'constellation': 'sentinel-3', 'created': '2026-03-14T10:08:20Z', 'datetime': '2026-03-13T10:02:18.256763Z', 'end_datetime': '2026-03-13T10:03:48.256763Z', 'eopf:instrument_mode': 'Earth Observation', 'gsd': 270, 'instruments': ['olci'], 'mission': 'copernicus', 'platform': 'sentinel-3b', 'processing:facility': 'OPE', 'processing:level': 'L1', 'processing:lineage': 'systematic', 'processing:software': {'PUG': '03.50'}, 'processing:version': 'TODO', 'product:timeliness': 'P1M', 'product:timeliness_category': 'NT', 'product:type': 'S03OLCEFR', 'proj:shape': [{'columns': 4865, 'name': 'FR', 'rows': 4091}], 'providers': [{'name': 'Copernicus Ground Segment', 'roles': ['processor']}, {'name': 'ESA', 'roles': ['producer']}], 'sat:absolute_orbit': 41047, 'sat:orbit_state': 'descending', 'sat:platform_international_designator': '2018-039A', 'sat:relative_orbit': 350, 'start_datetime': '2026-03-13T10:00:48.256763Z'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.2.0/schema.json', 'https://stac-extensions.github.io/classification/v2.0.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/sat/v1.1.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.2.0/schema.json', 'https://stac-extensions.github.io/projection/v2.0.0/schema.json', 'https://stac-extensions.github.io/product/v0.1.0/schema.json'], 'stac_version': '1.1.0', 'type': 'Feature'}

Also here, we can plot one spectral band as an example.

In [11]:

ds["measurements/oa08_radiance"][::4, ::4].plot(vmin=0., vmax=300.)

ds["measurements/oa08_radiance"][::4, ::4].plot(vmin=0., vmax=300.)

Out[11]:

<matplotlib.collections.QuadMesh at 0x7b2c7012d590>

---

Open a Sentinel-3 OLCI L1 ERR¶

We now access a Sentinel-3 OLCI L1 ERR product in native mode. The data access methods shown above apply equally to Sentinel-3 OLCI L1 EFR.

Find a Sentinel-3 OLCI L1 ERR Zarr Sample via STAC¶

To obtain a product URL, you can use the STAC Browser to search for available Sentinel-3 OLCI L1 ERR tiles.

In [22]:

catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu")
items = list(
    catalog.search(
        collections=["sentinel-3-olci-l1-err"],
        bbox=[7.2, 44.5, 7.4, 44.7],
        datetime=["2026-03-13", "2026-03-13"],
    ).items()
)
items

catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu") items = list( catalog.search( collections=["sentinel-3-olci-l1-err"], bbox=[7.2, 44.5, 7.4, 44.7], datetime=["2026-03-13", "2026-03-13"], ).items() ) items

Out[22]:

[<Item id=S3B_OL_1_ERR____20260313T095241_20260313T103636_20260314T095142_2635_117_350______ESA_O_NT_004>,
 <Item id=S3B_OL_1_ERR____20260313T095241_20260313T103636_20260313T121631_2635_117_350______ESA_O_NR_004>]

In [23]:

item = items[0]
item

item = items[0] item

Out[23]:

<Item id=S3B_OL_1_ERR____20260313T095241_20260313T103636_20260314T095142_2635_117_350______ESA_O_NT_004>

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  • 5 "https://cs-si.github.io/eopf-stac-extension/v1.2.0/schema.json"

  • 6 "https://stac-extensions.github.io/version/v1.2.0/schema.json"
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      • 0 "data"
  • Oa16_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcerr-eu/13/products/cpm_v262/S3B_OL_1_ERR____20260313T095241_20260313T103636_20260314T095142_2635_117_350______ESA_O_NT_004.zarr/measurements/oa16_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa16"
    • bands[] 1 items

      • 0

        • name "Oa16"
        • eo:common_name "rededge078"
        • eo:center_wavelength 778.75
        • eo:full_width_half_max 15
    • roles[] 1 items

      • 0 "data"
  • Oa17_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcerr-eu/13/products/cpm_v262/S3B_OL_1_ERR____20260313T095241_20260313T103636_20260314T095142_2635_117_350______ESA_O_NT_004.zarr/measurements/oa17_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa17"
    • bands[] 1 items

      • 0

        • name "Oa17"
        • eo:common_name "nir08"
        • eo:center_wavelength 865
        • eo:full_width_half_max 20
    • roles[] 1 items

      • 0 "data"
  • Oa18_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcerr-eu/13/products/cpm_v262/S3B_OL_1_ERR____20260313T095241_20260313T103636_20260314T095142_2635_117_350______ESA_O_NT_004.zarr/measurements/oa18_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa18"
    • bands[] 1 items

      • 0

        • name "Oa18"
        • eo:center_wavelength 885
        • eo:full_width_half_max 10
    • roles[] 1 items

      • 0 "data"
  • Oa19_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcerr-eu/13/products/cpm_v262/S3B_OL_1_ERR____20260313T095241_20260313T103636_20260314T095142_2635_117_350______ESA_O_NT_004.zarr/measurements/oa19_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa19"
    • bands[] 1 items

      • 0

        • name "Oa19"
        • eo:center_wavelength 900
        • eo:full_width_half_max 10
    • roles[] 1 items

      • 0 "data"
  • Oa20_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcerr-eu/13/products/cpm_v262/S3B_OL_1_ERR____20260313T095241_20260313T103636_20260314T095142_2635_117_350______ESA_O_NT_004.zarr/measurements/oa20_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa20"
    • bands[] 1 items

      • 0

        • name "Oa20"
        • eo:common_name "nir09"
        • eo:center_wavelength 940
        • eo:full_width_half_max 20
    • roles[] 1 items

      • 0 "data"
  • Oa21_radianceData

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03olcerr-eu/13/products/cpm_v262/S3B_OL_1_ERR____20260313T095241_20260313T103636_20260314T095142_2635_117_350______ESA_O_NT_004.zarr/measurements/oa21_radiance"
    • type "application/vnd+zarr"
    • title "TOA radiance for OLCI acquisition band Oa21"
    • bands[] 1 items

      • 0

        • name "Oa21"
        • eo:center_wavelength 1020
        • eo:full_width_half_max 40
    • roles[] 1 items

      • 0 "data"
• collection "sentinel-3-olci-l1-err"

Open Sentinel-3 OLCI L1 ERR as DataTree¶

We can use the "product" asset to obtain the href and xarray:open_datatree_kwargs from the STAC item, and open the product as an xarray.DataTree as shown below:

In [24]:

dt = xr.open_datatree(
    item.assets["product"].href,
    **item.assets["product"].extra_fields["xarray:open_datatree_kwargs"]
)
dt

dt = xr.open_datatree( item.assets["product"].href, **item.assets["product"].extra_fields["xarray:open_datatree_kwargs"] ) dt

Out[24]:

<xarray.DatasetView> Size: 0B
Dimensions:  ()
Data variables:
    *empty*
Attributes: (3)

xarray.DataTree

• Groups: (3)
  <xarray.DatasetView> Size: 0B
  Dimensions:  ()
  Data variables:
      *empty*

  conditions

  • Groups: (4)
    <xarray.DatasetView> Size: 55MB
    Dimensions:    (rows: 14974, columns: 77)
    Coordinates:
        latitude   (rows, columns) float64 9MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        longitude  (rows, columns) float64 9MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
    Dimensions without coordinates: rows, columns
    Data variables:
        oaa        (rows, columns) float64 9MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        oza        (rows, columns) float64 9MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        saa        (rows, columns) float64 9MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        sza        (rows, columns) float64 9MB dask.array<chunksize=(1024, 77), meta=np.ndarray>

    geometry

    • Groups: (0)
    • Dimensions:
      • rows: 14974
      • columns: 77
    • Coordinates: (2)
      • latitude
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        latitude of tie points

        standard_name :

        latitude

        units :

        degrees_north

        valid_max :

        90000000

        valid_min :

        -90000000

        Array Chunk Bytes 8.80 MiB 616.00 kiB Shape (14974, 77) (1024, 77) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

      • longitude
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        longitude of tie points

        standard_name :

        longitude

        units :

        degrees_east

        valid_max :

        180000000

        valid_min :

        -180000000

        Array Chunk Bytes 8.80 MiB 616.00 kiB Shape (14974, 77) (1024, 77) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

    • Data variables: (4)
      • oaa
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<i4', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': -2147483648, 'long_name': 'observation (viewing) azimuth angle', 'scale_factor': 1e-06, 'short_name': 'oaa', 'units': 'degrees', 'valid_max': 180000000, 'valid_min': -180000000}

        dtype :

        <i4

        long_name :

        observation (viewing) azimuth angle

        units :

        degrees

        Array Chunk Bytes 8.80 MiB 616.00 kiB Shape (14974, 77) (1024, 77) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

      • oza
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u4', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 4294967295, 'long_name': 'observation (viewing) zenith angle', 'scale_factor': 1e-06, 'short_name': 'oza', 'units': 'degrees', 'valid_max': 180000000, 'valid_min': 0}

        dtype :

        <u4

        long_name :

        observation (viewing) zenith angle

        units :

        degrees

        Array Chunk Bytes 8.80 MiB 616.00 kiB Shape (14974, 77) (1024, 77) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

      • saa
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<i4', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': -2147483648, 'long_name': 'sun azimuth angle', 'scale_factor': 1e-06, 'short_name': 'saa', 'units': 'degrees', 'valid_max': 180000000, 'valid_min': -180000000}

        dtype :

        <i4

        long_name :

        sun azimuth angle

        units :

        degrees

        Array Chunk Bytes 8.80 MiB 616.00 kiB Shape (14974, 77) (1024, 77) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

      • sza
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u4', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': 4294967295, 'long_name': 'sun zenith angle', 'scale_factor': 1e-06, 'short_name': 'sza', 'units': 'degrees', 'valid_max': 180000000, 'valid_min': 0}

        dtype :

        <u4

        long_name :

        sun zenith angle

        units :

        degrees

        Array Chunk Bytes 8.80 MiB 616.00 kiB Shape (14974, 77) (1024, 77) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

    • Attributes: (0)

    <xarray.DatasetView> Size: 474MB
    Dimensions:         (rows: 14974, columns: 1217)
    Coordinates:
        altitude        (rows, columns) int16 36MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
        latitude        (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
        longitude       (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
        time_stamp      (rows) datetime64[ns] 120kB dask.array<chunksize=(1024,), meta=np.ndarray>
    Dimensions without coordinates: rows, columns
    Data variables:
        detector_index  (rows, columns) float32 73MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
        frame_offset    (rows, columns) float32 73MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    image

    • Groups: (0)
    • Dimensions:
      • rows: 14974
      • columns: 1217
    • Coordinates: (4)
      • altitude
        (rows, columns)
        int16
        dask.array<chunksize=(1024, 1024), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <i2

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f4

        fill_value :

        -32768

        long_name :

        DEM corrected altitude of surface elevation above reference ellipsoid

        standard_name :

        altitude

        units :

        m

        valid_max :

        9000

        valid_min :

        -1000

        Array Chunk Bytes 34.76 MiB 2.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type int16 numpy.ndarray

      • latitude
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 1024), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        DEM corrected latitude

        standard_name :

        latitude

        units :

        degrees_north

        valid_max :

        90000000

        valid_min :

        -90000000

        Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

      • longitude
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 1024), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        DEM corrected longitude

        standard_name :

        longitude

        units :

        degrees_east

        valid_max :

        180000000

        valid_min :

        -180000000

        Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

      • time_stamp
        (rows)
        datetime64[ns]
        dask.array<chunksize=(1024,), meta=np.ndarray>

        dimensions :

        ['rows']

        dtype :

        <M8[ns]

        fill_value :

        -1

        long_name :

        elapsed time since 01 Jan 2000 00:00:00 as time stamp for each row

        standard_name :

        time

        Array Chunk Bytes 116.98 kiB 8.00 kiB Shape (14974,) (1024,) Dask graph 15 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

    • Data variables: (2)
      • detector_index
        (rows, columns)
        float32
        dask.array<chunksize=(1024, 1024), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<i2', 'fill_value': -1, 'long_name': 'detector index for each pixel', 'short_name': 'detector_index', 'valid_max': 3699, 'valid_min': 0}

        dtype :

        <i2

        long_name :

        detector index for each pixel

        Array Chunk Bytes 69.52 MiB 4.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float32 numpy.ndarray

      • frame_offset
        (rows, columns)
        float32
        dask.array<chunksize=(1024, 1024), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<i1', 'eopf_is_masked': True, 'eopf_target_dtype': '|i1', 'fill_value': -128, 'long_name': 'resampling along-track frame offset', 'short_name': 'frame_offset', 'valid_max': 15, 'valid_min': -15}

        dtype :

        <i1

        long_name :

        resampling along-track frame offset

        Array Chunk Bytes 69.52 MiB 4.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float32 numpy.ndarray

    • Attributes: (0)

    <xarray.DatasetView> Size: 964kB
    Dimensions:                       (bands: 21, detectors: 3700, bands2: 21)
    Coordinates:
      * detectors                     (detectors) int64 30kB 0 1 2 ... 3698 3699
    Dimensions without coordinates: bands, bands2
    Data variables:
        fwhm                          (bands, detectors) float32 311kB dask.array<chunksize=(21, 3700), meta=np.ndarray>
        lambda0                       (bands, detectors) float32 311kB dask.array<chunksize=(21, 3700), meta=np.ndarray>
        relative_spectral_covariance  (bands, bands2) float32 2kB dask.array<chunksize=(21, 21), meta=np.ndarray>
        solar_flux                    (bands, detectors) float32 311kB dask.array<chunksize=(21, 3700), meta=np.ndarray>

    instrument

    • Groups: (0)
    • Dimensions:
      • bands: 21
      • detectors: 3700
      • bands2: 21
    • Coordinates: (1)
      • detectors
        (detectors)
        int64
        0 1 2 3 4 ... 3696 3697 3698 3699

        dimensions :

        ['detectors']

        dtype :

        <i8

        long_name :

        instrument detectors

        array([   0,    1,    2, ..., 3697, 3698, 3699], shape=(3700,))

    • Data variables: (4)
      • fwhm
        (bands, detectors)
        float32
        dask.array<chunksize=(21, 3700), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['detectors'], 'dimensions': ['bands', 'detectors'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'OLCI bandwidth (Full Width at Half Maximum)', 'short_name': 'fwhm', 'units': 'nm', 'valid_max': 650.0, 'valid_min': 0.0}

        dtype :

        <f4

        long_name :

        OLCI bandwidth (Full Width at Half Maximum)

        units :

        nm

        Array Chunk Bytes 303.52 kiB 303.52 kiB Shape (21, 3700) (21, 3700) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • lambda0
        (bands, detectors)
        float32
        dask.array<chunksize=(21, 3700), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['detectors'], 'dimensions': ['bands', 'detectors'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'OLCI characterised central wavelength', 'short_name': 'lambda0', 'units': 'nm', 'valid_max': 1040.0, 'valid_min': 390.0}

        dtype :

        <f4

        long_name :

        OLCI characterised central wavelength

        units :

        nm

        Array Chunk Bytes 303.52 kiB 303.52 kiB Shape (21, 3700) (21, 3700) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • relative_spectral_covariance
        (bands, bands2)
        float32
        dask.array<chunksize=(21, 21), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['bands', 'bands2'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': 0, 'long_name': 'relative spectral covariance matrix', 'short_name': 'relative_spectral_covariance'}

        dtype :

        <f4

        long_name :

        relative spectral covariance matrix

        Array Chunk Bytes 1.72 kiB 1.72 kiB Shape (21, 21) (21, 21) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

      • solar_flux
        (bands, detectors)
        float32
        dask.array<chunksize=(21, 3700), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['detectors'], 'dimensions': ['bands', 'detectors'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'in-band solar irradiance, seasonally corrected', 'short_name': 'solar_flux', 'units': 'mW.m-2.nm-1', 'valid_max': 2500.0, 'valid_min': 500.0}

        dtype :

        <f4

        long_name :

        in-band solar irradiance, seasonally corrected

        units :

        mW.m-2.nm-1

        Array Chunk Bytes 303.52 kiB 303.52 kiB Shape (21, 3700) (21, 3700) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    • Attributes: (0)

    <xarray.DatasetView> Size: 161MB
    Dimensions:                          (pressure_level: 25, rows: 14974,
                                          columns: 77, wind_vector: 2)
    Coordinates:
        latitude                         (rows, columns) float64 9MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        longitude                        (rows, columns) float64 9MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
      * pressure_level                   (pressure_level) int64 200B 0 1 2 ... 23 24
      * wind_vector                      (wind_vector) int64 16B 0 1
    Dimensions without coordinates: rows, columns
    Data variables:
        atmospheric_temperature_profile  (pressure_level, rows, columns) float32 115MB dask.array<chunksize=(7, 1024, 77), meta=np.ndarray>
        horizontal_wind                  (wind_vector, rows, columns) float32 9MB dask.array<chunksize=(1, 1024, 77), meta=np.ndarray>
        humidity                         (rows, columns) float32 5MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        sea_level_pressure               (rows, columns) float32 5MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        total_columnar_water_vapour      (rows, columns) float32 5MB dask.array<chunksize=(1024, 77), meta=np.ndarray>
        total_ozone                      (rows, columns) float32 5MB dask.array<chunksize=(1024, 77), meta=np.ndarray>

    meteorology

    • Groups: (0)
    • Dimensions:
      • pressure_level: 25
      • rows: 14974
      • columns: 77
      • wind_vector: 2
    • Coordinates: (4)
      • latitude
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        latitude of tie points

        standard_name :

        latitude

        units :

        degrees_north

        valid_max :

        90000000

        valid_min :

        -90000000

        Array Chunk Bytes 8.80 MiB 616.00 kiB Shape (14974, 77) (1024, 77) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

      • longitude
        (rows, columns)
        float64
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        longitude of tie points

        standard_name :

        longitude

        units :

        degrees_east

        valid_max :

        180000000

        valid_min :

        -180000000

        Array Chunk Bytes 8.80 MiB 616.00 kiB Shape (14974, 77) (1024, 77) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

      • pressure_level
        (pressure_level)
        int64
        0 1 2 3 4 5 6 ... 19 20 21 22 23 24

        dimensions :

        ['pressure_level']

        dtype :

        <i8

        long_name :

        coordinates of the vertical temperature profile

        array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
               18, 19, 20, 21, 22, 23, 24])

      • wind_vector
        (wind_vector)
        int64
        0 1

        dimensions :

        ['wind_vector']

        dtype :

        <i8

        long_name :

        dimensions of horizontal wind vector

        array([0, 1])

    • Data variables: (6)
      • atmospheric_temperature_profile
        (pressure_level, rows, columns)
        float32
        dask.array<chunksize=(7, 1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'pressure_level', 'longitude'], 'dimensions': ['pressure_level', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'air temperature vertical profile', 'short_name': 'atmospheric_temperature_profile', 'standard_name': 'air_temperature', 'units': 'K', 'valid_max': 400.0, 'valid_min': 0.0}

        dtype :

        <f4

        long_name :

        air temperature vertical profile

        standard_name :

        air_temperature

        units :

        K

        Array Chunk Bytes 109.96 MiB 2.11 MiB Shape (25, 14974, 77) (7, 1024, 77) Dask graph 60 chunks in 2 graph layers Data type float32 numpy.ndarray

      • horizontal_wind
        (wind_vector, rows, columns)
        float32
        dask.array<chunksize=(1, 1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['wind_vector', 'latitude', 'longitude'], 'dimensions': ['wind_vector', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': 9.969209968386869e+36, 'long_name': 'horizontal wind vector at 10m altitude', 'short_name': 'horizontal_wind', 'units': 'm.s-1', 'valid_max': 100.0, 'valid_min': -100.0}

        dtype :

        <f4

        long_name :

        horizontal wind vector at 10m altitude

        units :

        m.s-1

        Array Chunk Bytes 8.80 MiB 308.00 kiB Shape (2, 14974, 77) (1, 1024, 77) Dask graph 30 chunks in 2 graph layers Data type float32 numpy.ndarray

      • humidity
        (rows, columns)
        float32
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'relative humidity', 'short_name': 'humidity', 'standard_name': 'relative_humidity', 'units': '%', 'valid_max': 100.0, 'valid_min': 0.0}

        dtype :

        <f4

        long_name :

        relative humidity

        standard_name :

        relative_humidity

        units :

        %

        Array Chunk Bytes 4.40 MiB 308.00 kiB Shape (14974, 77) (1024, 77) Dask graph 15 chunks in 2 graph layers Data type float32 numpy.ndarray

      • sea_level_pressure
        (rows, columns)
        float32
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'mean sea level pressure', 'short_name': 'sea_level_pressure', 'standard_name': 'air_pressure_at_sea_level', 'units': 'hPa', 'valid_max': 1500.0, 'valid_min': 0.0}

        dtype :

        <f4

        long_name :

        mean sea level pressure

        standard_name :

        air_pressure_at_sea_level

        units :

        hPa

        Array Chunk Bytes 4.40 MiB 308.00 kiB Shape (14974, 77) (1024, 77) Dask graph 15 chunks in 2 graph layers Data type float32 numpy.ndarray

      • total_columnar_water_vapour
        (rows, columns)
        float32
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'total column water vapour content', 'short_name': 'total_columnar_water_vapour', 'standard_name': 'atmosphere_water_vapor_content', 'units': 'kg.m-2', 'valid_max': 100.0, 'valid_min': 0.0}

        dtype :

        <f4

        long_name :

        total column water vapour content

        standard_name :

        atmosphere_water_vapor_content

        units :

        kg.m-2

        Array Chunk Bytes 4.40 MiB 308.00 kiB Shape (14974, 77) (1024, 77) Dask graph 15 chunks in 2 graph layers Data type float32 numpy.ndarray

      • total_ozone
        (rows, columns)
        float32
        dask.array<chunksize=(1024, 77), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'fill_value': -1.0, 'long_name': 'total columnar ozone content', 'short_name': 'total_ozone', 'standard_name': 'atmosphere_mass_content_of_ozone', 'units': 'kg.m-2', 'valid_max': 100.0, 'valid_min': 0.0}

        dtype :

        <f4

        long_name :

        total columnar ozone content

        standard_name :

        atmosphere_mass_content_of_ozone

        units :

        kg.m-2

        Array Chunk Bytes 4.40 MiB 308.00 kiB Shape (14974, 77) (1024, 77) Dask graph 15 chunks in 2 graph layers Data type float32 numpy.ndarray

    • Attributes: (0)

  • Dimensions:
  • Coordinates: (0)
  • Data variables: (0)
  • Attributes: (0)

  <xarray.DatasetView> Size: 3GB
  Dimensions:        (rows: 14974, columns: 1217)
  Coordinates:
      altitude       (rows, columns) int16 36MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      latitude       (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      longitude      (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      time_stamp     (rows) datetime64[ns] 120kB dask.array<chunksize=(1024,), meta=np.ndarray>
  Dimensions without coordinates: rows, columns
  Data variables: (12/21)
      oa01_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa02_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa03_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa04_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa05_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa06_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      ...             ...
      oa16_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa17_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa18_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa19_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa20_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa21_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>

  measurements

  • Groups: (0)
  • Dimensions:
    • rows: 14974
    • columns: 1217
  • Coordinates: (4)
    • altitude
      (rows, columns)
      int16
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i2

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f4

      fill_value :

      -32768

      long_name :

      DEM corrected altitude of surface elevation above reference ellipsoid

      standard_name :

      altitude

      units :

      m

      valid_max :

      9000

      valid_min :

      -1000

      Array Chunk Bytes 34.76 MiB 2.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type int16 numpy.ndarray

    • latitude
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i4

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f8

      fill_value :

      -2147483648

      long_name :

      DEM corrected latitude

      standard_name :

      latitude

      units :

      degrees_north

      valid_max :

      90000000

      valid_min :

      -90000000

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • longitude
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i4

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f8

      fill_value :

      -2147483648

      long_name :

      DEM corrected longitude

      standard_name :

      longitude

      units :

      degrees_east

      valid_max :

      180000000

      valid_min :

      -180000000

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • time_stamp
      (rows)
      datetime64[ns]
      dask.array<chunksize=(1024,), meta=np.ndarray>

      dimensions :

      ['rows']

      dtype :

      <M8[ns]

      fill_value :

      -1

      long_name :

      elapsed time since 01 Jan 2000 00:00:00 as time stamp for each row

      standard_name :

      time

      Array Chunk Bytes 116.98 kiB 8.00 kiB Shape (14974,) (1024,) Dask graph 15 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • Data variables: (21)
    • oa01_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 01', 'scale_factor': 0.013634907081723213, 'short_name': 'oa01_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 01

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa02_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 02', 'scale_factor': 0.011490973643958569, 'short_name': 'oa02_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 02

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa03_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 03', 'scale_factor': 0.012588496319949627, 'short_name': 'oa03_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 03

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa04_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 04', 'scale_factor': 0.011396283283829689, 'short_name': 'oa04_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 04

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa05_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 05', 'scale_factor': 0.012233924120664597, 'short_name': 'oa05_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 05

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa06_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 06', 'scale_factor': 0.010038239881396294, 'short_name': 'oa06_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 06

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa07_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 07', 'scale_factor': 0.01056649163365364, 'short_name': 'oa07_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 07

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa08_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 08', 'scale_factor': 0.008974685333669186, 'short_name': 'oa08_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 08

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa09_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 09', 'scale_factor': 0.009690877981483936, 'short_name': 'oa09_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 09

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa10_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 10', 'scale_factor': 0.008986380882561207, 'short_name': 'oa10_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 10

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa11_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 11', 'scale_factor': 0.008361034095287323, 'short_name': 'oa11_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 11

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa12_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 12', 'scale_factor': 0.007491027936339378, 'short_name': 'oa12_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 12

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa13_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 13', 'scale_factor': 0.007861301302909851, 'short_name': 'oa13_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 13

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa14_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 14', 'scale_factor': 0.007529641035944223, 'short_name': 'oa14_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 14

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa15_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 15', 'scale_factor': 0.00788309145718813, 'short_name': 'oa15_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 15

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa16_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 16', 'scale_factor': 0.007152478210628033, 'short_name': 'oa16_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 16

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa17_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 17', 'scale_factor': 0.006055756937712431, 'short_name': 'oa17_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 17

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa18_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 18', 'scale_factor': 0.0060341269709169865, 'short_name': 'oa18_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 18

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa19_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 19', 'scale_factor': 0.0055409627966582775, 'short_name': 'oa19_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 19

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa20_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 20', 'scale_factor': 0.005095385946333408, 'short_name': 'oa20_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 20

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa21_radiance
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 21', 'scale_factor': 0.004291551187634468, 'short_name': 'oa21_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

      dtype :

      <u2

      long_name :

      TOA radiance for OLCI acquisition band 21

      standard_name :

      toa_upwelling_spectral_radiance

      units :

      mW.m-2.sr-1.nm-1

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • Attributes: (0)

  <xarray.DatasetView> Size: 3GB
  Dimensions:            (rows: 14974, columns: 1217)
  Coordinates:
      altitude           (rows, columns) int16 36MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      latitude           (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      longitude          (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      time_stamp         (rows) datetime64[ns] 120kB dask.array<chunksize=(1024,), meta=np.ndarray>
  Dimensions without coordinates: rows, columns
  Data variables: (12/22)
      oa01_radiance_unc  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa02_radiance_unc  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa03_radiance_unc  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa04_radiance_unc  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa05_radiance_unc  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa06_radiance_unc  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      ...                 ...
      oa17_radiance_unc  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa18_radiance_unc  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa19_radiance_unc  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa20_radiance_unc  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      oa21_radiance_unc  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
      quality_flags      (rows, columns) uint32 73MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>

  quality

  • Groups: (0)
  • Dimensions:
    • rows: 14974
    • columns: 1217
  • Coordinates: (4)
    • altitude
      (rows, columns)
      int16
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i2

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f4

      fill_value :

      -32768

      long_name :

      DEM corrected altitude of surface elevation above reference ellipsoid

      standard_name :

      altitude

      units :

      m

      valid_max :

      9000

      valid_min :

      -1000

      Array Chunk Bytes 34.76 MiB 2.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type int16 numpy.ndarray

    • latitude
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i4

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f8

      fill_value :

      -2147483648

      long_name :

      DEM corrected latitude

      standard_name :

      latitude

      units :

      degrees_north

      valid_max :

      90000000

      valid_min :

      -90000000

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • longitude
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i4

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f8

      fill_value :

      -2147483648

      long_name :

      DEM corrected longitude

      standard_name :

      longitude

      units :

      degrees_east

      valid_max :

      180000000

      valid_min :

      -180000000

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • time_stamp
      (rows)
      datetime64[ns]
      dask.array<chunksize=(1024,), meta=np.ndarray>

      dimensions :

      ['rows']

      dtype :

      <M8[ns]

      fill_value :

      -1

      long_name :

      elapsed time since 01 Jan 2000 00:00:00 as time stamp for each row

      standard_name :

      time

      Array Chunk Bytes 116.98 kiB 8.00 kiB Shape (14974,) (1024,) Dask graph 15 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • Data variables: (22)
    • oa01_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.19358299672603607, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 01', 'scale_factor': 0.0054720002226531506, 'short_name': 'oa01_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 01

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa02_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.21523700654506683, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 02', 'scale_factor': 0.005102999974042177, 'short_name': 'oa02_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 02

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa03_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.2714740037918091, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 03', 'scale_factor': 0.005386999808251858, 'short_name': 'oa03_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 03

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa04_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.30964401364326477, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 04', 'scale_factor': 0.005243999883532524, 'short_name': 'oa04_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 04

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa05_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.3706839978694916, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 05', 'scale_factor': 0.005743000190705061, 'short_name': 'oa05_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 05

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa06_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.4443579912185669, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 06', 'scale_factor': 0.006068000104278326, 'short_name': 'oa06_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 06

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa07_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.5721830129623413, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 07', 'scale_factor': 0.00686699990183115, 'short_name': 'oa07_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 07

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa08_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.6707040071487427, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 08', 'scale_factor': 0.007137000095099211, 'short_name': 'oa08_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 08

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa09_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.6211689710617065, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 09', 'scale_factor': 0.006550000049173832, 'short_name': 'oa09_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 09

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa10_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.6792640089988708, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 10', 'scale_factor': 0.006779999937862158, 'short_name': 'oa10_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 10

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa11_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.7377849817276001, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 11', 'scale_factor': 0.007145000156015158, 'short_name': 'oa11_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 11

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa12_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.7818710207939148, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 12', 'scale_factor': 0.006775000132620335, 'short_name': 'oa12_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 12

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa13_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.7220579981803894, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 13', 'scale_factor': 0.005898999981582165, 'short_name': 'oa13_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 13

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa14_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.6917920112609863, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 14', 'scale_factor': 0.006147999782115221, 'short_name': 'oa14_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 14

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa15_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.7225130200386047, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 15', 'scale_factor': 0.006641000043600798, 'short_name': 'oa15_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 15

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa16_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.9848459959030151, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 16', 'scale_factor': 0.008098999969661236, 'short_name': 'oa16_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 16

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa17_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.9873489737510681, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 17', 'scale_factor': 0.008390000090003014, 'short_name': 'oa17_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 17

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa18_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.7908260226249695, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 18', 'scale_factor': 0.007825000211596489, 'short_name': 'oa18_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 18

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa19_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.8344600200653076, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 19', 'scale_factor': 0.007402999792248011, 'short_name': 'oa19_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 19

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa20_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.9243649840354919, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 20', 'scale_factor': 0.008166000247001648, 'short_name': 'oa20_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 20

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • oa21_radiance_unc
      (rows, columns)
      float64
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': -0.5929380059242249, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'log10 scaled radiometric uncertainty estimate for OLCI acquisition band 21', 'scale_factor': 0.0057830000296235085, 'short_name': 'oa21_radiance_unc', 'units': 'lg(re mW.m-2.sr-1.nm-1)', 'valid_max': 254, 'valid_min': 0}

      dtype :

      <u1

      long_name :

      log10 scaled radiometric uncertainty estimate for OLCI acquisition band 21

      units :

      lg(re mW.m-2.sr-1.nm-1)

      Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

    • quality_flags
      (rows, columns)
      uint32
      dask.array<chunksize=(1024, 1024), meta=np.ndarray>

      _eopf_attrs :

      {'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u4', 'eopf_is_masked': True, 'flag_masks': [2147483648, 1073741824, 536870912, 268435456, 134217728, 67108864, 33554432, 16777216, 8388608, 4194304, 2097152, 1048576, 524288], 'flag_meanings': 'land coastline fresh_inland_water tidal_region bright straylight_risk invalid cosmetic duplicated sun-glint_risk dubious saturated partially_saturated', 'long_name': 'classification and quality flags', 'short_name': 'quality_flags'}

      dtype :

      <u4

      flag_masks :

      [2147483648, 1073741824, 536870912, 268435456, 134217728, 67108864, 33554432, 16777216, 8388608, 4194304, 2097152, 1048576, 524288]

      flag_meanings :

      land coastline fresh_inland_water tidal_region bright straylight_risk invalid cosmetic duplicated sun-glint_risk dubious saturated partially_saturated

      long_name :

      classification and quality flags

      Array Chunk Bytes 69.52 MiB 4.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type uint32 numpy.ndarray

  • Attributes: (0)

• Dimensions:
• Coordinates: (0)
• Inherited coordinates: (0)
• Data variables: (0)
• Attributes: (3)

  other_metadata :

  {'absolute_pass_number': 82094, 'cycle_number': 117, 'data_information': {'accuracy_reference': '0', 'earth_sun_distance': 148692226821, 'measurement_accuracy': '2.000000', 'ocl_status': 'true', 'sampling': {'across_track_distance': 1080, 'along_track_distance': 1176, 'along_track_time': 176008, 'columns_per_tiepoint': 16, 'rows_per_tiepoint': 1}}, 'eopf_category': 'eoproduct', 'ephemeris': {'start': {'TAI': '2026-03-13T09:25:25.256763', 'UT1': '2026-03-13T09:24:48.319326', 'UTC': '2026-03-13T09:24:48.256763Z', 'position': {'x': -7094686.732, 'y': -1129221.277, 'z': 0.001}, 'velocity': {'x': -249.56373, 'y': 1621.140793, 'z': 7366.211176}}, 'stop': {'TAI': '2026-03-13T11:06:24.413833', 'UT1': '2026-03-13T11:05:47.476392', 'UTC': '2026-03-13T11:05:47.413833Z', 'position': {'x': -6898641.864, 'y': 2004695.114, 'z': 0.003}, 'velocity': {'x': 465.705986, 'y': 1572.789106, 'z': 7366.200009}}}, 'meteo': {'missing_time': 0, 'source': 'ECMWF', 'type': 'ANALYSIS'}, 'phase_identifier': 4, 'product_unit': {'alongtrack_coordinate': 'null', 'duration': 2635, 'type': 'STRIPE'}, 'relative_pass_number': 700}

  processing_history :

  {'Level-0 Product': [{'adfs': ['S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_TM_0_NAT__G_20260310T192718_20260310T210759_20260310T211703_6041______________SVL_O_NR_OPE.ISIP', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_TM_0_NAT____20260310T192700_20260310T210741_20260310T211958_6041_117_313______ESA_O_AL_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-10T21:20:06.608260Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260307T000000_20260317T000000_20260310T064738___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_TM_0_NAT__G_20260310T192718_20260310T210759_20260310T211703_6041______________SVL_O_NR_OPE.ISIP', 'S3IPF PDS 001 - 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i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T100839_20260313T101039_20260313T114419_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:44:26.590451Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T100057_20260313T100257_20260313T113923_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T100039_20260313T100239_20260313T114420_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:44:28.466363Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T101257_20260313T101457_20260313T113936_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T101239_20260313T101439_20260313T114426_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:44:33.920556Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T101457_20260313T101657_20260313T113938_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T101439_20260313T101639_20260313T114455_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:45:03.371903Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T101657_20260313T101857_20260313T113944_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T101639_20260313T101839_20260313T114502_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:45:09.982449Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T101857_20260313T102057_20260313T113945_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T101839_20260313T102039_20260313T114521_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:45:29.324817Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T102257_20260313T102457_20260313T113947_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - 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i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T103039_20260313T103239_20260313T114542_0119_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:45:49.932823Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T103457_20260313T103654_20260313T113956_0117______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - i1r8 - Product Data Format Specification - Level 0'], 'outputs': ['S3B_OL_0_EFR____20260313T103439_20260313T103636_20260313T114555_0117_117_350______ESA_O_NR_002.SEN3'], 'processor': 'IPF-0', 'time': '2026-03-13T11:46:03.623702Z', 'version': '06.16'}, {'adfs': ['S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___FRO_AX_20260310T000000_20260320T000000_20260313T064731___________________EUM_O_AL_001.SEN3', 'S3B_AX___OSF_AX_20180425T191855_99991231T235959_20250724T075944___________________EUM_O_AL_001.SEN3'], 'facility': 'ESA-Copernicus Ground Segment', 'inputs': ['S3B_OL_0_EFR__G_20260313T103257_20260313T103457_20260313T113956_0120______________SVL_O_NR_OPE.ISIP', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3B_TM_0_NAT____20260313T094951_20260313T112957_20260313T114140_6006_117_350______ESA_O_AL_002.SEN3', 'S3IPF PDS 001 - 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  stac_discovery :

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'oa02_radiance_unc'}, 'oa03_radiance': {'href': '/measurements/oa03_radiance', 'title': 'oa03_radiance'}, 'oa03_radiance_unc': {'href': '/quality/oa03_radiance_unc', 'title': 'oa03_radiance_unc'}, 'oa04_radiance': {'href': '/measurements/oa04_radiance', 'title': 'oa04_radiance'}, 'oa04_radiance_unc': {'href': '/quality/oa04_radiance_unc', 'title': 'oa04_radiance_unc'}, 'oa05_radiance': {'href': '/measurements/oa05_radiance', 'title': 'oa05_radiance'}, 'oa05_radiance_unc': {'href': '/quality/oa05_radiance_unc', 'title': 'oa05_radiance_unc'}, 'oa06_radiance': {'href': '/measurements/oa06_radiance', 'title': 'oa06_radiance'}, 'oa06_radiance_unc': {'href': '/quality/oa06_radiance_unc', 'title': 'oa06_radiance_unc'}, 'oa07_radiance': {'href': '/measurements/oa07_radiance', 'title': 'oa07_radiance'}, 'oa07_radiance_unc': {'href': '/quality/oa07_radiance_unc', 'title': 'oa07_radiance_unc'}, 'oa08_radiance': {'href': '/measurements/oa08_radiance', 'title': 'oa08_radiance'}, 'oa08_radiance_unc': {'href': '/quality/oa08_radiance_unc', 'title': 'oa08_radiance_unc'}, 'oa09_radiance': {'href': '/measurements/oa09_radiance', 'title': 'oa09_radiance'}, 'oa09_radiance_unc': {'href': '/quality/oa09_radiance_unc', 'title': 'oa09_radiance_unc'}, 'oa10_radiance': {'href': '/measurements/oa10_radiance', 'title': 'oa10_radiance'}, 'oa10_radiance_unc': {'href': '/quality/oa10_radiance_unc', 'title': 'oa10_radiance_unc'}, 'oa11_radiance': {'href': '/measurements/oa11_radiance', 'title': 'oa11_radiance'}, 'oa11_radiance_unc': {'href': '/quality/oa11_radiance_unc', 'title': 'oa11_radiance_unc'}, 'oa12_radiance': {'href': '/measurements/oa12_radiance', 'title': 'oa12_radiance'}, 'oa12_radiance_unc': {'href': '/quality/oa12_radiance_unc', 'title': 'oa12_radiance_unc'}, 'oa13_radiance': {'href': '/measurements/oa13_radiance', 'title': 'oa13_radiance'}, 'oa13_radiance_unc': {'href': '/quality/oa13_radiance_unc', 'title': 'oa13_radiance_unc'}, 'oa14_radiance': {'href': '/measurements/oa14_radiance', 'title': 'oa14_radiance'}, 'oa14_radiance_unc': {'href': '/quality/oa14_radiance_unc', 'title': 'oa14_radiance_unc'}, 'oa15_radiance': {'href': '/measurements/oa15_radiance', 'title': 'oa15_radiance'}, 'oa15_radiance_unc': {'href': '/quality/oa15_radiance_unc', 'title': 'oa15_radiance_unc'}, 'oa16_radiance': {'href': '/measurements/oa16_radiance', 'title': 'oa16_radiance'}, 'oa16_radiance_unc': {'href': '/quality/oa16_radiance_unc', 'title': 'oa16_radiance_unc'}, 'oa17_radiance': {'href': '/measurements/oa17_radiance', 'title': 'oa17_radiance'}, 'oa17_radiance_unc': {'href': '/quality/oa17_radiance_unc', 'title': 'oa17_radiance_unc'}, 'oa18_radiance': {'href': '/measurements/oa18_radiance', 'title': 'oa18_radiance'}, 'oa18_radiance_unc': {'href': '/quality/oa18_radiance_unc', 'title': 'oa18_radiance_unc'}, 'oa19_radiance': {'href': '/measurements/oa19_radiance', 'title': 'oa19_radiance'}, 'oa19_radiance_unc': {'href': '/quality/oa19_radiance_unc', 'title': 'oa19_radiance_unc'}, 'oa20_radiance': {'href': '/measurements/oa20_radiance', 'title': 'oa20_radiance'}, 'oa20_radiance_unc': {'href': '/quality/oa20_radiance_unc', 'title': 'oa20_radiance_unc'}, 'oa21_radiance': {'href': '/measurements/oa21_radiance', 'title': 'oa21_radiance'}, 'oa21_radiance_unc': {'href': '/quality/oa21_radiance_unc', 'title': 'oa21_radiance_unc'}, 'oaa': {'href': '/conditions/geometry/oaa', 'title': 'oaa'}, 'oza': {'href': '/conditions/geometry/oza', 'title': 'oza'}, 'quality_flags': {'href': '/quality/quality_flags', 'title': 'quality_flags'}, 'relative_spectral_covariance': {'href': '/conditions/instrument/relative_spectral_covariance', 'title': 'relative_spectral_covariance'}, 'saa': {'href': '/conditions/geometry/saa', 'title': 'saa'}, 'sea_level_pressure': {'href': '/conditions/meteorology/sea_level_pressure', 'title': 'sea_level_pressure'}, 'solar_flux': {'href': '/conditions/instrument/solar_flux', 'title': 'solar_flux'}, 'sza': {'href': '/conditions/geometry/sza', 'title': 'sza'}, 'total_columnar_water_vapour': {'href': '/conditions/meteorology/total_columnar_water_vapour', 'title': 'total_columnar_water_vapour'}, 'total_ozone': {'href': '/conditions/meteorology/total_ozone', 'title': 'total_ozone'}}, 'bbox': [52.4304, -75.2403, -59.8305, 80.7409], 'collection': 'OL__L1_.004.01.00', 'geometry': {'coordinates': [[[-59.8305, -67.7909], [-58.7956, -68.2747], [-57.7187, -68.7523], [-56.5951, -69.2228], [-55.4222, -69.6856], [-54.1974, -70.1401], [-52.9181, -70.5859], [-51.5816, -71.0223], [-50.1853, -71.4487], [-48.7266, -71.8643], [-47.2032, -72.2685], [-45.6117, -72.6603], [-43.9502, -73.0389], [-42.2165, -73.4034], [-40.4089, -73.753], [-38.5258, -74.0865], [-36.5663, -74.403], [-34.53, -74.7015], [-32.4173, -74.9809], [-30.2296, -75.2403], [-28.7106, -74.3021], [-26.3737, -72.6068], [-24.4102, -70.8921], [-22.7313, -69.1625], [-21.2729, -67.4212], [-19.9886, -65.6705], [-18.8443, -63.9124], [-17.8133, -62.1479], [-16.8754, -60.378], [-16.0153, -58.6037], [-15.2202, -56.8254], [-14.4801, -55.0435], [-13.787, -53.2586], [-13.1341, -51.4709], [-12.516, -49.6807], [-11.9282, -47.8882], [-11.3658, -46.0932], [-10.8272, -44.2965], [-10.3084, -42.498], [-9.80737, -40.6977], [-9.32174, -38.8958], [-8.84999, -37.0922], [-8.39033, -35.2872], [-7.94112, -33.4813], [-7.50155, -31.6733], [-7.07003, -29.8647], [-6.64613, -28.0545], [-6.22816, -26.2433], [-5.81516, -24.4318], [-5.40744, -22.6186], [-5.00343, -20.8048], [-4.60262, -18.9903], [-4.20435, -17.1749], [-3.80798, -15.3589], [-3.41312, -13.5424], [-3.01902, -11.7254], [-2.62512, -9.90794], [-2.23109, -8.09022], [-1.83628, -6.27217], [-1.44006, -4.45401], [-1.04207, -2.6357], [-0.641806, -0.817444], [-0.238544, 1.00084], [0.168025, 2.8188], [0.578633, 4.63652], [0.993853, 6.45393], [1.41428, 8.2708], [1.84047, 10.087], [2.27326, 11.9027], [2.71334, 13.7175], [3.16136, 15.5313], [3.61838, 17.3442], [4.08514, 19.1558], [4.56274, 20.966], [5.05194, 22.775], [5.55408, 24.5826], [6.07129, 26.3874], [6.60353, 28.1912], [7.15334, 29.9926], [7.72215, 31.7916], [8.31196, 33.588], [8.92507, 35.3815], [9.56397, 37.1721], [10.2313, 38.9591], [10.9303, 40.7424], [11.6648, 42.5215], [12.4387, 44.2959], [13.2572, 46.0653], [14.125, 47.8289], [15.0498, 49.5857], [16.0382, 51.3356], [17.0993, 53.0777], [18.2437, 54.8097], [19.4838, 56.5312], [20.835, 58.2404], [22.3146, 59.935], [23.9445, 61.6126], [25.7513, 63.2703], [27.7674, 64.904], [30.0327, 66.5089], [32.5951, 68.0787], [35.515, 69.6056], [38.8654, 71.0791], [42.7328, 72.4859], [47.2178, 73.8084], [52.4304, 75.0255], [50.89, 75.505], [49.2479, 75.9738], [47.4962, 76.4307], [45.6266, 76.8745], [43.6308, 77.3039], [41.5004, 77.7174], [39.2272, 78.1133], [36.8037, 78.4898], [34.2234, 78.8449], [31.4818, 79.1766], [28.5758, 79.4826], [25.5195, 79.7595], [22.276, 80.0083], [18.8948, 80.2234], [15.3757, 80.4037], [11.737, 80.5472], [8.00226, 80.6522], [4.19967, 80.7175], [0.357014, 80.7409], [0.256445, 78.9567], [0.108213, 77.1731], [-0.068649, 75.3892], [-0.265643, 73.6044], [-0.476748, 71.8194], [-0.698426, 70.0341], [-0.929127, 68.248], [-1.16629, 66.4614], [-1.40875, 64.6743], [-1.65614, 62.8866], [-1.90768, 61.0983], [-2.16322, 59.309], [-2.42174, 57.5194], [-2.68367, 55.7292], [-2.9482, 53.9378], [-3.21588, 52.1461], [-3.48686, 50.3536], [-3.75989, 48.5603], [-4.0362, 46.7662], [-4.3151, 44.9715], [-4.59684, 43.1759], [-4.88141, 41.3797], [-5.16899, 39.5829], [-5.45969, 37.7852], [-5.75348, 35.9869], [-6.05044, 34.1881], [-6.35077, 32.3887], [-6.65474, 30.5884], [-6.96211, 28.7874], [-7.27392, 26.9878], [-7.58904, 25.1855], [-7.90844, 23.3832], [-8.23242, 21.5814], [-8.56093, 19.779], [-8.89434, 17.9763], [-9.23269, 16.1737], [-9.57654, 14.3708], [-9.92597, 12.568], [-10.2813, 10.7654], [-10.6429, 8.96312], [-11.0113, 7.16099], [-11.3866, 5.35933], [-11.7692, 3.55835], [-12.1599, 1.75785], [-12.5588, -0.041834], [-12.9667, -1.84074], [-13.384, -3.6385], [-13.8112, -5.43508], [-14.2491, -7.23026], [-14.6986, -9.0244], [-15.1601, -10.8165], [-15.6346, -12.6067], [-16.1231, -14.3952], [-16.6266, -16.1814], [-17.146, -17.965], [-17.6826, -19.7461], [-18.2378, -21.5249], [-18.8133, -23.2987], [-19.4102, -25.0705], [-20.0304, -26.8391], [-20.676, -28.6022], [-21.3491, -30.3613], [-22.0526, -32.1155], [-22.7885, -33.8641], [-23.5602, -35.6068], [-24.3713, -37.3431], [-25.2252, -39.0721], [-26.1266, -40.7931], [-27.0798, -42.5051], [-28.0922, -44.2082], [-29.1675, -45.8999], [-30.3144, -47.5794], [-31.5409, -49.2454], [-32.8563, -50.8963], [-34.2717, -52.53], [-35.7989, -54.1442], [-37.4526, -55.7362], [-39.249, -57.3029], [-41.2066, -58.8404], [-43.3466, -60.3443], [-45.6938, -61.8095], [-48.2747, -63.2293], [-51.1195, -64.5965], [-54.2603, -65.9022], [-57.7309, -67.1362], [-59.8305, -67.7909]]], 'type': 'Polygon'}, 'id': 'S03OLCERR_20260313T095240_2635_B350_S5F7', 'links': [{'href': './.zattrs.json', 'rel': 'collection', 'type': 'application/json'}], 'properties': {'bands': [{'eo:center_wavelength': 0.4, 'eo:full_width_half_max': 0.015, 'name': 'oa01'}, {'eo:center_wavelength': 0.41250000000000003, 'eo:full_width_half_max': 0.01, 'name': 'oa02'}, {'eo:center_wavelength': 0.4425, 'eo:full_width_half_max': 0.01, 'name': 'oa03'}, {'eo:center_wavelength': 0.49, 'eo:common_name': 'blue', 'eo:full_width_half_max': 0.01, 'name': 'oa04'}, {'eo:center_wavelength': 0.51, 'eo:full_width_half_max': 0.01, 'name': 'oa05'}, {'eo:center_wavelength': 0.56, 'eo:common_name': 'green', 'eo:full_width_half_max': 0.01, 'name': 'oa06'}, {'eo:center_wavelength': 0.62, 'eo:common_name': 'yellow', 'eo:full_width_half_max': 0.01, 'name': 'oa07'}, {'eo:center_wavelength': 0.665, 'eo:full_width_half_max': 0.01, 'name': 'oa08'}, {'eo:center_wavelength': 0.67375, 'eo:full_width_half_max': 0.0075, 'name': 'oa09'}, {'eo:center_wavelength': 0.68125, 'eo:full_width_half_max': 0.0075, 'name': 'oa10'}, {'eo:center_wavelength': 0.70875, 'eo:full_width_half_max': 0.01, 'name': 'oa11'}, {'eo:center_wavelength': 0.75375, 'eo:full_width_half_max': 0.0075, 'name': 'oa12'}, {'eo:center_wavelength': 0.76125, 'eo:full_width_half_max': 0.0025, 'name': 'oa13'}, {'eo:center_wavelength': 0.764375, 'eo:full_width_half_max': 0.00375, 'name': 'oa14'}, {'eo:center_wavelength': 0.7675000000000001, 'eo:full_width_half_max': 0.0025, 'name': 'oa15'}, {'eo:center_wavelength': 0.77875, 'eo:full_width_half_max': 0.015, 'name': 'oa16'}, {'eo:center_wavelength': 0.865, 'eo:full_width_half_max': 0.02, 'name': 'oa17'}, {'eo:center_wavelength': 0.885, 'eo:full_width_half_max': 0.01, 'name': 'oa18'}, {'eo:center_wavelength': 0.9, 'eo:full_width_half_max': 0.01, 'name': 'oa19'}, {'eo:center_wavelength': 0.9400000000000001, 'eo:full_width_half_max': 0.02, 'name': 'oa20'}, {'eo:center_wavelength': 1.02, 'eo:full_width_half_max': 0.04, 'name': 'oa21'}], 'classification:classes': [{'count': 602488, 'name': 'invalid', 'percentage': 3.0}, {'count': 16573, 'name': 'cosmetic', 'percentage': 0.0}, {'count': 0, 'name': 'duplicated', 'percentage': 0.0}, {'count': 1106, 'name': 'saturated', 'percentage': 3e-06}, {'count': 1076, 'name': 'dubious', 'percentage': 0.0}, {'name': 'saline_water', 'percentage': 67.0}, {'name': 'bright', 'percentage': 50.0}, {'name': 'coastal', 'percentage': 0.0}, {'name': 'fresh_water', 'percentage': 1.0}, {'name': 'tidal', 'percentage': 1.0}], 'constellation': 'sentinel-3', 'created': '2026-03-14T09:51:42Z', 'datetime': '2026-03-13T10:14:38.515766Z', 'end_datetime': '2026-03-13T10:36:36.285125Z', 'eopf:instrument_mode': 'Earth Observation', 'instruments': ['olci'], 'mission': 'copernicus', 'platform': 'sentinel-3b', 'processing:facility': 'OPE', 'processing:level': 'L1', 'processing:lineage': 'systematic', 'processing:software': {'PUG': '03.50'}, 'processing:version': 'TODO', 'product:timeliness': 'P1M', 'product:timeliness_category': 'NT', 'product:type': 'S03OLCERR', 'proj:shape': [{'columns': 1217, 'name': 'RR', 'rows': 14974}], 'providers': [{'name': 'Copernicus Ground Segment', 'roles': ['processor']}, {'name': 'ESA', 'roles': ['producer']}], 'sat:absolute_orbit': 41047, 'sat:orbit_state': 'descending', 'sat:platform_international_designator': '2018-039A', 'sat:relative_orbit': 350, 'start_datetime': '2026-03-13T09:52:40.746406Z'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.2.0/schema.json', 'https://stac-extensions.github.io/classification/v2.0.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/sat/v1.1.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.2.0/schema.json', 'https://stac-extensions.github.io/projection/v2.0.0/schema.json', 'https://stac-extensions.github.io/product/v0.1.0/schema.json'], 'stac_version': '1.1.0', 'type': 'Feature'}

Open Sentinel-3 OLCI L1 EFF Radiance Group as Dataset¶

We can directly access the individual reflectance group (radianceData) as xarray.Dataset objects. The opening parameters are stored in the asset’s extra field "xarray:open_dataset_kwargs".

In [25]:

ds = xr.open_dataset(
    item.assets["radianceData"].href,
    **item.assets["radianceData"].extra_fields["xarray:open_dataset_kwargs"]
)
ds

ds = xr.open_dataset( item.assets["radianceData"].href, **item.assets["radianceData"].extra_fields["xarray:open_dataset_kwargs"] ) ds

Out[25]:

<xarray.Dataset> Size: 3GB
Dimensions:        (rows: 14974, columns: 1217)
Coordinates:
    altitude       (rows, columns) int16 36MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    latitude       (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    longitude      (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    time_stamp     (rows) datetime64[ns] 120kB dask.array<chunksize=(1024,), meta=np.ndarray>
Dimensions without coordinates: rows, columns
Data variables: (12/21)
    oa01_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa02_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa03_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa04_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa05_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa06_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    ...             ...
    oa16_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa17_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa18_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa19_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa20_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>
    oa21_radiance  (rows, columns) float64 146MB dask.array<chunksize=(1024, 1024), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • rows: 14974
  • columns: 1217
• Coordinates: (4)
  • altitude
    (rows, columns)
    int16
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i2

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f4

    fill_value :

    -32768

    long_name :

    DEM corrected altitude of surface elevation above reference ellipsoid

    standard_name :

    altitude

    units :

    m

    valid_max :

    9000

    valid_min :

    -1000

    Array Chunk Bytes 34.76 MiB 2.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type int16 numpy.ndarray

  • latitude
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    DEM corrected latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90000000

    valid_min :

    -90000000

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • longitude
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    DEM corrected longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180000000

    valid_min :

    -180000000

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • time_stamp
    (rows)
    datetime64[ns]
    dask.array<chunksize=(1024,), meta=np.ndarray>

    dimensions :

    ['rows']

    dtype :

    <M8[ns]

    fill_value :

    -1

    long_name :

    elapsed time since 01 Jan 2000 00:00:00 as time stamp for each row

    standard_name :

    time

    Array Chunk Bytes 116.98 kiB 8.00 kiB Shape (14974,) (1024,) Dask graph 15 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

• Data variables: (21)
  • oa01_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 01', 'scale_factor': 0.013634907081723213, 'short_name': 'oa01_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 01

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa02_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 02', 'scale_factor': 0.011490973643958569, 'short_name': 'oa02_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 02

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa03_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 03', 'scale_factor': 0.012588496319949627, 'short_name': 'oa03_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 03

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa04_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 04', 'scale_factor': 0.011396283283829689, 'short_name': 'oa04_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 04

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa05_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 05', 'scale_factor': 0.012233924120664597, 'short_name': 'oa05_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 05

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa06_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 06', 'scale_factor': 0.010038239881396294, 'short_name': 'oa06_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 06

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa07_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 07', 'scale_factor': 0.01056649163365364, 'short_name': 'oa07_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 07

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa08_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 08', 'scale_factor': 0.008974685333669186, 'short_name': 'oa08_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 08

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa09_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 09', 'scale_factor': 0.009690877981483936, 'short_name': 'oa09_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 09

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa10_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 10', 'scale_factor': 0.008986380882561207, 'short_name': 'oa10_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 10

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa11_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 11', 'scale_factor': 0.008361034095287323, 'short_name': 'oa11_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 11

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa12_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 12', 'scale_factor': 0.007491027936339378, 'short_name': 'oa12_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 12

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa13_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 13', 'scale_factor': 0.007861301302909851, 'short_name': 'oa13_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 13

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa14_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 14', 'scale_factor': 0.007529641035944223, 'short_name': 'oa14_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 14

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa15_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 15', 'scale_factor': 0.00788309145718813, 'short_name': 'oa15_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 15

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa16_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 16', 'scale_factor': 0.007152478210628033, 'short_name': 'oa16_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 16

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa17_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 17', 'scale_factor': 0.006055756937712431, 'short_name': 'oa17_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 17

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa18_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 18', 'scale_factor': 0.0060341269709169865, 'short_name': 'oa18_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 18

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa19_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 19', 'scale_factor': 0.0055409627966582775, 'short_name': 'oa19_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 19

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa20_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 20', 'scale_factor': 0.005095385946333408, 'short_name': 'oa20_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 20

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

  • oa21_radiance
    (rows, columns)
    float64
    dask.array<chunksize=(1024, 1024), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 0.0, 'coordinates': ['latitude', 'longitude', 'time_stamp', 'altitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'TOA radiance for OLCI acquisition band 21', 'scale_factor': 0.004291551187634468, 'short_name': 'oa21_radiance', 'standard_name': 'toa_upwelling_spectral_radiance', 'units': 'mW.m-2.sr-1.nm-1', 'valid_max': 65534, 'valid_min': 0}

    dtype :

    <u2

    long_name :

    TOA radiance for OLCI acquisition band 21

    standard_name :

    toa_upwelling_spectral_radiance

    units :

    mW.m-2.sr-1.nm-1

    Array Chunk Bytes 139.03 MiB 8.00 MiB Shape (14974, 1217) (1024, 1024) Dask graph 30 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (0)
• Attributes: (0)

As an example, we plot the red band (oa08_radiance), which will trigger loading and visualization of the data. We also plot the 2D curvilinear latitude and longitude grids, which can be used for geolocation. Note that one product spanns half an orbit.

Note: To speed up rendering of large datasets in Matplotlib, we plot the data at a lower resolution (every 4th pixel along-track).

In [26]:

fig, ax = plt.subplots(1, 3, figsize=(10, 5))
ds.oa08_radiance[::4, :].plot.imshow(ax=ax[0], vmin=0, vmax=300)
ds.latitude[::4, :].plot.imshow(ax=ax[1], cmap="viridis")
ds.longitude[::4, :].plot.imshow(ax=ax[2], cmap="viridis")
plt.tight_layout()

fig, ax = plt.subplots(1, 3, figsize=(10, 5)) ds.oa08_radiance[::4, :].plot.imshow(ax=ax[0], vmin=0, vmax=300) ds.latitude[::4, :].plot.imshow(ax=ax[1], cmap="viridis") ds.longitude[::4, :].plot.imshow(ax=ax[2], cmap="viridis") plt.tight_layout()

Open a Sentinel-3 SLSTR Level-2 LST¶

We now access a Sentinel-3 SLSTR Level-2 LST product in native mode. The data access methods shown above apply equally to Sentinel-3 SLSTR Level-2 LST.

Find a Sentinel-3 SLSTR Level-2 LST Zarr Sample via STAC¶

To obtain a product URL, you can use the STAC Browser to search for a Sentinel-3 SLSTR Level-2 LST tile.

In [17]:

catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu")
items = list(
    catalog.search(
        collections=["sentinel-3-slstr-l2-lst"],
        bbox=[7.2, 44.5, 7.4, 44.7],
        datetime=["2026-03-13", "2026-03-13"],
    ).items()
)
items

catalog = pystac_client.Client.open("https://stac.core.eopf.eodc.eu") items = list( catalog.search( collections=["sentinel-3-slstr-l2-lst"], bbox=[7.2, 44.5, 7.4, 44.7], datetime=["2026-03-13", "2026-03-13"], ).items() ) items

Out[17]:

[<Item id=S3B_SL_2_LST____20260313T212343_20260313T212643_20260313T235949_0179_117_357_0720_ESA_O_NR_004>,
 <Item id=S3B_SL_2_LST____20260313T212343_20260313T212643_20260315T051508_0179_117_357_0720_ESA_O_NT_004>,
 <Item id=S3B_SL_2_LST____20260313T100048_20260313T100348_20260314T174003_0180_117_350_2160_ESA_O_NT_004>]

In [18]:

item = items[0]
item

item = items[0] item

Out[18]:

<Item id=S3B_SL_2_LST____20260313T212343_20260313T212643_20260313T235949_0179_117_357_0720_ESA_O_NR_004>

• type "Feature"
• stac_version "1.1.0"
• stac_extensions[] 7 items

  • 0 "https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"

  • 1 "https://stac-extensions.github.io/sat/v1.0.0/schema.json"

  • 2 "https://stac-extensions.github.io/eo/v2.0.0/schema.json"

  • 3 "https://stac-extensions.github.io/processing/v1.2.0/schema.json"

  • 4 "https://stac-extensions.github.io/product/v0.1.0/schema.json"

  • 5 "https://cs-si.github.io/eopf-stac-extension/v1.2.0/schema.json"

  • 6 "https://stac-extensions.github.io/version/v1.2.0/schema.json"
• id "S3B_SL_2_LST____20260313T212343_20260313T212643_20260313T235949_0179_117_357_0720_ESA_O_NR_004"
• geometry

  • type "Polygon"
  • coordinates[] 1 items

    • 0[] 71 items

      • 0[] 2 items

        • 0 -9.7816

        • 1 51.3164

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• properties

  • gsd 1000.0
  • created "2026-03-14T01:11:40.979998Z"
  • mission "Sentinel-3"
  • updated "2026-03-14T01:11:40.979998Z"
  • datetime "2026-03-13T21:25:12.824041Z"
  • platform "sentinel-3b"
  • providers[] 3 items

    • 0

      • url "https://commission.europa.eu/"
      • name "European Commission"
      • roles[] 1 items

        • 0 "licensor"

    • 1

      • url "https://sentinel.esa.int/web/sentinel/missions/sentinel-3"
      • name "ESA"
      • roles[] 2 items

        • 0 "producer"

        • 1 "processor"

    • 2

      • url "https://zarr.eopf.copernicus.eu/"
      • name "EOPF Sentinel Zarr Samples Service"
      • roles[] 2 items

        • 0 "host"

        • 1 "processor"
  • published "2026-03-14T01:11:40.979998Z"
  • deprecated False
  • instruments[] 1 items

    • 0 "slstr"
  • end_datetime "2026-03-13T21:26:42.824041Z"
  • product:type "S03SLSLST"
  • constellation "sentinel-3"
  • start_datetime "2026-03-13T21:23:42.824041Z"
  • sat:orbit_state "ascending"
  • processing:level "L2"
  • processing:lineage "systematic"
  • processing:version "004"
  • product:timeliness "PT3H"
  • sat:absolute_orbit 41054
  • sat:relative_orbit 357
  • processing:software

    • PUG "03.50"
    • EOPF-CPM "2.6.2"
  • product:timeliness_category "NR"
  • sat:platform_international_designator "2018-039A"
• links[] 5 items

  • 0

    • rel "collection"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-3-slstr-l2-lst"
    • type "application/json"

  • 1

    • rel "parent"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-3-slstr-l2-lst"
    • type "application/json"

  • 2

    • rel "root"
    • href "https://stac.core.eopf.eodc.eu"
    • type "application/json"
    • title "EOPF Sentinel Zarr Samples Service STAC API"

  • 3

    • rel "self"
    • href "https://stac.core.eopf.eodc.eu/collections/sentinel-3-slstr-l2-lst/items/S3B_SL_2_LST____20260313T212343_20260313T212643_20260313T235949_0179_117_357_0720_ESA_O_NR_004"
    • type "application/geo+json"

  • 4

    • rel "license"
    • href "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice"
    • type "application/pdf"
    • title "Legal notice on the use of Copernicus Sentinel Data and Service Information"
• assets

  • lst

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03slslst-eu/13/products/cpm_v262/S3B_SL_2_LST____20260313T212343_20260313T212643_20260313T235949_0179_117_357_0720_ESA_O_NR_004.zarr/measurements"
    • type "application/vnd+zarr"
    • title "Land Surface Temperature (LST)"
    • description "Gridded Land Surface Temperature generated on the wide 1 km measurement grid"
    • gsd 1000
    • bands[] 3 items

      • 0

        • name "S7"
        • eo:center_wavelength 3742
        • eo:full_width_half_max 398

      • 1

        • name "S8"
        • eo:center_wavelength 10854
        • eo:full_width_half_max 776

      • 2

        • name "S9"
        • eo:center_wavelength 12022.5
        • eo:full_width_half_max 905
    • roles[] 2 items

      • 0 "data"

      • 1 "dataset"
  • product

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03slslst-eu/13/products/cpm_v262/S3B_SL_2_LST____20260313T212343_20260313T212643_20260313T235949_0179_117_357_0720_ESA_O_NR_004.zarr"
    • type "application/vnd+zarr"
    • title "EOPF Product"
    • description "The full Zarr store of the EOPF product"
    • xarray:open_datatree_kwargs

      • chunks
      • engine "eopf-zarr"
      • op_mode "native"
    • roles[] 2 items

      • 0 "data"

      • 1 "metadata"
  • zipped_product

    • href "https://download.user.eopf.eodc.eu/zip/collections/sentinel-3-slstr-l2-lst/items/S3B_SL_2_LST____20260313T212343_20260313T212643_20260313T235949_0179_117_357_0720_ESA_O_NR_004.zip"
    • type "application/zip"
    • title "Zipped EOPF Product"
    • description "The full EOPF Zarr store as zip archive"
    • roles[] 3 items

      • 0 "data"

      • 1 "metadata"

      • 2 "archive"
  • product_metadata

    • href "https://objects.eodc.eu:443/e05ab01a9d56408d82ac32d69a5aae2a:202603-s03slslst-eu/13/products/cpm_v262/S3B_SL_2_LST____20260313T212343_20260313T212643_20260313T235949_0179_117_357_0720_ESA_O_NR_004.zarr/.zmetadata"
    • type "application/json"
    • title "Consolidated Metadata"
    • description "Consolidated metadata of the EOPF product"
    • roles[] 1 items

      • 0 "metadata"
• collection "sentinel-3-slstr-l2-lst"

Open Sentinel-3 SLSTR Level-2 LST as DataTree¶

We can use the "product" asset to obtain the href and xarray:open_datatree_kwargs from the STAC item, and open the product as an xarray.DataTree as shown below:

In [19]:

dt = xr.open_datatree(
    item.assets["product"].href,
    **item.assets["product"].extra_fields["xarray:open_datatree_kwargs"]
)
dt

dt = xr.open_datatree( item.assets["product"].href, **item.assets["product"].extra_fields["xarray:open_datatree_kwargs"] ) dt

Out[19]:

<xarray.DatasetView> Size: 0B
Dimensions:  ()
Data variables:
    *empty*
Attributes: (3)

xarray.DataTree

• Groups: (3)
  <xarray.DatasetView> Size: 0B
  Dimensions:  ()
  Data variables:
      *empty*

  conditions

  • Groups: (5)
    <xarray.DatasetView> Size: 59MB
    Dimensions:     (rows: 1200, columns: 1500, nval: 256)
    Dimensions without coordinates: rows, columns, nval
    Data variables:
        biome       (rows, columns) uint8 2MB dask.array<chunksize=(601, 750), meta=np.ndarray>
        elevation   (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
        fraction    (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
        ndvi        (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
        tcwv        (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
        validation  (nval) int8 256B dask.array<chunksize=(256,), meta=np.ndarray>

    auxiliary

    • Groups: (1)
      <xarray.DatasetView> Size: 7MB
      Dimensions:    (rows: 1200, columns: 1500, nval: 256, orphan_pixels: 187)
      Dimensions without coordinates: rows, columns, nval, orphan_pixels
      Data variables:
          biome      (rows, orphan_pixels) uint8 224kB dask.array<chunksize=(601, 187), meta=np.ndarray>
          elevation  (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>
          fraction   (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>
          ndvi       (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>
          tcwv       (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>

      orphan

      • Groups: (0)
      • Dimensions:
        • rows: 1200
        • columns: 1500
        • nval: 256
        • orphan_pixels: 187
      • Coordinates: (0)
      • Data variables: (5)
        • biome
          (rows, orphan_pixels)
          uint8
          dask.array<chunksize=(601, 187), meta=np.ndarray>

          _eopf_attrs :

          {'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<u1', 'flag_meanings': '0', 'flag_values': 0, 'long_name': 'ungridded globcover surface classification code', 'short_name': 'biome_auxiliary_orphan'}

          dtype :

          <u1

          flag_meanings :

          0

          flag_values :

          0

          long_name :

          ungridded globcover surface classification code

          Array Chunk Bytes 219.14 kiB 109.75 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type uint8 numpy.ndarray

        • elevation
          (rows, orphan_pixels)
          float64
          dask.array<chunksize=(601, 187), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': 0.0, 'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'surface elevation of detector FOV centre above reference ellipsoid', 'scale_factor': 1.0, 'short_name': 'elevation_auxiliary_orphan', 'standard_name': 'surface_altitude', 'units': 'm', 'valid_max': 9000, 'valid_min': -450}

          dtype :

          <i2

          long_name :

          surface elevation of detector FOV centre above reference ellipsoid

          standard_name :

          surface_altitude

          units :

          m

          Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

        • fraction
          (rows, orphan_pixels)
          float64
          dask.array<chunksize=(601, 187), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': 0.5, 'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'ungridded fractional vegetation cover', 'scale_factor': 1.9999999494757503e-05, 'short_name': 'fraction_auxiliary_orphan', 'standard_name': 'vegetation_area_fraction', 'valid_max': 25000, 'valid_min': -25000}

          dtype :

          <i2

          long_name :

          ungridded fractional vegetation cover

          standard_name :

          vegetation_area_fraction

          Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

        • ndvi
          (rows, orphan_pixels)
          float64
          dask.array<chunksize=(601, 187), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': 0.0, 'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'Ungridded Normalized Difference Vegetation Index', 'scale_factor': 4.999999873689376e-05, 'short_name': 'ndvi_auxiliary_orphan', 'standard_name': 'normalized_difference_vegetation_index', 'valid_max': 25000, 'valid_min': -25000}

          dtype :

          <i2

          long_name :

          Ungridded Normalized Difference Vegetation Index

          standard_name :

          normalized_difference_vegetation_index

          Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

        • tcwv
          (rows, orphan_pixels)
          float64
          dask.array<chunksize=(601, 187), meta=np.ndarray>

          _eopf_attrs :

          {'add_offset': 30.0, 'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'Ungridded Total Column Water Vapour', 'scale_factor': 0.0010000000474974513, 'short_name': 'tcwv_auxiliary_orphan', 'standard_name': 'atmosphere_mass_content_of_water_vapor', 'units': 'kg m-2', 'valid_max': 32767, 'valid_min': -30000}

          dtype :

          <i2

          long_name :

          Ungridded Total Column Water Vapour

          standard_name :

          atmosphere_mass_content_of_water_vapor

          units :

          kg m-2

          Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • Attributes: (0)

    • Dimensions:
      • rows: 1200
      • columns: 1500
      • nval: 256
    • Coordinates: (0)
    • Data variables: (6)
      • biome
        (rows, columns)
        uint8
        dask.array<chunksize=(601, 750), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'flag_meanings': 'open_ocean irrigated_cropland rainfed_cropland mosaic_cropland mosaic_vegetation broadleaved_evergreen_forest closed_broadleaved_deciduous_forrest open_broadleaved_deciduous_forest closed_needleleaved_forest open_needleleaved_forest mixed_forest mosaic_forest mosaic_grassland shrubland grassland sparse_vegetation freshwater_flooded_forest saltwater_flooded_forest flooded_vegetation artificial_surface bare_area_unknown bare_area_orthents bare_area_sand bare_area_calcids bare_area_cambids bare_area_orthels water snow_and_ice unfilled', 'flag_values': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28], 'long_name': 'gridded globcover surface classification code'}

        dtype :

        <u1

        flag_meanings :

        open_ocean irrigated_cropland rainfed_cropland mosaic_cropland mosaic_vegetation broadleaved_evergreen_forest closed_broadleaved_deciduous_forrest open_broadleaved_deciduous_forest closed_needleleaved_forest open_needleleaved_forest mixed_forest mosaic_forest mosaic_grassland shrubland grassland sparse_vegetation freshwater_flooded_forest saltwater_flooded_forest flooded_vegetation artificial_surface bare_area_unknown bare_area_orthents bare_area_sand bare_area_calcids bare_area_cambids bare_area_orthels water snow_and_ice unfilled

        flag_values :

        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]

        long_name :

        gridded globcover surface classification code

        Array Chunk Bytes 1.72 MiB 440.19 kiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type uint8 numpy.ndarray

      • elevation
        (rows, columns)
        float64
        dask.array<chunksize=(601, 750), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f8', 'fill_value': -32768, 'long_name': 'surface elevation of detector FOV centre above reference ellipsoid', 'scale_factor': 1.0, 'short_name': 'elevation', 'standard_name': 'surface_altitude', 'units': 'm', 'valid_max': 9000, 'valid_min': -450}

        dtype :

        <i2

        long_name :

        surface elevation of detector FOV centre above reference ellipsoid

        standard_name :

        surface_altitude

        units :

        m

        Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • fraction
        (rows, columns)
        float64
        dask.array<chunksize=(601, 750), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.5, 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'gridded fractional vegetation cover', 'scale_factor': 1.9999999494757503e-05, 'short_name': 'fraction', 'standard_name': 'vegetation_area_fraction', 'valid_max': 25000, 'valid_min': -25000}

        dtype :

        <i2

        long_name :

        gridded fractional vegetation cover

        standard_name :

        vegetation_area_fraction

        Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • ndvi
        (rows, columns)
        float64
        dask.array<chunksize=(601, 750), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'Gridded Normalized Difference Vegetation Index', 'scale_factor': 4.999999873689376e-05, 'short_name': 'ndvi', 'standard_name': 'normalized_difference_vegetation_index', 'valid_max': 25000, 'valid_min': -25000}

        dtype :

        <i2

        long_name :

        Gridded Normalized Difference Vegetation Index

        standard_name :

        normalized_difference_vegetation_index

        Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • tcwv
        (rows, columns)
        float64
        dask.array<chunksize=(601, 750), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 30.0, 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'Gridded Total Column Water Vapour', 'scale_factor': 0.0010000000474974513, 'short_name': 'tcwv', 'standard_name': 'atmosphere_mass_content_of_water_vapor', 'units': 'kg m-2', 'valid_max': 32767, 'valid_min': -30000}

        dtype :

        <i2

        long_name :

        Gridded Total Column Water Vapour

        standard_name :

        atmosphere_mass_content_of_water_vapor

        units :

        kg m-2

        Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

      • validation
        (nval)
        int8
        dask.array<chunksize=(256,), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['nval'], 'dtype': '<i1', 'flag_meanings': 'not_recognised not_validated limited_validation fully_validated', 'flag_values': [-1, 0, 1, 2], 'long_name': 'LST validation status flag for biome class', 'short_name': 'validation'}

        dtype :

        <i1

        flag_meanings :

        not_recognised not_validated limited_validation fully_validated

        flag_values :

        [-1, 0, 1, 2]

        long_name :

        LST validation status flag for biome class

        Array Chunk Bytes 256 B 256 B Shape (256,) (256,) Dask graph 1 chunks in 2 graph layers Data type int8 numpy.ndarray

    • Attributes: (0)

    <xarray.DatasetView> Size: 12MB
    Dimensions:           (rows: 1200, columns: 130)
    Coordinates:
      * columns           (columns) int64 1kB 0 1 2 3 4 5 ... 125 126 127 128 129
        latitude          (rows, columns) float64 1MB dask.array<chunksize=(600, 130), meta=np.ndarray>
        longitude         (rows, columns) float64 1MB dask.array<chunksize=(600, 130), meta=np.ndarray>
      * rows              (rows) int64 10kB 0 1 2 3 4 5 ... 1195 1196 1197 1198 1199
        x                 (rows, columns) float64 1MB dask.array<chunksize=(600, 130), meta=np.ndarray>
        y                 (rows, columns) float64 1MB dask.array<chunksize=(600, 130), meta=np.ndarray>
    Data variables:
        sat_azimuth_tn    (rows, columns) float64 1MB dask.array<chunksize=(600, 130), meta=np.ndarray>
        sat_path_tn       (rows, columns) float64 1MB dask.array<chunksize=(600, 130), meta=np.ndarray>
        sat_zenith_tn     (rows, columns) float64 1MB dask.array<chunksize=(600, 130), meta=np.ndarray>
        solar_azimuth_tn  (rows, columns) float64 1MB dask.array<chunksize=(600, 130), meta=np.ndarray>
        solar_path_tn     (rows, columns) float64 1MB dask.array<chunksize=(600, 130), meta=np.ndarray>
        solar_zenith_tn   (rows, columns) float64 1MB dask.array<chunksize=(600, 130), meta=np.ndarray>

    geometry

    • Groups: (0)
    • Dimensions:
      • rows: 1200
      • columns: 130
    • Coordinates: (6)
      • columns
        (columns)
        int64
        0 1 2 3 4 5 ... 125 126 127 128 129

        dimensions :

        ['columns']

        dtype :

        <i8

        long_name :

        columns of meteo grid

        array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
                14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
                28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
                42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
                56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
                70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
                84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
                98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
               112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
               126, 127, 128, 129])

      • latitude
        (rows, columns)
        float64
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <f8

        eopf_is_masked :

        True

        fill_value :

        1e+20

        long_name :

        latitude of detector FOV centre on the earth's surface

        standard_name :

        latitude

        units :

        degrees_north

        valid_max :

        90.0

        valid_min :

        -90.0

        Array Chunk Bytes 1.19 MiB 609.38 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • longitude
        (rows, columns)
        float64
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <f8

        eopf_is_masked :

        True

        fill_value :

        1e+20

        long_name :

        longitude of detector FOV centre on the earth's surface

        standard_name :

        longitude

        units :

        degrees_east

        valid_max :

        180.0

        valid_min :

        -180.0

        Array Chunk Bytes 1.19 MiB 609.38 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • rows
        (rows)
        int64
        0 1 2 3 4 ... 1196 1197 1198 1199

        dimensions :

        ['rows']

        dtype :

        <i8

        long_name :

        rows of meteo grid

        array([   0,    1,    2, ..., 1197, 1198, 1199], shape=(1200,))

      • x
        (rows, columns)
        float64
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <f8

        long_name :

        geolocated x (across track) coordinate of detector FOV centre

        units :

        m

        Array Chunk Bytes 1.19 MiB 609.38 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • y
        (rows, columns)
        float64
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <f8

        long_name :

        geolocated y (along track) coordinate of detector FOV centre

        units :

        m

        Array Chunk Bytes 1.19 MiB 609.38 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

    • Data variables: (6)
      • sat_azimuth_tn
        (rows, columns)
        float64
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f8', 'eopf_is_masked': True, 'long_name': 'satellite azimuth angle', 'short_name': 'sat_azimuth_tn', 'standard_name': 'satellite_azimuth_angle', 'units': 'degrees'}

        dtype :

        <f8

        long_name :

        satellite azimuth angle

        standard_name :

        satellite_azimuth_angle

        units :

        degrees

        Array Chunk Bytes 1.19 MiB 609.38 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • sat_path_tn
        (rows, columns)
        float64
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f8', 'eopf_is_masked': True, 'long_name': 'distance from satellite to surface', 'short_name': 'sat_path_tn', 'units': 'm'}

        dtype :

        <f8

        long_name :

        distance from satellite to surface

        units :

        m

        Array Chunk Bytes 1.19 MiB 609.38 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • sat_zenith_tn
        (rows, columns)
        float64
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f8', 'eopf_is_masked': True, 'long_name': 'satellite zenith angle', 'short_name': 'sat_zenith_tn', 'standard_name': 'satellite_zenith_angle', 'units': 'degrees'}

        dtype :

        <f8

        long_name :

        satellite zenith angle

        standard_name :

        satellite_zenith_angle

        units :

        degrees

        Array Chunk Bytes 1.19 MiB 609.38 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • solar_azimuth_tn
        (rows, columns)
        float64
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f8', 'eopf_is_masked': True, 'long_name': 'solar azimuth angle', 'short_name': 'solar_azimuth_tn', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}

        dtype :

        <f8

        long_name :

        solar azimuth angle

        standard_name :

        solar_azimuth_angle

        units :

        degrees

        Array Chunk Bytes 1.19 MiB 609.38 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • solar_path_tn
        (rows, columns)
        float64
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f8', 'eopf_is_masked': True, 'long_name': 'distance from sun to surface', 'short_name': 'solar_path_tn', 'units': 'm'}

        dtype :

        <f8

        long_name :

        distance from sun to surface

        units :

        m

        Array Chunk Bytes 1.19 MiB 609.38 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • solar_zenith_tn
        (rows, columns)
        float64
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f8', 'eopf_is_masked': True, 'long_name': 'solar zenith angle', 'short_name': 'solar_zenith_tn', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}

        dtype :

        <f8

        long_name :

        solar zenith angle

        standard_name :

        solar_zenith_angle

        units :

        degrees

        Array Chunk Bytes 1.19 MiB 609.38 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

    • Attributes: (0)

    <xarray.DatasetView> Size: 69MB
    Dimensions:                       (rows: 1200, columns: 130, t_series: 5,
                                       p_atmos: 25)
    Coordinates:
      * columns                       (columns) int64 1kB 0 1 2 3 ... 127 128 129
        latitude                      (rows, columns) float64 1MB dask.array<chunksize=(600, 130), meta=np.ndarray>
        longitude                     (rows, columns) float64 1MB dask.array<chunksize=(600, 130), meta=np.ndarray>
      * p_atmos                       (p_atmos) int16 50B 1 2 3 5 ... 925 950 1000
      * rows                          (rows) int64 10kB 0 1 2 3 ... 1197 1198 1199
      * t_series                      (t_series) datetime64[ns] 40B 2026-03-13T03...
        x                             (rows, columns) float64 1MB dask.array<chunksize=(600, 130), meta=np.ndarray>
        y                             (rows, columns) float64 1MB dask.array<chunksize=(600, 130), meta=np.ndarray>
    Data variables: (12/22)
        cloud_fraction_tp             (rows, columns) float32 624kB dask.array<chunksize=(600, 130), meta=np.ndarray>
        dew_point_tp                  (rows, columns) float32 624kB dask.array<chunksize=(600, 130), meta=np.ndarray>
        east_west_stress_tp           (t_series, rows, columns) float32 3MB dask.array<chunksize=(5, 600, 130), meta=np.ndarray>
        latent_heat_tp                (t_series, rows, columns) float32 3MB dask.array<chunksize=(5, 600, 130), meta=np.ndarray>
        north_south_stress_tp         (t_series, rows, columns) float32 3MB dask.array<chunksize=(5, 600, 130), meta=np.ndarray>
        sea_ice_fraction_tp           (rows, columns) float32 624kB dask.array<chunksize=(600, 130), meta=np.ndarray>
        ...                            ...
        temperature_tp                (rows, columns) float32 624kB dask.array<chunksize=(600, 130), meta=np.ndarray>
        thermal_radiation_tp          (t_series, rows, columns) float32 3MB dask.array<chunksize=(5, 600, 130), meta=np.ndarray>
        total_column_ozone_tp         (rows, columns) float32 624kB dask.array<chunksize=(600, 130), meta=np.ndarray>
        total_column_water_vapour_tp  (rows, columns) float32 624kB dask.array<chunksize=(600, 130), meta=np.ndarray>
        u_wind_tp                     (t_series, rows, columns) float32 3MB dask.array<chunksize=(3, 600, 130), meta=np.ndarray>
        v_wind_tp                     (t_series, rows, columns) float32 3MB dask.array<chunksize=(3, 600, 130), meta=np.ndarray>

    meteorology

    • Groups: (0)
    • Dimensions:
      • rows: 1200
      • columns: 130
      • t_series: 5
      • p_atmos: 25
    • Coordinates: (8)
      • columns
        (columns)
        int64
        0 1 2 3 4 5 ... 125 126 127 128 129

        dimensions :

        ['columns']

        dtype :

        <i8

        long_name :

        columns of meteo grid

        array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
                14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
                28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
                42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
                56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
                70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
                84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
                98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
               112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
               126, 127, 128, 129])

      • latitude
        (rows, columns)
        float64
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <f8

        eopf_is_masked :

        True

        fill_value :

        1e+20

        long_name :

        latitude of detector FOV centre on the earth's surface

        standard_name :

        latitude

        units :

        degrees_north

        valid_max :

        90.0

        valid_min :

        -90.0

        Array Chunk Bytes 1.19 MiB 609.38 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • longitude
        (rows, columns)
        float64
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <f8

        eopf_is_masked :

        True

        fill_value :

        1e+20

        long_name :

        longitude of detector FOV centre on the earth's surface

        standard_name :

        longitude

        units :

        degrees_east

        valid_max :

        180.0

        valid_min :

        -180.0

        Array Chunk Bytes 1.19 MiB 609.38 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • p_atmos
        (p_atmos)
        int16
        1 2 3 5 7 ... 850 900 925 950 1000

        axis :

        Z

        dimensions :

        ['p_atmos']

        dtype :

        <i2

        long_name :

        atmospheric pressure level coordinates

        standard_name :

        air_pressure

        units :

        hPa

        array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,
                200,  250,  300,  400,  500,  600,  700,  800,  850,  900,  925,  950,
               1000], dtype=int16)

      • rows
        (rows)
        int64
        0 1 2 3 4 ... 1196 1197 1198 1199

        dimensions :

        ['rows']

        dtype :

        <i8

        long_name :

        rows of meteo grid

        array([   0,    1,    2, ..., 1197, 1198, 1199], shape=(1200,))

      • t_series
        (t_series)
        datetime64[ns]
        2026-03-13T03:00:00 ... 2026-03-...

        axis :

        T

        bounds :

        t_bound

        dimensions :

        ['t_series']

        dtype :

        <M8[ns]

        long_name :

        synoptic times of time series meteo fields

        standard_name :

        time

        array(['2026-03-13T03:00:00.000000000', '2026-03-13T09:00:00.000000000',
               '2026-03-13T15:00:00.000000000', '2026-03-13T21:00:00.000000000',
               '2026-03-14T03:00:00.000000000'], dtype='datetime64[ns]')

      • x
        (rows, columns)
        float64
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <f8

        long_name :

        geolocated x (across track) coordinate of detector FOV centre

        units :

        m

        Array Chunk Bytes 1.19 MiB 609.38 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • y
        (rows, columns)
        float64
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        dimensions :

        ['rows', 'columns']

        dtype :

        <f8

        long_name :

        geolocated y (along track) coordinate of detector FOV centre

        units :

        m

        Array Chunk Bytes 1.19 MiB 609.38 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

    • Data variables: (22)
      • cloud_fraction_tp
        (rows, columns)
        float32
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'fractional cloud cover from ECMWF parameter 164 at synoptic time', 'model': 'ECMWF_F', 'parameter': '164', 'short_name': 'cloud_fraction_tp', 'standard_name': 'cloud_area_fraction'}

        dtype :

        <f4

        long_name :

        fractional cloud cover from ECMWF parameter 164 at synoptic time

        standard_name :

        cloud_area_fraction

        Array Chunk Bytes 609.38 kiB 304.69 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • dew_point_tp
        (rows, columns)
        float32
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': '2m dew point from ECMWF parameter 168 at synoptic time', 'model': 'ECMWF_F', 'parameter': '168', 'short_name': 'dew_point_tp', 'standard_name': 'dew_point_temperature', 'units': 'K'}

        dtype :

        <f4

        long_name :

        2m dew point from ECMWF parameter 168 at synoptic time

        standard_name :

        dew_point_temperature

        units :

        K

        Array Chunk Bytes 609.38 kiB 304.69 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • east_west_stress_tp
        (t_series, rows, columns)
        float32
        dask.array<chunksize=(5, 600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude', 't_series'], 'dimensions': ['t_series', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'east-west integrated surface wind stress from ECMWF parameter 180 at synoptic times', 'model': 'ECMWF_F', 'parameter': '180', 'short_name': 'east_west_stress_tp', 'standard_name': 'integral_of_surface_downward_eastward_stress_wrt_time', 'units': 'N m-2 s'}

        dtype :

        <f4

        long_name :

        east-west integrated surface wind stress from ECMWF parameter 180 at synoptic times

        standard_name :

        integral_of_surface_downward_eastward_stress_wrt_time

        units :

        N m-2 s

        Array Chunk Bytes 2.98 MiB 1.49 MiB Shape (5, 1200, 130) (5, 600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • latent_heat_tp
        (t_series, rows, columns)
        float32
        dask.array<chunksize=(5, 600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude', 't_series'], 'dimensions': ['t_series', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'integrated surface latent heat flux from ECMWF parameter 147 at synoptic times', 'model': 'ECMWF_F', 'parameter': '147', 'short_name': 'latent_heat_tp', 'standard_name': 'integral_of_surface_downward_latent_heat_flux_wrt_time', 'units': 'W m-2 s'}

        dtype :

        <f4

        long_name :

        integrated surface latent heat flux from ECMWF parameter 147 at synoptic times

        standard_name :

        integral_of_surface_downward_latent_heat_flux_wrt_time

        units :

        W m-2 s

        Array Chunk Bytes 2.98 MiB 1.49 MiB Shape (5, 1200, 130) (5, 600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • north_south_stress_tp
        (t_series, rows, columns)
        float32
        dask.array<chunksize=(5, 600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude', 't_series'], 'dimensions': ['t_series', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'north-south integrated surface wind stress from ECMWF parameter 181 at synoptic times', 'model': 'ECMWF_F', 'parameter': '181', 'short_name': 'north_south_stress_tp', 'standard_name': 'integral_of_surface_downward_northward_stress_wrt_time', 'units': 'N m-2 s'}

        dtype :

        <f4

        long_name :

        north-south integrated surface wind stress from ECMWF parameter 181 at synoptic times

        standard_name :

        integral_of_surface_downward_northward_stress_wrt_time

        units :

        N m-2 s

        Array Chunk Bytes 2.98 MiB 1.49 MiB Shape (5, 1200, 130) (5, 600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • sea_ice_fraction_tp
        (rows, columns)
        float32
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'sea ice fraction from ECMWF parameter 31 at synoptic time', 'model': 'ECMWF_F', 'parameter': '31', 'short_name': 'sea_ice_fraction_tp', 'standard_name': 'sea_ice_fraction'}

        dtype :

        <f4

        long_name :

        sea ice fraction from ECMWF parameter 31 at synoptic time

        standard_name :

        sea_ice_fraction

        Array Chunk Bytes 609.38 kiB 304.69 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • sea_surface_temperature_tp
        (rows, columns)
        float32
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'sea surface temperature from ECMWF parameter 34 at synoptic time', 'model': 'ECMWF_F', 'parameter': '34', 'short_name': 'sea_surface_temperature_tp', 'standard_name': 'sea_surface_temperature', 'units': 'K'}

        dtype :

        <f4

        long_name :

        sea surface temperature from ECMWF parameter 34 at synoptic time

        standard_name :

        sea_surface_temperature

        units :

        K

        Array Chunk Bytes 609.38 kiB 304.69 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • sensible_heat_tp
        (t_series, rows, columns)
        float32
        dask.array<chunksize=(5, 600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude', 't_series'], 'dimensions': ['t_series', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'integrated surface sensible heat flux from ECMWF parameter 146 at synoptic times', 'model': 'ECMWF_F', 'parameter': '146', 'short_name': 'sensible_heat_tp', 'standard_name': 'integral_of_surface_downward_sensible_heat_flux_wrt_time', 'units': 'W m-2 s'}

        dtype :

        <f4

        long_name :

        integrated surface sensible heat flux from ECMWF parameter 146 at synoptic times

        standard_name :

        integral_of_surface_downward_sensible_heat_flux_wrt_time

        units :

        W m-2 s

        Array Chunk Bytes 2.98 MiB 1.49 MiB Shape (5, 1200, 130) (5, 600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • skin_temperature_tp
        (rows, columns)
        float32
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'skin temperature from ECMWF parameter 235 at synoptic time', 'model': 'ECMWF_F', 'parameter': '235', 'short_name': 'skin_temperature_tp', 'standard_name': 'surface_temperature', 'units': 'K'}

        dtype :

        <f4

        long_name :

        skin temperature from ECMWF parameter 235 at synoptic time

        standard_name :

        surface_temperature

        units :

        K

        Array Chunk Bytes 609.38 kiB 304.69 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • snow_albedo_tp
        (rows, columns)
        float32
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'snow albedo from ECMWF parameter 32 at synoptic time', 'model': 'ECMWF_F', 'parameter': '32', 'short_name': 'snow_albedo_tp', 'standard_name': 'snow_albedo'}

        dtype :

        <f4

        long_name :

        snow albedo from ECMWF parameter 32 at synoptic time

        standard_name :

        snow_albedo

        Array Chunk Bytes 609.38 kiB 304.69 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • snow_depth_tp
        (rows, columns)
        float32
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'snow liquid water equivalent thickness from ECMWF parameter 141 at synoptic time', 'model': 'ECMWF_F', 'parameter': '141', 'standard_name': 'lwe_thickness_of_surface_snow_amount', 'units': 'metre'}

        dtype :

        <f4

        long_name :

        snow liquid water equivalent thickness from ECMWF parameter 141 at synoptic time

        standard_name :

        lwe_thickness_of_surface_snow_amount

        units :

        metre

        Array Chunk Bytes 609.38 kiB 304.69 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • soil_wetness_tp
        (rows, columns)
        float32
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'volumetric soil water layer 1 from ECMWF parameter 39 at synoptic time', 'model': 'ECMWF_F', 'parameter': '39', 'short_name': 'soil_wetness_tp', 'standard_name': 'moisture_content_of_soil_layer', 'units': 'm'}

        dtype :

        <f4

        long_name :

        volumetric soil water layer 1 from ECMWF parameter 39 at synoptic time

        standard_name :

        moisture_content_of_soil_layer

        units :

        m

        Array Chunk Bytes 609.38 kiB 304.69 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • solar_radiation_tp
        (t_series, rows, columns)
        float32
        dask.array<chunksize=(5, 600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude', 't_series'], 'dimensions': ['t_series', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'integrated surface solar radiation from ECMWF parameter 176 at synoptic times', 'model': 'ECMWF_F', 'parameter': '176', 'short_name': 'solar_radiation_tp', 'standard_name': 'integral_of_surface_net_downward_shortwave_flux_wrt_time', 'units': 'W m-2 s'}

        dtype :

        <f4

        long_name :

        integrated surface solar radiation from ECMWF parameter 176 at synoptic times

        standard_name :

        integral_of_surface_net_downward_shortwave_flux_wrt_time

        units :

        W m-2 s

        Array Chunk Bytes 2.98 MiB 1.49 MiB Shape (5, 1200, 130) (5, 600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • specific_humidity_tp
        (p_atmos, rows, columns)
        float32
        dask.array<chunksize=(13, 600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude', 'p_atmos'], 'dimensions': ['p_atmos', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'specific humidity vertical profile from ECMWF parameter 133 at synoptic time', 'model': 'ECMWF_F', 'parameter': '133', 'short_name': 'specific_humidity_tp', 'standard_name': 'specific_humidity', 'units': 'kg/kg'}

        dtype :

        <f4

        long_name :

        specific humidity vertical profile from ECMWF parameter 133 at synoptic time

        standard_name :

        specific_humidity

        units :

        kg/kg

        Array Chunk Bytes 14.88 MiB 3.87 MiB Shape (25, 1200, 130) (13, 600, 130) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

      • surface_pressure_tp
        (rows, columns)
        float32
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'surface atmospheric pressure from ECMWF parameter 134 at synoptic time', 'model': 'ECMWF_F', 'parameter': '134', 'short_name': 'surface_pressure_tp', 'standard_name': 'surface_air_pressure', 'units': 'hPa'}

        dtype :

        <f4

        long_name :

        surface atmospheric pressure from ECMWF parameter 134 at synoptic time

        standard_name :

        surface_air_pressure

        units :

        hPa

        Array Chunk Bytes 609.38 kiB 304.69 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • temperature_profile_tp
        (p_atmos, rows, columns)
        float32
        dask.array<chunksize=(13, 600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude', 'p_atmos'], 'dimensions': ['p_atmos', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'atmospheric temperature vertical profile from ECMWF parameter 130 at synoptic time', 'model': 'ECMWF_F', 'parameter': '130', 'short_name': 'temperature_profile_tp', 'standard_name': 'air_temperature', 'units': 'K'}

        dtype :

        <f4

        long_name :

        atmospheric temperature vertical profile from ECMWF parameter 130 at synoptic time

        standard_name :

        air_temperature

        units :

        K

        Array Chunk Bytes 14.88 MiB 3.87 MiB Shape (25, 1200, 130) (13, 600, 130) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

      • temperature_tp
        (rows, columns)
        float32
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': '2m air temperature from ECMWF parameter 167 at synoptic time', 'model': 'ECMWF_F', 'parameter': '167', 'short_name': 'temperature_tp', 'standard_name': 'air_temperature', 'units': 'K'}

        dtype :

        <f4

        long_name :

        2m air temperature from ECMWF parameter 167 at synoptic time

        standard_name :

        air_temperature

        units :

        K

        Array Chunk Bytes 609.38 kiB 304.69 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • thermal_radiation_tp
        (t_series, rows, columns)
        float32
        dask.array<chunksize=(5, 600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude', 't_series'], 'dimensions': ['t_series', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'integrated surface thermal radiation from ECMWF parameter 177 at synoptic times', 'model': 'ECMWF_F', 'parameter': '177', 'short_name': 'thermal_radiation_tp', 'standard_name': 'integral_of_surface_net_downward_longwave_flux_wrt_time', 'units': 'W m-2 s'}

        dtype :

        <f4

        long_name :

        integrated surface thermal radiation from ECMWF parameter 177 at synoptic times

        standard_name :

        integral_of_surface_net_downward_longwave_flux_wrt_time

        units :

        W m-2 s

        Array Chunk Bytes 2.98 MiB 1.49 MiB Shape (5, 1200, 130) (5, 600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • total_column_ozone_tp
        (rows, columns)
        float32
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'total atmospheric column ozone from ECMWF parameter 206 at synoptic time', 'model': 'ECMWF_F', 'parameter': '206', 'short_name': 'total_column_ozone_tp', 'standard_name': 'atmosphere_mass_content_of_ozone', 'units': 'kg m-2'}

        dtype :

        <f4

        long_name :

        total atmospheric column ozone from ECMWF parameter 206 at synoptic time

        standard_name :

        atmosphere_mass_content_of_ozone

        units :

        kg m-2

        Array Chunk Bytes 609.38 kiB 304.69 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • total_column_water_vapour_tp
        (rows, columns)
        float32
        dask.array<chunksize=(600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': 'total column water vapour from ECMWF parameter 137 at synoptic time', 'model': 'ECMWF_F', 'parameter': '137', 'short_name': 'total_column_water_vapour_tp', 'standard_name': 'atmosphere_water_vapor_content', 'units': 'kg m-2'}

        dtype :

        <f4

        long_name :

        total column water vapour from ECMWF parameter 137 at synoptic time

        standard_name :

        atmosphere_water_vapor_content

        units :

        kg m-2

        Array Chunk Bytes 609.38 kiB 304.69 kiB Shape (1200, 130) (600, 130) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • u_wind_tp
        (t_series, rows, columns)
        float32
        dask.array<chunksize=(3, 600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude', 't_series'], 'dimensions': ['t_series', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': '10m U wind component from ECMWF parameter 165 at synoptic times', 'model': 'ECMWF_F', 'parameter': '165', 'short_name': 'u_wind_tp', 'standard_name': 'eastward_wind', 'units': 'm s-1'}

        dtype :

        <f4

        long_name :

        10m U wind component from ECMWF parameter 165 at synoptic times

        standard_name :

        eastward_wind

        units :

        m s-1

        Array Chunk Bytes 2.98 MiB 914.06 kiB Shape (5, 1200, 130) (3, 600, 130) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

      • v_wind_tp
        (t_series, rows, columns)
        float32
        dask.array<chunksize=(3, 600, 130), meta=np.ndarray>

        _eopf_attrs :

        {'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude', 't_series'], 'dimensions': ['t_series', 'rows', 'columns'], 'dtype': '<f4', 'eopf_is_masked': True, 'long_name': '10m V wind component from ECMWF parameter 166 at synoptic times', 'model': 'ECMWF_F', 'parameter': '166', 'short_name': 'v_wind_tp', 'standard_name': 'northward_wind', 'units': 'm s-1'}

        dtype :

        <f4

        long_name :

        10m V wind component from ECMWF parameter 166 at synoptic times

        standard_name :

        northward_wind

        units :

        m s-1

        Array Chunk Bytes 2.98 MiB 914.06 kiB Shape (5, 1200, 130) (3, 600, 130) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

    • Attributes: (0)

    <xarray.DatasetView> Size: 22MB
    Dimensions:   (rows: 1200, columns: 1500)
    Dimensions without coordinates: rows, columns
    Data variables:
        detector  (rows, columns) float32 7MB dask.array<chunksize=(601, 750), meta=np.ndarray>
        pixel     (rows, columns) float32 7MB dask.array<chunksize=(601, 750), meta=np.ndarray>
        scan      (rows, columns) float32 7MB dask.array<chunksize=(601, 750), meta=np.ndarray>

    processing

    • Groups: (1)
      <xarray.DatasetView> Size: 3MB
      Dimensions:   (rows: 1200, columns: 1500, orphan_pixels: 187)
      Dimensions without coordinates: rows, columns, orphan_pixels
      Data variables:
          detector  (rows, orphan_pixels) float32 898kB dask.array<chunksize=(601, 187), meta=np.ndarray>
          pixel     (rows, orphan_pixels) float32 898kB dask.array<chunksize=(601, 187), meta=np.ndarray>
          scan      (rows, orphan_pixels) float32 898kB dask.array<chunksize=(601, 187), meta=np.ndarray>

      orphan

      • Groups: (0)
      • Dimensions:
        • rows: 1200
        • columns: 1500
        • orphan_pixels: 187
      • Coordinates: (0)
      • Data variables: (3)
        • detector
          (rows, orphan_pixels)
          float32
          dask.array<chunksize=(601, 187), meta=np.ndarray>

          _eopf_attrs :

          {'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'gridded pixel detector number', 'short_name': 'detector_processing_orphan'}

          dtype :

          <u1

          long_name :

          gridded pixel detector number

          Array Chunk Bytes 876.56 kiB 439.01 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

        • pixel
          (rows, orphan_pixels)
          float32
          dask.array<chunksize=(601, 187), meta=np.ndarray>

          _eopf_attrs :

          {'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'gridded pixel number', 'short_name': 'pixel_processing_orphan'}

          dtype :

          <u2

          long_name :

          gridded pixel number

          Array Chunk Bytes 876.56 kiB 439.01 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

        • scan
          (rows, orphan_pixels)
          float32
          dask.array<chunksize=(601, 187), meta=np.ndarray>

          _eopf_attrs :

          {'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'pixel scan number', 'short_name': 'scan_processing_orphan'}

          dtype :

          <u2

          long_name :

          pixel scan number

          Array Chunk Bytes 876.56 kiB 439.01 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • Attributes: (0)

    • Dimensions:
      • rows: 1200
      • columns: 1500
    • Coordinates: (0)
    • Data variables: (3)
      • detector
        (rows, columns)
        float32
        dask.array<chunksize=(601, 750), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 255, 'long_name': 'gridded pixel detector number', 'short_name': 'detector'}

        dtype :

        <u1

        long_name :

        gridded pixel detector number

        Array Chunk Bytes 6.87 MiB 1.72 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

      • pixel
        (rows, columns)
        float32
        dask.array<chunksize=(601, 750), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'gridded pixel number', 'short_name': 'pixel'}

        dtype :

        <u2

        long_name :

        gridded pixel number

        Array Chunk Bytes 6.87 MiB 1.72 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

      • scan
        (rows, columns)
        float32
        dask.array<chunksize=(601, 750), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'pixel scan number', 'short_name': 'scan'}

        dtype :

        <u2

        long_name :

        pixel scan number

        Array Chunk Bytes 6.87 MiB 1.72 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float32 numpy.ndarray

    • Attributes: (0)

    <xarray.DatasetView> Size: 67kB
    Dimensions:               (rows: 1200)
    Dimensions without coordinates: rows
    Data variables:
        nadir_first_scan_i    (rows) float32 5kB dask.array<chunksize=(601,), meta=np.ndarray>
        nadir_last_scan_i     (rows) float32 5kB dask.array<chunksize=(601,), meta=np.ndarray>
        nadir_maximal_ts_i    (rows) datetime64[ns] 10kB dask.array<chunksize=(601,), meta=np.ndarray>
        nadir_minimal_ts_i    (rows) datetime64[ns] 10kB dask.array<chunksize=(601,), meta=np.ndarray>
        oblique_first_scan_i  (rows) float32 5kB dask.array<chunksize=(601,), meta=np.ndarray>
        oblique_last_scan_i   (rows) float32 5kB dask.array<chunksize=(601,), meta=np.ndarray>
        oblique_maximal_ts_i  (rows) datetime64[ns] 10kB dask.array<chunksize=(601,), meta=np.ndarray>
        oblique_minimal_ts_i  (rows) datetime64[ns] 10kB dask.array<chunksize=(601,), meta=np.ndarray>
        time_stamp_i          (rows) datetime64[ns] 10kB dask.array<chunksize=(601,), meta=np.ndarray>

    time

    • Groups: (0)
    • Dimensions:
      • rows: 1200
    • Coordinates: (0)
    • Data variables: (9)
      • nadir_first_scan_i
        (rows)
        float32
        dask.array<chunksize=(601,), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'scan number of the first nadir scan contributing to each line', 'short_name': 'nadir_first_scan_i', 'valid_max': 19353, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        scan number of the first nadir scan contributing to each line

        Array Chunk Bytes 4.69 kiB 2.35 kiB Shape (1200,) (601,) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • nadir_last_scan_i
        (rows)
        float32
        dask.array<chunksize=(601,), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'scan number of the last nadir scan contributing to each line', 'short_name': 'nadir_last_scan_i', 'valid_max': 19353, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        scan number of the last nadir scan contributing to each line

        Array Chunk Bytes 4.69 kiB 2.35 kiB Shape (1200,) (601,) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • nadir_maximal_ts_i
        (rows)
        datetime64[ns]
        dask.array<chunksize=(601,), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows'], 'dtype': '<M8[ns]', 'fill_value': -9223372036854775808, 'long_name': 'time at SCANSYNC of the last scan contributing to each nadir image row', 'short_name': 'nadir_maximal_ts_i', 'standard_name': 'time'}

        dtype :

        <M8[ns]

        long_name :

        time at SCANSYNC of the last scan contributing to each nadir image row

        standard_name :

        time

        Array Chunk Bytes 9.38 kiB 4.70 kiB Shape (1200,) (601,) Dask graph 2 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

      • nadir_minimal_ts_i
        (rows)
        datetime64[ns]
        dask.array<chunksize=(601,), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows'], 'dtype': '<M8[ns]', 'fill_value': -9223372036854775808, 'long_name': 'time at SCANSYNC of the first scan contributing to each nadir image row', 'short_name': 'nadir_minimal_ts_i', 'standard_name': 'time'}

        dtype :

        <M8[ns]

        long_name :

        time at SCANSYNC of the first scan contributing to each nadir image row

        standard_name :

        time

        Array Chunk Bytes 9.38 kiB 4.70 kiB Shape (1200,) (601,) Dask graph 2 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

      • oblique_first_scan_i
        (rows)
        float32
        dask.array<chunksize=(601,), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'scan number of the first oblique scan contributing to each line', 'short_name': 'oblique_first_scan_i', 'valid_max': 19353, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        scan number of the first oblique scan contributing to each line

        Array Chunk Bytes 4.69 kiB 2.35 kiB Shape (1200,) (601,) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • oblique_last_scan_i
        (rows)
        float32
        dask.array<chunksize=(601,), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows'], 'dtype': '<u2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': 65535, 'long_name': 'scan number of the last oblique scan contributing to each line', 'short_name': 'oblique_last_scan_i', 'valid_max': 19353, 'valid_min': 0}

        dtype :

        <u2

        long_name :

        scan number of the last oblique scan contributing to each line

        Array Chunk Bytes 4.69 kiB 2.35 kiB Shape (1200,) (601,) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

      • oblique_maximal_ts_i
        (rows)
        datetime64[ns]
        dask.array<chunksize=(601,), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows'], 'dtype': '<M8[ns]', 'fill_value': -9223372036854775808, 'long_name': 'time at SCANSYNC of the last scan contributing to each oblique image row', 'short_name': 'oblique_maximal_ts_i', 'standard_name': 'time'}

        dtype :

        <M8[ns]

        long_name :

        time at SCANSYNC of the last scan contributing to each oblique image row

        standard_name :

        time

        Array Chunk Bytes 9.38 kiB 4.70 kiB Shape (1200,) (601,) Dask graph 2 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

      • oblique_minimal_ts_i
        (rows)
        datetime64[ns]
        dask.array<chunksize=(601,), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows'], 'dtype': '<M8[ns]', 'fill_value': -9223372036854775808, 'long_name': 'time at SCANSYNC of the first scan contributing to each oblique image row', 'short_name': 'oblique_minimal_ts_i', 'standard_name': 'time'}

        dtype :

        <M8[ns]

        long_name :

        time at SCANSYNC of the first scan contributing to each oblique image row

        standard_name :

        time

        Array Chunk Bytes 9.38 kiB 4.70 kiB Shape (1200,) (601,) Dask graph 2 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

      • time_stamp_i
        (rows)
        datetime64[ns]
        dask.array<chunksize=(601,), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows'], 'dtype': '<M8[ns]', 'fill_value': -9223372036854775808, 'long_name': 'time at which the sub-satellite point crosses the image row', 'short_name': 'time_stamp_i', 'standard_name': 'time'}

        dtype :

        <M8[ns]

        long_name :

        time at which the sub-satellite point crosses the image row

        standard_name :

        time

        Array Chunk Bytes 9.38 kiB 4.70 kiB Shape (1200,) (601,) Dask graph 2 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

    • Attributes: (0)

  • Dimensions:
  • Coordinates: (0)
  • Data variables: (0)
  • Attributes: (0)

  <xarray.DatasetView> Size: 72MB
  Dimensions:    (rows: 1200, columns: 1500)
  Coordinates:
    * columns    (columns) int64 12kB 0 1 2 3 4 5 ... 1495 1496 1497 1498 1499
      latitude   (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
      longitude  (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
    * rows       (rows) int64 10kB 0 1 2 3 4 5 6 ... 1194 1195 1196 1197 1198 1199
      x          (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
      y          (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
  Data variables:
      lst        (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>

  measurements

  • Groups: (1)
    <xarray.DatasetView> Size: 9MB
    Dimensions:    (rows: 1200, columns: 1500, orphan_pixels: 187)
    Coordinates:
      * columns    (columns) int64 12kB 0 1 2 3 4 5 ... 1495 1496 1497 1498 1499
      * rows       (rows) int64 10kB 0 1 2 3 4 5 6 ... 1194 1195 1196 1197 1198 1199
        latitude   (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>
        longitude  (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>
        x          (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>
        y          (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>
    Dimensions without coordinates: orphan_pixels
    Data variables:
        lst        (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>

    orphan

    • Groups: (0)
    • Dimensions:
      • rows: 1200
      • columns: 1500
      • orphan_pixels: 187
    • Coordinates: (4)
      • latitude
        (rows, orphan_pixels)
        float64
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        dimensions :

        ['rows', 'orphan_pixels']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        latitude of detector FOV centre on the earth's surface

        standard_name :

        latitude

        units :

        degrees_north

        valid_max :

        90000000

        valid_min :

        -90000000

        Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • longitude
        (rows, orphan_pixels)
        float64
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        dimensions :

        ['rows', 'orphan_pixels']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -2147483648

        long_name :

        longitude of detector FOV centre on the earth's surface

        standard_name :

        longitude

        units :

        degrees_east

        valid_max :

        180000000

        valid_min :

        -180000000

        Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • x
        (rows, orphan_pixels)
        float64
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        dimensions :

        ['rows', 'orphan_pixels']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -1000000

        long_name :

        geolocated x (across track) coordinate of detector FOV centre

        units :

        m

        valid_max :

        1000000

        valid_min :

        -500000

        Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • y
        (rows, orphan_pixels)
        float64
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        dimensions :

        ['rows', 'orphan_pixels']

        dtype :

        <i4

        eopf_is_masked :

        True

        eopf_target_dtype :

        <f8

        fill_value :

        -5000000

        long_name :

        geolocated y (along track) coordinate of detector FOV centre

        units :

        m

        valid_max :

        50000000

        valid_min :

        -1000000

        Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

    • Data variables: (1)
      • lst
        (rows, orphan_pixels)
        float64
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 290.0, 'coordinates': ['x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'ungridded land surface temperature', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst_orphan', 'standard_name': 'surface_temperature', 'units': 'K', 'valid_max': 32767, 'valid_min': -32767}

        dtype :

        <i2

        long_name :

        ungridded land surface temperature

        standard_name :

        surface_temperature

        units :

        K

        Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

    • Attributes: (0)

  • Dimensions:
    • rows: 1200
    • columns: 1500
  • Coordinates: (6)
    • columns
      (columns)
      int64
      0 1 2 3 4 ... 1496 1497 1498 1499

      dimensions :

      ['columns']

      dtype :

      <i8

      long_name :

      columns of image nadir view

      array([   0,    1,    2, ..., 1497, 1498, 1499], shape=(1500,))

    • latitude
      (rows, columns)
      float64
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i4

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f8

      fill_value :

      -2147483648

      long_name :

      latitude of detector FOV centre on the earth's surface

      standard_name :

      latitude

      units :

      degrees_north

      valid_max :

      90000000

      valid_min :

      -90000000

      Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

    • longitude
      (rows, columns)
      float64
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i4

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f8

      fill_value :

      -2147483648

      long_name :

      longitude of detector FOV centre on the earth's surface

      standard_name :

      longitude

      units :

      degrees_east

      valid_max :

      180000000

      valid_min :

      -180000000

      Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

    • rows
      (rows)
      int64
      0 1 2 3 4 ... 1196 1197 1198 1199

      dimensions :

      ['rows']

      dtype :

      <i8

      long_name :

      rows of image nadir view

      array([   0,    1,    2, ..., 1197, 1198, 1199], shape=(1200,))

    • x
      (rows, columns)
      float64
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i4

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f8

      fill_value :

      -1000000

      long_name :

      geolocated x (across track) coordinate of detector FOV centre

      units :

      m

      valid_max :

      1000000

      valid_min :

      -500000

      Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

    • y
      (rows, columns)
      float64
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      dimensions :

      ['rows', 'columns']

      dtype :

      <i4

      eopf_is_masked :

      True

      eopf_target_dtype :

      <f8

      fill_value :

      -5000000

      long_name :

      geolocated y (along track) coordinate of detector FOV centre

      units :

      m

      valid_max :

      50000000

      valid_min :

      -1000000

      Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • Data variables: (1)
    • lst
      (rows, columns)
      float64
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 290.0, 'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'gridded land surface temperature', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst', 'standard_name': 'surface_temperature', 'units': 'K', 'valid_max': 32767, 'valid_min': -32767}

      dtype :

      <i2

      long_name :

      gridded land surface temperature

      standard_name :

      surface_temperature

      units :

      K

      Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • Attributes: (0)

  <xarray.DatasetView> Size: 113MB
  Dimensions:                 (rows: 1200, columns: 1500)
  Dimensions without coordinates: rows, columns
  Data variables:
      cloud_in                (rows, columns) uint16 4MB dask.array<chunksize=(601, 750), meta=np.ndarray>
      confidence_in           (rows, columns) uint16 4MB dask.array<chunksize=(601, 750), meta=np.ndarray>
      exception               (rows, columns) int16 4MB dask.array<chunksize=(601, 750), meta=np.ndarray>
      lst_uncertainty         (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
      lst_uncertainty_atm     (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
      lst_uncertainty_geo     (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
      lst_uncertainty_random  (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
      lst_uncertainty_sf      (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
      lst_uncertainty_sys     (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
      lst_uncertainty_t       (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
      pointing_in             (rows, columns) uint8 2MB dask.array<chunksize=(601, 750), meta=np.ndarray>

  quality

  • Groups: (1)
    <xarray.DatasetView> Size: 14MB
    Dimensions:                 (rows: 1200, columns: 1500, orphan_pixels: 187)
    Dimensions without coordinates: rows, columns, orphan_pixels
    Data variables:
        cloud_orphan_in         (rows, orphan_pixels) uint16 449kB dask.array<chunksize=(601, 187), meta=np.ndarray>
        confidence_orphan_in    (rows, orphan_pixels) uint16 449kB dask.array<chunksize=(601, 187), meta=np.ndarray>
        exception               (rows, orphan_pixels) int16 449kB dask.array<chunksize=(601, 187), meta=np.ndarray>
        lst_uncertainty         (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>
        lst_uncertainty_atm     (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>
        lst_uncertainty_geo     (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>
        lst_uncertainty_random  (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>
        lst_uncertainty_sf      (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>
        lst_uncertainty_sys     (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>
        lst_uncertainty_t       (rows, orphan_pixels) float64 2MB dask.array<chunksize=(601, 187), meta=np.ndarray>
        pointing_orphan_in      (rows, orphan_pixels) uint8 224kB dask.array<chunksize=(601, 187), meta=np.ndarray>

    orphan

    • Groups: (0)
    • Dimensions:
      • rows: 1200
      • columns: 1500
      • orphan_pixels: 187
    • Coordinates: (0)
    • Data variables: (11)
      • cloud_orphan_in
        (rows, orphan_pixels)
        uint16
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<u2', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768], 'flag_meanings': 'visible 1.37_threshold 1.6_small_histogram 1.6_large_histogram 2.25_small_histogram 2.25_large_histogram 11_spatial_coherence gross_cloud thin_cirrus medium_high fog_low_stratus 11_12_view_difference 3.7_11_view_difference thermal_histogram spare spare', 'long_name': 'global cloud flag from various tests', 'short_name': 'cloud_orphan_in'}

        dtype :

        <u2

        flag_masks :

        [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768]

        flag_meanings :

        visible 1.37_threshold 1.6_small_histogram 1.6_large_histogram 2.25_small_histogram 2.25_large_histogram 11_spatial_coherence gross_cloud thin_cirrus medium_high fog_low_stratus 11_12_view_difference 3.7_11_view_difference thermal_histogram spare spare

        long_name :

        global cloud flag from various tests

        Array Chunk Bytes 438.28 kiB 219.51 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type uint16 numpy.ndarray

      • confidence_orphan_in
        (rows, orphan_pixels)
        uint16
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<u2', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768], 'flag_meanings': 'coastline ocean tidal land inland_water unfilled spare spare cosmetic duplicate day twilight sun_glint snow summary_cloud summary_pointing', 'long_name': 'global confidence flag from various tests', 'short_name': 'confidence_orphan_in'}

        dtype :

        <u2

        flag_masks :

        [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768]

        flag_meanings :

        coastline ocean tidal land inland_water unfilled spare spare cosmetic duplicate day twilight sun_glint snow summary_cloud summary_pointing

        long_name :

        global confidence flag from various tests

        Array Chunk Bytes 438.28 kiB 219.51 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type uint16 numpy.ndarray

      • exception
        (rows, orphan_pixels)
        int16
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<i2', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024], 'flag_meanings': 'isp_absent pixel_absent not_decompressed no_signal saturation invalid_radiance no_parameters unfilled_pixel lst_underflow lst_overflow biome', 'long_name': 'ungridded LST pixel exception flags', 'short_name': 'exception_orphan', 'standard_name': 'surface_temperature status_flags'}

        dtype :

        <i2

        flag_masks :

        [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]

        flag_meanings :

        isp_absent pixel_absent not_decompressed no_signal saturation invalid_radiance no_parameters unfilled_pixel lst_underflow lst_overflow biome

        long_name :

        ungridded LST pixel exception flags

        standard_name :

        surface_temperature status_flags

        Array Chunk Bytes 438.28 kiB 219.51 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type int16 numpy.ndarray

      • lst_uncertainty
        (rows, orphan_pixels)
        float64
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'ungridded land surface temperature estimated total uncertainty', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst_uncertainty_orphan', 'standard_name': 'surface_temperature_standard_error', 'units': 'K', 'valid_max': 4000, 'valid_min': 0}

        dtype :

        <i2

        long_name :

        ungridded land surface temperature estimated total uncertainty

        standard_name :

        surface_temperature_standard_error

        units :

        K

        Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • lst_uncertainty_atm
        (rows, orphan_pixels)
        float64
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'ungridded locally correlated LST uncertainties due to atmospheric fit', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst_uncertainty_atm_orphan', 'standard_name': 'surface_temperature_standard_error', 'units': 'K', 'valid_max': 4000, 'valid_min': 0}

        dtype :

        <i2

        long_name :

        ungridded locally correlated LST uncertainties due to atmospheric fit

        standard_name :

        surface_temperature_standard_error

        units :

        K

        Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • lst_uncertainty_geo
        (rows, orphan_pixels)
        float64
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'ungridded locally correlated LST uncertainties due to geolocation', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst_uncertainty_geo_orphan', 'standard_name': 'surface_temperature_standard_error', 'units': 'K', 'valid_max': 4000, 'valid_min': 0}

        dtype :

        <i2

        long_name :

        ungridded locally correlated LST uncertainties due to geolocation

        standard_name :

        surface_temperature_standard_error

        units :

        K

        Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • lst_uncertainty_random
        (rows, orphan_pixels)
        float64
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'ungridded uncorrelated LST uncertainties from on-board calibration', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst_uncertainty_random_orphan', 'standard_name': 'surface_temperature_standard_error', 'units': 'K', 'valid_max': 4000, 'valid_min': 0}

        dtype :

        <i2

        long_name :

        ungridded uncorrelated LST uncertainties from on-board calibration

        standard_name :

        surface_temperature_standard_error

        units :

        K

        Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • lst_uncertainty_sf
        (rows, orphan_pixels)
        float64
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'ungridded locally correlated LST uncertainties due to fractional vegetation', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst_uncertainty_sf_orphan', 'standard_name': 'surface_temperature_standard_error', 'units': 'K', 'valid_max': 4000, 'valid_min': 0}

        dtype :

        <i2

        long_name :

        ungridded locally correlated LST uncertainties due to fractional vegetation

        standard_name :

        surface_temperature_standard_error

        units :

        K

        Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • lst_uncertainty_sys
        (rows, orphan_pixels)
        float64
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'ungridded large systematic LST uncertainties', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst_uncertainty_sys_orphan', 'standard_name': 'surface_temperature_standard_error', 'units': 'K', 'valid_max': 4000, 'valid_min': 0}

        dtype :

        <i2

        long_name :

        ungridded large systematic LST uncertainties

        standard_name :

        surface_temperature_standard_error

        units :

        K

        Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • lst_uncertainty_t
        (rows, orphan_pixels)
        float64
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        _eopf_attrs :

        {'add_offset': 0.0, 'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'ungridded locally correlated LST uncertainties from pre-lauch calibration', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst_uncertainty_t_orphan', 'standard_name': 'surface_temperature_standard_error', 'units': 'K', 'valid_max': 4000, 'valid_min': 0}

        dtype :

        <i2

        long_name :

        ungridded locally correlated LST uncertainties from pre-lauch calibration

        standard_name :

        surface_temperature_standard_error

        units :

        K

        Array Chunk Bytes 1.71 MiB 878.02 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

      • pointing_orphan_in
        (rows, orphan_pixels)
        uint8
        dask.array<chunksize=(601, 187), meta=np.ndarray>

        _eopf_attrs :

        {'dimensions': ['rows', 'orphan_pixels'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128], 'flag_meanings': 'flipmirrorabsoluteerror flipmirrorintegratederror flipmirrorrmserror scanmirrorabsoluteerror scanmirrorintegratederror scanmirrorrmserror scantimeerror platform_mode', 'long_name': 'global pointing flag from various tests', 'short_name': 'pointing_orphan_in'}

        dtype :

        <u1

        flag_masks :

        [1, 2, 4, 8, 16, 32, 64, 128]

        flag_meanings :

        flipmirrorabsoluteerror flipmirrorintegratederror flipmirrorrmserror scanmirrorabsoluteerror scanmirrorintegratederror scanmirrorrmserror scantimeerror platform_mode

        long_name :

        global pointing flag from various tests

        Array Chunk Bytes 219.14 kiB 109.75 kiB Shape (1200, 187) (601, 187) Dask graph 2 chunks in 2 graph layers Data type uint8 numpy.ndarray

    • Attributes: (0)

  • Dimensions:
    • rows: 1200
    • columns: 1500
  • Coordinates: (0)
  • Data variables: (11)
    • cloud_in
      (rows, columns)
      uint16
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      _eopf_attrs :

      {'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768], 'flag_meanings': 'visible 1.37_threshold 1.6_small_histogram 1.6_large_histogram 2.25_small_histogram 2.25_large_histogram 11_spatial_coherence gross_cloud thin_cirrus medium_high fog_low_stratus 11_12_view_difference 3.7_11_view_difference thermal_histogram spare spare', 'long_name': 'global cloud flag from various tests', 'short_name': 'cloud_in'}

      dtype :

      <u2

      flag_masks :

      [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768]

      flag_meanings :

      visible 1.37_threshold 1.6_small_histogram 1.6_large_histogram 2.25_small_histogram 2.25_large_histogram 11_spatial_coherence gross_cloud thin_cirrus medium_high fog_low_stratus 11_12_view_difference 3.7_11_view_difference thermal_histogram spare spare

      long_name :

      global cloud flag from various tests

      Array Chunk Bytes 3.43 MiB 880.37 kiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type uint16 numpy.ndarray

    • confidence_in
      (rows, columns)
      uint16
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      _eopf_attrs :

      {'dimensions': ['rows', 'columns'], 'dtype': '<u2', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768], 'flag_meanings': 'coastline ocean tidal land inland_water unfilled spare spare cosmetic duplicate day twilight sun_glint snow summary_cloud summary_pointing', 'long_name': 'global confidence flag from various tests', 'short_name': 'confidence_in'}

      dtype :

      <u2

      flag_masks :

      [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768]

      flag_meanings :

      coastline ocean tidal land inland_water unfilled spare spare cosmetic duplicate day twilight sun_glint snow summary_cloud summary_pointing

      long_name :

      global confidence flag from various tests

      Array Chunk Bytes 3.43 MiB 880.37 kiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type uint16 numpy.ndarray

    • exception
      (rows, columns)
      int16
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      _eopf_attrs :

      {'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024], 'flag_meanings': 'isp_absent pixel_absent not_decompressed no_signal saturation invalid_radiance no_parameters unfilled_pixel lst_underflow lst_overflow biome', 'long_name': 'gridded LST pixel exception flags', 'short_name': 'exception', 'standard_name': 'surface_temperature status_flags'}

      dtype :

      <i2

      flag_masks :

      [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]

      flag_meanings :

      isp_absent pixel_absent not_decompressed no_signal saturation invalid_radiance no_parameters unfilled_pixel lst_underflow lst_overflow biome

      long_name :

      gridded LST pixel exception flags

      standard_name :

      surface_temperature status_flags

      Array Chunk Bytes 3.43 MiB 880.37 kiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type int16 numpy.ndarray

    • lst_uncertainty
      (rows, columns)
      float64
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'gridded land surface temperature estimated total uncertainty', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst_uncertainty', 'standard_name': 'surface_temperature_standard_error', 'units': 'K', 'valid_max': 4000, 'valid_min': 0}

      dtype :

      <i2

      long_name :

      gridded land surface temperature estimated total uncertainty

      standard_name :

      surface_temperature_standard_error

      units :

      K

      Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

    • lst_uncertainty_atm
      (rows, columns)
      float64
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'gridded locally correlated LST uncertainties due to atmospheric fit', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst_uncertainty_atm', 'standard_name': 'surface_temperature_standard_error', 'units': 'K', 'valid_max': 4000, 'valid_min': 0}

      dtype :

      <i2

      long_name :

      gridded locally correlated LST uncertainties due to atmospheric fit

      standard_name :

      surface_temperature_standard_error

      units :

      K

      Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

    • lst_uncertainty_geo
      (rows, columns)
      float64
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'gridded locally correlated LST uncertainties due to geolocation', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst_uncertainty_geo', 'standard_name': 'surface_temperature_standard_error', 'units': 'K', 'valid_max': 4000, 'valid_min': 0}

      dtype :

      <i2

      long_name :

      gridded locally correlated LST uncertainties due to geolocation

      standard_name :

      surface_temperature_standard_error

      units :

      K

      Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

    • lst_uncertainty_random
      (rows, columns)
      float64
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'gridded uncorrelated LST uncertainties from on-board calibration', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst_uncertainty_random', 'standard_name': 'surface_temperature_standard_error', 'units': 'K', 'valid_max': 4000, 'valid_min': 0}

      dtype :

      <i2

      long_name :

      gridded uncorrelated LST uncertainties from on-board calibration

      standard_name :

      surface_temperature_standard_error

      units :

      K

      Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

    • lst_uncertainty_sf
      (rows, columns)
      float64
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'gridded locally correlated LST uncertainties due to fractional vegetation', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst_uncertainty_sf', 'standard_name': 'surface_temperature_standard_error', 'units': 'K', 'valid_max': 4000, 'valid_min': 0}

      dtype :

      <i2

      long_name :

      gridded locally correlated LST uncertainties due to fractional vegetation

      standard_name :

      surface_temperature_standard_error

      units :

      K

      Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

    • lst_uncertainty_sys
      (rows, columns)
      float64
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'gridded large systematic LST uncertainties', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst_uncertainty_sys', 'standard_name': 'surface_temperature_standard_error', 'units': 'K', 'valid_max': 4000, 'valid_min': 0}

      dtype :

      <i2

      long_name :

      gridded large systematic LST uncertainties

      standard_name :

      surface_temperature_standard_error

      units :

      K

      Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

    • lst_uncertainty_t
      (rows, columns)
      float64
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      _eopf_attrs :

      {'add_offset': 0.0, 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'gridded locally correlated LST uncertainties from pre-launch calibration', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst_uncertainty_t', 'standard_name': 'surface_temperature_standard_error', 'units': 'K', 'valid_max': 4000, 'valid_min': 0}

      dtype :

      <i2

      long_name :

      gridded locally correlated LST uncertainties from pre-launch calibration

      standard_name :

      surface_temperature_standard_error

      units :

      K

      Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

    • pointing_in
      (rows, columns)
      uint8
      dask.array<chunksize=(601, 750), meta=np.ndarray>

      _eopf_attrs :

      {'dimensions': ['rows', 'columns'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128], 'flag_meanings': 'flipmirrorabsoluteerror flipmirrorintegratederror flipmirrorrmserror scanmirrorabsoluteerror scanmirrorintegratederror scanmirrorrmserror scantimeerror platform_mode', 'long_name': 'global pointing flag from various tests', 'short_name': 'pointing_in'}

      dtype :

      <u1

      flag_masks :

      [1, 2, 4, 8, 16, 32, 64, 128]

      flag_meanings :

      flipmirrorabsoluteerror flipmirrorintegratederror flipmirrorrmserror scanmirrorabsoluteerror scanmirrorintegratederror scanmirrorrmserror scantimeerror platform_mode

      long_name :

      global pointing flag from various tests

      Array Chunk Bytes 1.72 MiB 440.19 kiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • Attributes: (0)

• Dimensions:
• Coordinates: (0)
• Inherited coordinates: (0)
• Data variables: (0)
• Attributes: (3)

  other_metadata :

  {'L0_offset_between_scan_index_and_ISP_scan_count_in': 11663, 'absolute_pass_number': 82107, 'band_description': {'s1': {'detector_temperature_max': 266.999, 'detector_temperature_min': 266.947}, 's2': {'detector_temperature_max': 266.524, 'detector_temperature_min': 266.474}, 's3': {'detector_temperature_max': 266.024, 'detector_temperature_min': 265.975}, 's4': {'detector_temperature_max': 87.769, 'detector_temperature_min': 87.72}, 's5': {'detector_temperature_max': 87.831, 'detector_temperature_min': 87.7885}, 's6': {'detector_temperature_max': 88.7536, 'detector_temperature_min': 88.7079}, 's7': {'detector_temperature_max': 87.4356, 'detector_temperature_min': 87.3932}, 's8': {'detector_temperature_max': 87.3304, 'detector_temperature_min': 87.2943}, 's9': {'detector_temperature_max': 87.5602, 'detector_temperature_min': 87.5242}}, 'cycle_number': 117, 'data_information': {'accuracy_reference': '[PDS-SL]', 'measurement_accuracy': '~2% S1-S6, ~0.1K S7-S9, ~1K F1-F2'}, 'eopf_category': 'eoproduct', 'ephemeris': {'start': {'TAI': '2026-03-13T21:12:19.824041', 'UT1': '2026-03-13T21:11:42.886630', 'UTC': '2026-03-13T21:11:42.824041Z', 'position': {'x': 7018895.257, 'y': 1532288.188, 'z': -0.002}, 'velocity': {'x': 341.629843, 'y': -1604.303059, 'z': 7365.970636}}, 'stop': {'TAI': '2026-03-13T22:53:18.968586', 'UT1': '2026-03-13T22:52:42.031167', 'UTC': '2026-03-13T22:52:41.968586Z', 'position': {'x': 7002055.678, 'y': -1607655.772, 'z': 0.003}, 'velocity': {'x': -375.287233, 'y': -1596.825626, 'z': 7365.925691}}}, 'i_nadir_first_acquired_pixel': 2469, 'i_oblique_first_acquired_pixel': 1124, 'in_scan_period_in_microseconds': 299985, 'meteo': {'missing_time': 0, 'source': 'ECMWF', 'type': 'FORECAST'}, 'phase_identifier': 4, 'pixel_time_sampling_interval_along_scan_i_in_microseconds': 81.73999786376953, 'product_unit': {'alongtrack_coordinate': 720, 'duration': 179, 'type': 'FRAME'}, 'relative_pass_number': 713, 'single_meteofield_synoptic_time_UTC_hours': 0}

  processing_history :

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'Metadata Specification, Excel document S3IPF.PDS.008', 'S3B_SL_2_LST____20260313T211921_20260313T212421_20260313T235613_0299_117_357______ESA_O_NR_004.SEN3', 'S3B_SL_2_LST____20260313T212421_20260313T213007_20260313T235545_0346_117_357______ESA_O_NR_004.SEN3'], 'outputs': ['S3B_SL_2_LST____20260313T212343_20260313T212643_20260313T235949_0179_117_357_0720_ESA_O_NR_004.SEN3'], 'processor': 'PUG', 'time': '2026-03-14T00:00:00.553510Z', 'version': '03.50'}, {'facility': 'CS-SopraSteria', 'inputs': ['S3B_SL_2_LST____20260313T212343_20260313T212643_20260313T235949_0179_117_357_0720_ESA_O_NR_004.SEN3'], 'outputs': ['S3B_SL_2_LST____20260313T212343_20260313T212643_20260313T235949_0179_117_357_0720_ESA_O_NR_004.zarr.zarr'], 'processor': 'EOPF-CPM', 'time': '2026-03-14T01:11:31.706013Z', 'version': '2.6.2'}]}

  stac_discovery :

  {'assets': {'biome_auxiliary_orphan': {'href': '/conditions/auxiliary/orphan/biome', 'title': 'biome_auxiliary_orphan'}, 'cloud_fraction_tp': {'href': '/conditions/meteorology/cloud_fraction_tp', 'title': 'cloud_fraction_tp'}, 'cloud_in': {'href': '/quality/cloud_in', 'title': 'cloud_in'}, 'cloud_orphan_in': {'href': '/quality/orphan/cloud_orphan_in', 'title': 'cloud_orphan_in'}, 'confidence_in': {'href': '/quality/confidence_in', 'title': 'confidence_in'}, 'confidence_orphan_in': {'href': '/quality/orphan/confidence_orphan_in', 'title': 'confidence_orphan_in'}, 'detector': {'href': '/conditions/processing/detector', 'title': 'detector'}, 'detector_processing_orphan': {'href': '/conditions/processing/orphan/detector', 'title': 'detector_processing_orphan'}, 'dew_point_tp': {'href': '/conditions/meteorology/dew_point_tp', 'title': 'dew_point_tp'}, 'east_west_stress_tp': {'href': '/conditions/meteorology/east_west_stress_tp', 'title': 'east_west_stress_tp'}, 'elevation': {'href': '/conditions/auxiliary/elevation', 'title': 'elevation'}, 'elevation_auxiliary_orphan': {'href': '/conditions/auxiliary/orphan/elevation', 'title': 'elevation_auxiliary_orphan'}, 'exception': {'href': '/quality/exception', 'title': 'exception'}, 'exception_orphan': {'href': '/quality/orphan/exception', 'title': 'exception_orphan'}, 'fraction': {'href': '/conditions/auxiliary/fraction', 'title': 'fraction'}, 'fraction_auxiliary_orphan': {'href': '/conditions/auxiliary/orphan/fraction', 'title': 'fraction_auxiliary_orphan'}, 'latent_heat_tp': {'href': '/conditions/meteorology/latent_heat_tp', 'title': 'latent_heat_tp'}, 'lst': {'href': '/measurements/lst', 'title': 'lst'}, 'lst_orphan': {'href': '/measurements/orphan/lst', 'title': 'lst_orphan'}, 'lst_uncertainty': {'href': '/quality/lst_uncertainty', 'title': 'lst_uncertainty'}, 'lst_uncertainty_atm': {'href': '/quality/lst_uncertainty_atm', 'title': 'lst_uncertainty_atm'}, 'lst_uncertainty_atm_orphan': {'href': '/quality/orphan/lst_uncertainty_atm', 'title': 'lst_uncertainty_atm_orphan'}, 'lst_uncertainty_geo': {'href': '/quality/lst_uncertainty_geo', 'title': 'lst_uncertainty_geo'}, 'lst_uncertainty_geo_orphan': {'href': '/quality/orphan/lst_uncertainty_geo', 'title': 'lst_uncertainty_geo_orphan'}, 'lst_uncertainty_orphan': {'href': '/quality/orphan/lst_uncertainty', 'title': 'lst_uncertainty_orphan'}, 'lst_uncertainty_random': {'href': '/quality/lst_uncertainty_random', 'title': 'lst_uncertainty_random'}, 'lst_uncertainty_random_orphan': {'href': '/quality/orphan/lst_uncertainty_random', 'title': 'lst_uncertainty_random_orphan'}, 'lst_uncertainty_sf': {'href': '/quality/lst_uncertainty_sf', 'title': 'lst_uncertainty_sf'}, 'lst_uncertainty_sf_orphan': {'href': '/quality/orphan/lst_uncertainty_sf', 'title': 'lst_uncertainty_sf_orphan'}, 'lst_uncertainty_sys': {'href': '/quality/lst_uncertainty_sys', 'title': 'lst_uncertainty_sys'}, 'lst_uncertainty_sys_orphan': {'href': '/quality/orphan/lst_uncertainty_sys', 'title': 'lst_uncertainty_sys_orphan'}, 'lst_uncertainty_t': {'href': '/quality/lst_uncertainty_t', 'title': 'lst_uncertainty_t'}, 'lst_uncertainty_t_orphan': {'href': '/quality/orphan/lst_uncertainty_t', 'title': 'lst_uncertainty_t_orphan'}, 'nadir_first_scan_i': {'href': '/conditions/time/nadir_first_scan_i', 'title': 'nadir_first_scan_i'}, 'nadir_last_scan_i': {'href': '/conditions/time/nadir_last_scan_i', 'title': 'nadir_last_scan_i'}, 'nadir_maximal_ts_i': {'href': '/conditions/time/nadir_maximal_ts_i', 'title': 'nadir_maximal_ts_i'}, 'nadir_minimal_ts_i': {'href': '/conditions/time/nadir_minimal_ts_i', 'title': 'nadir_minimal_ts_i'}, 'ndvi': {'href': '/conditions/auxiliary/ndvi', 'title': 'ndvi'}, 'ndvi_auxiliary_orphan': {'href': '/conditions/auxiliary/orphan/ndvi', 'title': 'ndvi_auxiliary_orphan'}, 'north_south_stress_tp': {'href': '/conditions/meteorology/north_south_stress_tp', 'title': 'north_south_stress_tp'}, 'oblique_first_scan_i': {'href': '/conditions/time/oblique_first_scan_i', 'title': 'oblique_first_scan_i'}, 'oblique_last_scan_i': {'href': '/conditions/time/oblique_last_scan_i', 'title': 'oblique_last_scan_i'}, 'oblique_maximal_ts_i': {'href': '/conditions/time/oblique_maximal_ts_i', 'title': 'oblique_maximal_ts_i'}, 'oblique_minimal_ts_i': {'href': '/conditions/time/oblique_minimal_ts_i', 'title': 'oblique_minimal_ts_i'}, 'pixel': {'href': '/conditions/processing/pixel', 'title': 'pixel'}, 'pixel_processing_orphan': {'href': '/conditions/processing/orphan/pixel', 'title': 'pixel_processing_orphan'}, 'pointing_in': {'href': '/quality/pointing_in', 'title': 'pointing_in'}, 'pointing_orphan_in': {'href': '/quality/orphan/pointing_orphan_in', 'title': 'pointing_orphan_in'}, 'sat_azimuth_tn': {'href': '/conditions/geometry/sat_azimuth_tn', 'title': 'sat_azimuth_tn'}, 'sat_path_tn': {'href': '/conditions/geometry/sat_path_tn', 'title': 'sat_path_tn'}, 'sat_zenith_tn': {'href': '/conditions/geometry/sat_zenith_tn', 'title': 'sat_zenith_tn'}, 'scan': {'href': '/conditions/processing/scan', 'title': 'scan'}, 'scan_processing_orphan': {'href': '/conditions/processing/orphan/scan', 'title': 'scan_processing_orphan'}, 'sea_ice_fraction_tp': {'href': '/conditions/meteorology/sea_ice_fraction_tp', 'title': 'sea_ice_fraction_tp'}, 'sea_surface_temperature_tp': {'href': '/conditions/meteorology/sea_surface_temperature_tp', 'title': 'sea_surface_temperature_tp'}, 'sensible_heat_tp': {'href': '/conditions/meteorology/sensible_heat_tp', 'title': 'sensible_heat_tp'}, 'skin_temperature_tp': {'href': '/conditions/meteorology/skin_temperature_tp', 'title': 'skin_temperature_tp'}, 'snow_albedo_tp': {'href': '/conditions/meteorology/snow_albedo_tp', 'title': 'snow_albedo_tp'}, 'soil_wetness_tp': {'href': '/conditions/meteorology/soil_wetness_tp', 'title': 'soil_wetness_tp'}, 'solar_azimuth_tn': {'href': '/conditions/geometry/solar_azimuth_tn', 'title': 'solar_azimuth_tn'}, 'solar_path_tn': {'href': '/conditions/geometry/solar_path_tn', 'title': 'solar_path_tn'}, 'solar_radiation_tp': {'href': '/conditions/meteorology/solar_radiation_tp', 'title': 'solar_radiation_tp'}, 'solar_zenith_tn': {'href': '/conditions/geometry/solar_zenith_tn', 'title': 'solar_zenith_tn'}, 'specific_humidity_tp': {'href': '/conditions/meteorology/specific_humidity_tp', 'title': 'specific_humidity_tp'}, 'surface_pressure_tp': {'href': '/conditions/meteorology/surface_pressure_tp', 'title': 'surface_pressure_tp'}, 'tcwv': {'href': '/conditions/auxiliary/tcwv', 'title': 'tcwv'}, 'tcwv_auxiliary_orphan': {'href': '/conditions/auxiliary/orphan/tcwv', 'title': 'tcwv_auxiliary_orphan'}, 'temperature_profile_tp': {'href': '/conditions/meteorology/temperature_profile_tp', 'title': 'temperature_profile_tp'}, 'temperature_tp': {'href': '/conditions/meteorology/temperature_tp', 'title': 'temperature_tp'}, 'thermal_radiation_tp': {'href': '/conditions/meteorology/thermal_radiation_tp', 'title': 'thermal_radiation_tp'}, 'time_stamp_i': {'href': '/conditions/time/time_stamp_i', 'title': 'time_stamp_i'}, 'total_column_ozone_tp': {'href': '/conditions/meteorology/total_column_ozone_tp', 'title': 'total_column_ozone_tp'}, 'total_column_water_vapour_tp': {'href': '/conditions/meteorology/total_column_water_vapour_tp', 'title': 'total_column_water_vapour_tp'}, 'u_wind_tp': {'href': '/conditions/meteorology/u_wind_tp', 'title': 'u_wind_tp'}, 'v_wind_tp': {'href': '/conditions/meteorology/v_wind_tp', 'title': 'v_wind_tp'}, 'validation': {'href': '/conditions/auxiliary/validation', 'title': 'validation'}}, 'bbox': [13.4892, 41.1036, -9.7816, 54.4788], 'collection': '004', 'geometry': {'coordinates': [[[-9.7816, 51.3164], [-9.5388, 51.3789], [-8.84322, 51.5538], [-8.16286, 51.7148], [-7.47445, 51.8768], [-6.7682, 52.0322], [-6.06147, 52.1822], [-5.36546, 52.3339], [-4.64595, 52.4781], [-3.92827, 52.6159], [-3.21116, 52.749], [-2.49213, 52.8778], [-1.7559, 53.0133], [-1.02042, 53.1342], [-0.284995, 53.2503], [0.448813, 53.3623], [1.20058, 53.4702], [1.94421, 53.5742], [2.70076, 53.6733], [3.46592, 53.7679], [4.20987, 53.8551], [4.98765, 53.9419], [5.74444, 54.0233], [6.51186, 54.1013], [7.29091, 54.1595], [8.06605, 54.2275], [8.82896, 54.2865], [9.61304, 54.347], [10.3817, 54.4024], [11.1673, 54.4418], [11.9558, 54.4788], [12.3218, 51.8978], [12.7028, 49.2716], [13.092, 46.6438], [13.4878, 44.0232], [13.4892, 44.0145], [12.8461, 43.9598], [12.2266, 43.9147], [11.5938, 43.8483], [10.9742, 43.7822], [10.3409, 43.726], [9.71547, 43.6526], [9.09573, 43.5795], [8.46148, 43.5041], [7.85007, 43.4221], [7.23984, 43.3401], [6.61759, 43.252], [5.99435, 43.1615], [5.38253, 43.0773], [4.77657, 42.9811], [4.16384, 42.8742], [3.55086, 42.7728], [2.951, 42.6684], [2.3461, 42.5602], [1.74865, 42.4488], [1.14496, 42.3348], [0.543831, 42.2086], [-0.05038, 42.0895], [-0.638413, 41.9687], [-1.23313, 41.8425], [-1.81618, 41.7057], [-2.40888, 41.5737], [-2.98684, 41.4362], [-3.56624, 41.2963], [-4.1442, 41.1568], [-4.36205, 41.1036], [-4.37713, 41.1094], [-5.56055, 43.6866], [-6.85099, 46.2593], [-8.26364, 48.8164], [-9.7816, 51.3164]]], 'type': 'Polygon'}, 'id': 'S03SLSLST_20260313T212342_0180_B357_T24E', 'links': [{'href': './.zattrs.json', 'rel': 'collection', 'type': 'application/json'}], 'properties': {'constellation': 'sentinel-3', 'created': '2026-03-13T23:59:49Z', 'datetime': '2026-03-13T21:25:12.824041Z', 'end_datetime': '2026-03-13T21:26:42.824041Z', 'eopf:instrument_mode': 'Earth Observation', 'gsd': 1000, 'instruments': ['slstr'], 'platform': 'sentinel-3b', 'processing:level': 'L2', 'processing:lineage': 'systematic', 'processing:software': {'PUG': '03.50'}, 'processing:version': '004', 'product:timeliness': 'PT3H', 'product:timeliness_category': 'NR', 'product:type': 'S03SLSLST', 'proj:shape': [{'columns': 1500, 'name': 'nadir', 'rows': 1200, 'start_offset': 4845, 'track_offset': 998}, {'columns': 900, 'name': 'oblique', 'rows': 1200, 'start_offset': 4845, 'track_offset': 450}], 'providers': [{'name': 'Copernicus Ground Segment', 'roles': ['processor']}, {'name': 'ESA', 'roles': ['producer']}], 'sat:absolute_orbit': 41054, 'sat:orbit_state': 'ascending', 'sat:platform_international_designator': '2018-039A', 'sat:relative_orbit': 357, 'start_datetime': '2026-03-13T21:23:42.824041Z'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.2.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/sat/v1.1.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.2.0/schema.json', 'https://stac-extensions.github.io/product/v0.1.0/schema.json'], 'stac_version': '1.1.0', 'type': 'Feature'}

Open Sentinel-3 SLSTR Level-2 LST Group as Dataset¶

Similarly, we can directly access the individual LST group (lst) as xarray.Dataset objects.

In [20]:

ds = xr.open_dataset(
    item.assets["lst"].href,
    engine="eopf-zarr", 
    op_mode="native",
    chunks={}
)
ds

ds = xr.open_dataset( item.assets["lst"].href, engine="eopf-zarr", op_mode="native", chunks={} ) ds

Out[20]:

<xarray.Dataset> Size: 72MB
Dimensions:    (rows: 1200, columns: 1500)
Coordinates:
  * columns    (columns) int64 12kB 0 1 2 3 4 5 ... 1495 1496 1497 1498 1499
    latitude   (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
    longitude  (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
  * rows       (rows) int64 10kB 0 1 2 3 4 5 6 ... 1194 1195 1196 1197 1198 1199
    x          (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
    y          (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>
Data variables:
    lst        (rows, columns) float64 14MB dask.array<chunksize=(601, 750), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • rows: 1200
  • columns: 1500
• Coordinates: (6)
  • columns
    (columns)
    int64
    0 1 2 3 4 ... 1496 1497 1498 1499

    dimensions :

    ['columns']

    dtype :

    <i8

    long_name :

    columns of image nadir view

    array([   0,    1,    2, ..., 1497, 1498, 1499], shape=(1500,))

  • latitude
    (rows, columns)
    float64
    dask.array<chunksize=(601, 750), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    latitude of detector FOV centre on the earth's surface

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90000000

    valid_min :

    -90000000

    Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • longitude
    (rows, columns)
    float64
    dask.array<chunksize=(601, 750), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -2147483648

    long_name :

    longitude of detector FOV centre on the earth's surface

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180000000

    valid_min :

    -180000000

    Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • rows
    (rows)
    int64
    0 1 2 3 4 ... 1196 1197 1198 1199

    dimensions :

    ['rows']

    dtype :

    <i8

    long_name :

    rows of image nadir view

    array([   0,    1,    2, ..., 1197, 1198, 1199], shape=(1200,))

  • x
    (rows, columns)
    float64
    dask.array<chunksize=(601, 750), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -1000000

    long_name :

    geolocated x (across track) coordinate of detector FOV centre

    units :

    m

    valid_max :

    1000000

    valid_min :

    -500000

    Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • y
    (rows, columns)
    float64
    dask.array<chunksize=(601, 750), meta=np.ndarray>

    dimensions :

    ['rows', 'columns']

    dtype :

    <i4

    eopf_is_masked :

    True

    eopf_target_dtype :

    <f8

    fill_value :

    -5000000

    long_name :

    geolocated y (along track) coordinate of detector FOV centre

    units :

    m

    valid_max :

    50000000

    valid_min :

    -1000000

    Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

• Data variables: (1)
  • lst
    (rows, columns)
    float64
    dask.array<chunksize=(601, 750), meta=np.ndarray>

    _eopf_attrs :

    {'add_offset': 290.0, 'coordinates': ['rows', 'columns', 'x', 'y', 'latitude', 'longitude'], 'dimensions': ['rows', 'columns'], 'dtype': '<i2', 'eopf_is_masked': True, 'eopf_target_dtype': '<f4', 'fill_value': -32768, 'long_name': 'gridded land surface temperature', 'scale_factor': 0.0020000000949949026, 'short_name': 'lst', 'standard_name': 'surface_temperature', 'units': 'K', 'valid_max': 32767, 'valid_min': -32767}

    dtype :

    <i2

    long_name :

    gridded land surface temperature

    standard_name :

    surface_temperature

    units :

    K

    Array Chunk Bytes 13.73 MiB 3.44 MiB Shape (1200, 1500) (601, 750) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (2)
  • columns
    PandasIndex

    PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,
           ...
           1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499],
          dtype='int64', name='columns', length=1500))

  • rows
    PandasIndex

    PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,
           ...
           1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199],
          dtype='int64', name='rows', length=1200))

• Attributes: (0)

And we can plot the LST array along with the 2D latitude and longitude grids.

In [21]:

fig, ax = plt.subplots(1, 3, figsize=(15, 4))
ds.lst.plot.imshow(ax=ax[0])
ds.latitude.plot.imshow(ax=ax[1], cmap="viridis")
ds.longitude.plot.imshow(ax=ax[2], cmap="viridis")
plt.tight_layout()

fig, ax = plt.subplots(1, 3, figsize=(15, 4)) ds.lst.plot.imshow(ax=ax[0]) ds.latitude.plot.imshow(ax=ax[1], cmap="viridis") ds.longitude.plot.imshow(ax=ax[2], cmap="viridis") plt.tight_layout()

Other Sentinel-3 products can be accessed using the same approach and are therefore not shown here in detail.

---

Conclusion¶

This notebook demonstrates how to access Sentinel-3 EOPF Zarr samples in native mode using the xarray-eopf plugin. Key takeaways are:

• Access all Sentinel-3 products using hte same methods.
• Open the full Zarr store as an xr.DataTree using xr.open_dataset and the asset "product".
• Open subgroups (e.g., radianceData, lst) as xr.Dataset using xr.open_dataset.
• Open the full Zarr store as a flattened xr.Dataset using xr.open_dataset and the asset "product".
• Filter variables using the variables keyword argument.
• Opening parameters are integrated in STAC assets.

Note: This notebook only covers the native mode, which presents the data as close as possible to the original product.
For an analysis-ready view, see the Sentinel-3 analysis mode notebook.