{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Access to Zarr Data\n",
    "\n",
    "Some datasets from the Open Data Portal have been migrated to the Zarr format. This allows for faster opening and processing, so it makes sense to check whether data is provided in the Zarr format first.\n",
    "\n",
    "The data is available via store abbreviation 'cci-zarr-store'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from xcube.core.store import new_data_store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "zarr_store = new_data_store('esa-cci-zarr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, let's see what data sets are available. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ESACCI-BIOMASS-L4-AGB-MERGED-100m-2010-2018-fv2.0.zarr',\n",
       " 'ESACCI-BIOMASS-L4-AGB-MERGED-100m-2010-2020-fv4.0.zarr',\n",
       " 'ESACCI-GHG-L2-CH4-SCIAMACHY-WFMD-2002-2011-fv1.zarr',\n",
       " 'ESACCI-GHG-L2-CO2-OCO-2-FOCAL-2014-2021-v10.zarr',\n",
       " 'ESACCI-GHG-L2-CO2-SCIAMACHY-WFMD-2002-2012-fv1.zarr',\n",
       " 'ESACCI-ICESHEETS_Antarctica_GMB-2002-2016-v1.1.zarr',\n",
       " 'ESACCI-ICESHEETS_Greenland_GMB-2003-2016-v1.1.zarr',\n",
       " 'ESACCI-L3C_CLOUD-CLD_PRODUCTS-AVHRR_NOAA-1982-2016-fv3.0.zarr',\n",
       " 'ESACCI-L3C_SNOW-SWE-1979-2018-fv1.0.zarr',\n",
       " 'ESACCI-L3C_SNOW-SWE-1979-2020-fv2.0.zarr',\n",
       " 'ESACCI-L4_FIRE-BA-MODIS-2001-2022-fv5.1.zarr',\n",
       " 'ESACCI-L4_GHRSST-SST-GMPE-GLOB_CDR2.0-1981-2016-v02.0-fv01.0.zarr',\n",
       " 'ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992-2015-v2.0.7b.zarr',\n",
       " 'ESACCI-LST-L3C-LST-MODISA-0.01deg_1DAILY_DAY-2002-2018-fv3.00.zarr',\n",
       " 'ESACCI-LST-L3C-LST-MODISA-0.01deg_1DAILY_NIGHT-2002-2018-fv3.00.zarr',\n",
       " 'ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1DAILY_DAY-1995-2020-fv3.00.zarr',\n",
       " 'ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1DAILY_NIGHT-1995-2020-fv3.00.zarr',\n",
       " 'ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1MONTHLY_DAY-1995-2020-fv3.00.zarr',\n",
       " 'ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1MONTHLY_NIGHT-1995-2020-fv3.00.zarr',\n",
       " 'ESACCI-OC-L3S-IOP-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-1997-2020-fv5.0.zarr',\n",
       " 'ESACCI-OC-L3S-IOP-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',\n",
       " 'ESACCI-OC-L3S-IOP-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',\n",
       " 'ESACCI-OC-L3S-IOP-MERGED-1Y_YEARLY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',\n",
       " 'ESACCI-OC-L3S-IOP-MERGED-5D_DAILY_4km_GEO_PML_OCx_QAA-2022-fv6.0.zarr',\n",
       " 'ESACCI-OC-L3S-IOP-MERGED-8D_DAILY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',\n",
       " 'ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',\n",
       " 'ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',\n",
       " 'ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1Y_YEARLY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',\n",
       " 'ESACCI-OC-L3S-OC_PRODUCTS-MERGED-5D_DAILY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr',\n",
       " 'ESACCI-OC-L3S-OC_PRODUCTS-MERGED-8D_DAILY_4km_GEO_PML_OCx_QAA-2022-fv6.0.zarr',\n",
       " 'ESACCI-OC-L3S-RRS-MERGED-1M_MONTHLY_4km_GEO_PML_RRS-1997-2022-fv6.0.zarr',\n",
       " 'ESACCI-OC-L3S-RRS-MERGED-1Y_YEARLY_4km_GEO_PML_RRS-1997-2022-fv6.0.zarr',\n",
       " 'ESACCI-OC-L3S-RRS-MERGED-1D_DAILY_4km_GEO_PML_RRS-1997-2022-fv6.0.zarr',\n",
       " 'ESACCI-OC-L3S-RRS-MERGED-5D_DAILY_4km_GEO_PML_RRS-1997-2022-fv6.0.zarr',\n",
       " 'ESACCI-OC-L3S-RRS-MERGED-8D_DAILY_4km_GEO_PML_RRS-1997-2022-fv6.0.zarr',\n",
       " 'ESACCI-PERMAFROST-L4-ALT-MODISLST-AREA4_PP-1997-2018-fv02.0.zarr',\n",
       " 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-NH25KMEASE2-2002-2012-fv2.0.zarr',\n",
       " 'ESACCI-SEAICE-L3C-SITHICK-SIRAL_CRYOSAT2-NH25KMEASE2-2010-2017-fv2.0.zarr',\n",
       " 'ESACCI-SEAICE-L4-SICONC-AMSR_50.0kmEASE2-NH-2002-2017-fv2.1.zarr',\n",
       " 'ESACCI-SEALEVEL-IND-MSLTR-MERGED-1993-2016-fv02.zarr',\n",
       " 'ESACCI-SEALEVEL-L4-MSLA-MERGED-1993-2015-fv02.zarr',\n",
       " 'ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-1978-2020-fv05.3.zarr',\n",
       " 'ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-1978-2021-fv07.1.zarr',\n",
       " 'ESACCI-WATERVAPOUR-L3C-TCWV-meris-005deg-2002-2017-fv3.2.zarr']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets = zarr_store.list_data_ids()\n",
    "datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The names are similar but different to the ones from the Climate Toolbox store.\n",
    "We can have a look at a dataset's metadata to find out more about it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "attrs": {
        "Conventions": "CF-1.9",
        "cdm_data_type": "Grid",
        "comment": "This dataset was produced with funding of the ESA CCI+ Soil Moisture project; ESRIN Contract No: 4000126684/19/I-NB",
        "contact": "cci_sm_contact@eodc.eu",
        "creator_email": "cci_sm_developer@eodc.eu",
        "creator_name": "Department of Geodesy and Geoinformation, Vienna University of Technology",
        "creator_url": "https://climers.geo.tuwien.ac.at/",
        "date_created": "File created: 2022-04-06 12:31:39.526359",
        "geospatial_lat_max": "90.0",
        "geospatial_lat_min": "-90.0",
        "geospatial_lat_resolution": "0.25 degree",
        "geospatial_lat_units": "degrees_north",
        "geospatial_lon_max": "180.0",
        "geospatial_lon_min": "-180.0",
        "geospatial_lon_resolution": "0.25 degree",
        "geospatial_lon_units": "degrees_east",
        "geospatial_vertical_max": "0.0",
        "geospatial_vertical_min": "0.0",
        "history": "2022-04-06 12:29:35 - product produced\n2023-08-09 17:33:51 - converted by nc2zarr, version 1.1.2.dev0",
        "id": "ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-20211231000000-fv07.1.nc",
        "institution": "TU Wien (AUT); VanderSat B.V. (NL)",
        "key_variables": "sm",
        "keywords": "soil moisture, soil water content, microwave remote sensing, active, passive, combined, climate data record",
        "keywords_vocabulary": "NASA Global Change Master Directory (GCMD) Science Keywords",
        "license": "Data use is free and open for all registered users.",
        "naming_authority": "TU Wien",
        "platform": "Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1, MIRAS, SMAP, GPM, FengYun-3B, FengYun-3C, FengYun-3D; ERS-1, ERS-2, METOP-A, METOP-B",
        "processing_level": "Quality-controlled, super-collocated (L3S) Satellite Soil Moisture (SM) data from multiple sensors",
        "product_version": "v07.1",
        "project": "Climate Change Initiative - European Space Agency",
        "references": "http://www.esa-soilmoisture-cci.org; Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F.,  Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017) ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001; Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dorigo, W. (2019) Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth System Science Data 11, 717-739, https://doi.org/10.5194/essd-11-717-2019; Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. https://doi.org/10.1109/TGRS.2017.2734070",
        "sensor": "SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP_radiometer, GMI, VIRR-3B, VIRR-3C, VIRR-3D; AMI-WS, ASCAT-A, ASCAT-B, ASCAT-C",
        "source": "WARP 5.5R1.1/AMI-WS/ERS12 Level 2 Soil Moisture; WARP 5.4R1.0/AMI-WS/ERS2 Level 2 Soil Moisture; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling;; LPRMv7/SMMR/Nimbus 7 L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/SSMI/F08, F11, F13 DMSP L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/TMI/TRMM L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/AMSR-E/Aqua L2B Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/WINDSAT/CORIOLIS L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/AMSR2/GCOM-W1 L3 Surface Soil Moisture, Ancillary Params; LPRMv7/SMOS/MIRAS L3 Surface Soil Moisture, CATDS Level 3 Brightness Temperatures (L3TB) version 300 RE03 & RE04; LPRMv7/SMAP_radiometer/SMAP L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/GMI/GPM L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3B L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3C L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3D L3 Surface Soil Moisture, Ancillary Params, and quality flags;;",
        "spatial_resolution": "25km",
        "standard_name_vocabulary": "CF Standard Name Table v77",
        "summary": "This dataset was produced with funding of the ESA CCI+ Soil Moisture project; ESRIN Contract No: 4000126684/19/I-NB",
        "time_coverage_duration": "P43Y",
        "time_coverage_end": "2021-12-31 00:00:00",
        "time_coverage_end_product": "20211231T235959Z",
        "time_coverage_resolution": "P1D",
        "time_coverage_start": "1978-11-01 00:00:00",
        "time_coverage_start_product": "19781101T000000Z",
        "title": "ESA CCI Surface Soil Moisture COMBINED active+passive Product",
        "tracking_id": "ad35798e-58e0-488f-b5b9-593874a47700"
       },
       "bbox": [
        -180,
        -90,
        180,
        90
       ],
       "coords": {
        "lat": {
         "attrs": {
          "_CoordinateAxisType": "Lat",
          "standard_name": "latitude",
          "units": "degrees_north",
          "valid_range": [
           -90,
           90
          ]
         },
         "dims": [
          "lat"
         ],
         "dtype": "float64",
         "name": "lat"
        },
        "lon": {
         "attrs": {
          "_CoordinateAxisType": "Lon",
          "standard_name": "longitude",
          "units": "degrees_east",
          "valid_range": [
           -180,
           180
          ]
         },
         "dims": [
          "lon"
         ],
         "dtype": "float64",
         "name": "lon"
        },
        "time": {
         "dims": [
          "time"
         ],
         "dtype": "datetime64[ns]",
         "name": "time"
        }
       },
       "data_id": "ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-1978-2021-fv07.1.zarr",
       "data_type": "dataset",
       "data_vars": {
        "dnflag": {
         "attrs": {
          "_CoordinateAxes": "time lat lon",
          "bit_meanings": [
           "NaN",
           "day",
           "night"
          ],
          "bits": [
           "0b0",
           "0b1",
           "0b10"
          ],
          "dtype": "int8",
          "flag_meanings": [
           "NaN",
           "day",
           "night",
           "day_night_combination"
          ],
          "flag_values": [
           0,
           1,
           2,
           3
          ],
          "long_name": "Day / Night Flag",
          "valid_range": [
           0,
           3
          ]
         },
         "chunks": [
          16,
          720,
          720
         ],
         "dims": [
          "time",
          "lat",
          "lon"
         ],
         "dtype": "float32",
         "name": "dnflag"
        },
        "flag": {
         "attrs": {
          "_CoordinateAxes": "time lat lon",
          "bit_meanings": [
           "no_data_inconsistency_detected",
           "snow_coverage_or_temperature_below_zero",
           "dense_vegetation",
           "others_no_convergence_in_the_model_thus_no_valid_sm_estimates",
           "soil_moisture_value_exceeds_physical_boundary",
           "weight_of_measurement_below_threshold",
           "all_datasets_deemed_unreliable",
           "barren_ground_advisory_flag",
           "NaN"
          ],
          "bits": [
           "0b0",
           "0b1",
           "0b10",
           "0b100",
           "0b1000",
           "0b10000",
           "0b100000",
           "0b1000000",
           "0b10000000"
          ],
          "dtype": "int16",
          "flag_meanings": [
           "no_data_inconsistency_detected",
           "snow_coverage_or_temperature_below_zero",
           "dense_vegetation",
           "combination_of_flag_values_1_and_2",
           "soil_moisture_value_exceeds_physical_boundary",
           "combination_of_flag_values_1_and_8",
           "combination_of_flag_values_2_and_8",
           "barren_ground_advisory_flag",
           "combination_of_flag_values_1_and_64",
           "combination_of_flag_values_2_and_64",
           "combination_of_flag_values_1_and_2_and_64",
           "combination_of_flag_values_8_and_64",
           "combination_of_flag_values_1_and_8_and_64",
           "combination_of_flag_values_2_and_8_and_64",
           "combination_of_flag_values_1_and_2_and_4_and_8_and_16_and_32_and_64"
          ],
          "flag_values": [
           0,
           1,
           2,
           3,
           8,
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           10,
           64,
           65,
           66,
           67,
           72,
           73,
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          ],
          "long_name": "Flag",
          "standard_name": "soil_moisture_content status_flag",
          "valid_range": [
           0,
           255
          ]
         },
         "chunks": [
          16,
          720,
          720
         ],
         "dims": [
          "time",
          "lat",
          "lon"
         ],
         "dtype": "float32",
         "name": "flag"
        },
        "freqbandID": {
         "attrs": {
          "_CoordinateAxes": "time lat lon",
          "bit_meanings": [
           "NaN",
           "L14",
           "C53",
           "C66",
           "C68",
           "C69",
           "C73",
           "X107",
           "K194",
           "MODEL"
          ],
          "bits": [
           "0b0",
           "0b1",
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           "0b100",
           "0b1000",
           "0b10000",
           "0b100000",
           "0b1000000",
           "0b10000000",
           "0b100000000"
          ],
          "dtype": "int16",
          "flag_meanings": [
           "NaN",
           "C66",
           "X107",
           "C66+X107"
          ],
          "flag_values": [
           0,
           4,
           64,
           68
          ],
          "long_name": "Frequency Band Identification",
          "valid_range": [
           0,
           511
          ]
         },
         "chunks": [
          16,
          720,
          720
         ],
         "dims": [
          "time",
          "lat",
          "lon"
         ],
         "dtype": "float32",
         "name": "freqbandID"
        },
        "mode": {
         "attrs": {
          "_CoordinateAxes": "time lat lon",
          "bit_meanings": [
           "NaN",
           "ascending",
           "descending"
          ],
          "bits": [
           "0b0",
           "0b1",
           "0b10"
          ],
          "dtype": "int8",
          "flag_meanings": [
           "NaN",
           "ascending",
           "descending",
           "ascending_descending_combination"
          ],
          "flag_values": [
           0,
           1,
           2,
           3
          ],
          "long_name": "Satellite Mode",
          "valid_range": [
           0,
           3
          ]
         },
         "chunks": [
          16,
          720,
          720
         ],
         "dims": [
          "time",
          "lat",
          "lon"
         ],
         "dtype": "float32",
         "name": "mode"
        },
        "sensor": {
         "attrs": {
          "_CoordinateAxes": "time lat lon",
          "bit_meanings": [
           "NaN",
           "SMMR",
           "SSMI",
           "TMI",
           "AMSRE",
           "WindSat",
           "AMSR2",
           "SMOS",
           "AMIWS",
           "ASCATA",
           "ASCATB",
           "SMAP",
           "MODEL",
           "GPM",
           "FY3B",
           "FY3D",
           "ASCATC",
           "FY3C"
          ],
          "bits": [
           "0b0",
           "0b1",
           "0b10",
           "0b100",
           "0b1000",
           "0b10000",
           "0b100000",
           "0b1000000",
           "0b10000000",
           "0b100000000",
           "0b1000000000",
           "0b10000000000",
           "0b100000000000",
           "0b1000000000000",
           "0b10000000000000",
           "0b100000000000000",
           "0b1000000000000000",
           "0b10000000000000000"
          ],
          "dtype": "int32",
          "flag_meanings": [
           "NaN",
           "SMMR"
          ],
          "flag_values": [
           0,
           1
          ],
          "long_name": "Sensor",
          "valid_range": [
           0,
           131071
          ]
         },
         "chunks": [
          16,
          720,
          720
         ],
         "dims": [
          "time",
          "lat",
          "lon"
         ],
         "dtype": "float64",
         "name": "sensor"
        },
        "sm": {
         "attrs": {
          "_CoordinateAxes": "time lat lon",
          "ancillary_variables": "sm_uncertainty flag t0",
          "dtype": "float32",
          "long_name": "Volumetric Soil Moisture",
          "standard_name": "soil_moisture_content",
          "units": "m3 m-3",
          "valid_range": [
           0,
           1
          ]
         },
         "chunks": [
          16,
          720,
          720
         ],
         "dims": [
          "time",
          "lat",
          "lon"
         ],
         "dtype": "float32",
         "name": "sm"
        },
        "sm_uncertainty": {
         "attrs": {
          "_CoordinateAxes": "time lat lon",
          "dtype": "float32",
          "long_name": "Volumetric Soil Moisture Uncertainty",
          "standard_name": "soil_moisture_content standard_error",
          "units": "m3 m-3",
          "valid_range": [
           0,
           1
          ]
         },
         "chunks": [
          16,
          720,
          720
         ],
         "dims": [
          "time",
          "lat",
          "lon"
         ],
         "dtype": "float32",
         "name": "sm_uncertainty"
        },
        "t0": {
         "attrs": {
          "_CoordinateAxes": "time lat lon",
          "dtype": "float64",
          "long_name": "Observation Timestamp",
          "units": "days after 1970-01-01 00:00:00 UTC",
          "valid_range": [
           3225,
           3227
          ]
         },
         "chunks": [
          16,
          720,
          720
         ],
         "dims": [
          "time",
          "lat",
          "lon"
         ],
         "dtype": "float64",
         "name": "t0"
        }
       },
       "dims": {
        "lat": 720,
        "lon": 1440,
        "time": 15767
       },
       "spatial_res": 0.25,
       "time_period": "1D",
       "time_range": [
        "1978-10-31",
        "2021-12-31"
       ]
      },
      "text/plain": [
       "<xcube.core.store.descriptor.DatasetDescriptor at 0x7fe84327eba0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zarr_store.describe_data('ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-1978-2021-fv07.1.zarr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cubes can easily be opened from the store like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 654GB\n",
       "Dimensions:         (time: 15767, lat: 720, lon: 1440)\n",
       "Coordinates:\n",
       "  * lat             (lat) float64 6kB 89.88 89.62 89.38 ... -89.38 -89.62 -89.88\n",
       "  * lon             (lon) float64 12kB -179.9 -179.6 -179.4 ... 179.6 179.9\n",
       "  * time            (time) datetime64[ns] 126kB 1978-11-01 ... 2021-12-31\n",
       "Data variables:\n",
       "    dnflag          (time, lat, lon) float32 65GB dask.array&lt;chunksize=(16, 720, 720), meta=np.ndarray&gt;\n",
       "    flag            (time, lat, lon) float32 65GB dask.array&lt;chunksize=(16, 720, 720), meta=np.ndarray&gt;\n",
       "    freqbandID      (time, lat, lon) float32 65GB dask.array&lt;chunksize=(16, 720, 720), meta=np.ndarray&gt;\n",
       "    mode            (time, lat, lon) float32 65GB dask.array&lt;chunksize=(16, 720, 720), meta=np.ndarray&gt;\n",
       "    sensor          (time, lat, lon) float64 131GB dask.array&lt;chunksize=(16, 720, 720), meta=np.ndarray&gt;\n",
       "    sm              (time, lat, lon) float32 65GB dask.array&lt;chunksize=(16, 720, 720), meta=np.ndarray&gt;\n",
       "    sm_uncertainty  (time, lat, lon) float32 65GB dask.array&lt;chunksize=(16, 720, 720), meta=np.ndarray&gt;\n",
       "    t0              (time, lat, lon) float64 131GB dask.array&lt;chunksize=(16, 720, 720), meta=np.ndarray&gt;\n",
       "Attributes: (12/44)\n",
       "    Conventions:                  CF-1.9\n",
       "    cdm_data_type:                Grid\n",
       "    comment:                      This dataset was produced with funding of t...\n",
       "    contact:                      cci_sm_contact@eodc.eu\n",
       "    creator_email:                cci_sm_developer@eodc.eu\n",
       "    creator_name:                 Department of Geodesy and Geoinformation, V...\n",
       "    ...                           ...\n",
       "    time_coverage_end_product:    20211231T235959Z\n",
       "    time_coverage_resolution:     P1D\n",
       "    time_coverage_start:          1978-11-01 00:00:00\n",
       "    time_coverage_start_product:  19781101T000000Z\n",
       "    title:                        ESA CCI Surface Soil Moisture COMBINED acti...\n",
       "    tracking_id:                  ad35798e-58e0-488f-b5b9-593874a47700</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bd303cc0-79c2-4d61-9c9e-50c2e02e91a6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd303cc0-79c2-4d61-9c9e-50c2e02e91a6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 15767</li><li><span class='xr-has-index'>lat</span>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1b000fb9-84aa-4122-8b03-9b7788855f67' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1b000fb9-84aa-4122-8b03-9b7788855f67' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.88 89.62 89.38 ... -89.62 -89.88</div><input id='attrs-9b5292c1-6a5d-47cd-86bb-e2cd455a94f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b5292c1-6a5d-47cd-86bb-e2cd455a94f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33eeb77c-0a85-4344-82c5-0a08be3752cc' class='xr-var-data-in' type='checkbox'><label for='data-33eeb77c-0a85-4344-82c5-0a08be3752cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_range :</span></dt><dd>[-90.0, 90.0]</dd></dl></div><div class='xr-var-data'><pre>array([ 89.875,  89.625,  89.375, ..., -89.375, -89.625, -89.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-58c32074-8aec-466f-948e-0a760ceb0107' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58c32074-8aec-466f-948e-0a760ceb0107' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da54d2ef-78be-489e-a53a-ad7ba44aaba4' class='xr-var-data-in' type='checkbox'><label for='data-da54d2ef-78be-489e-a53a-ad7ba44aaba4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_range :</span></dt><dd>[-180.0, 180.0]</dd></dl></div><div class='xr-var-data'><pre>array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1978-11-01 ... 2021-12-31</div><input id='attrs-1204d934-86ab-4ccd-9e73-c79beaf798b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1204d934-86ab-4ccd-9e73-c79beaf798b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df8184ea-8f0a-4c64-a497-b7abb306ca10' class='xr-var-data-in' type='checkbox'><label for='data-df8184ea-8f0a-4c64-a497-b7abb306ca10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1978-11-01T00:00:00.000000000&#x27;, &#x27;1978-11-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1978-11-03T00:00:00.000000000&#x27;, ..., &#x27;2021-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-30T00:00:00.000000000&#x27;, &#x27;2021-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-764b055d-f704-48c8-a544-ddd3212192a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-764b055d-f704-48c8-a544-ddd3212192a5' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>dnflag</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(16, 720, 720), meta=np.ndarray&gt;</div><input id='attrs-be1ad1e6-26a1-4e85-9c6f-7624e21f0d0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be1ad1e6-26a1-4e85-9c6f-7624e21f0d0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c0312f4-2f91-439e-b9bc-fc21bbd3abae' class='xr-var-data-in' type='checkbox'><label for='data-3c0312f4-2f91-439e-b9bc-fc21bbd3abae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd><dt><span>bit_meanings :</span></dt><dd>[&#x27;NaN&#x27;, &#x27;day&#x27;, &#x27;night&#x27;]</dd><dt><span>bits :</span></dt><dd>[&#x27;0b0&#x27;, &#x27;0b1&#x27;, &#x27;0b10&#x27;]</dd><dt><span>dtype :</span></dt><dd>int8</dd><dt><span>flag_meanings :</span></dt><dd>[&#x27;NaN&#x27;, &#x27;day&#x27;, &#x27;night&#x27;, &#x27;day_night_combination&#x27;]</dd><dt><span>flag_values :</span></dt><dd>[0, 1, 2, 3]</dd><dt><span>long_name :</span></dt><dd>Day / Night Flag</dd><dt><span>valid_range :</span></dt><dd>[0, 3]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 60.90 GiB </td>\n",
       "                        <td> 31.64 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (15767, 720, 1440) </td>\n",
       "                        <td> (16, 720, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1972 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flag</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(16, 720, 720), meta=np.ndarray&gt;</div><input id='attrs-74005477-8356-42c9-a507-f461e67c4f5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74005477-8356-42c9-a507-f461e67c4f5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11dd6f98-f7f2-4ea0-95c0-6c3c4118f988' class='xr-var-data-in' type='checkbox'><label for='data-11dd6f98-f7f2-4ea0-95c0-6c3c4118f988' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd><dt><span>bit_meanings :</span></dt><dd>[&#x27;no_data_inconsistency_detected&#x27;, &#x27;snow_coverage_or_temperature_below_zero&#x27;, &#x27;dense_vegetation&#x27;, &#x27;others_no_convergence_in_the_model_thus_no_valid_sm_estimates&#x27;, &#x27;soil_moisture_value_exceeds_physical_boundary&#x27;, &#x27;weight_of_measurement_below_threshold&#x27;, &#x27;all_datasets_deemed_unreliable&#x27;, &#x27;barren_ground_advisory_flag&#x27;, &#x27;NaN&#x27;]</dd><dt><span>bits :</span></dt><dd>[&#x27;0b0&#x27;, &#x27;0b1&#x27;, &#x27;0b10&#x27;, &#x27;0b100&#x27;, &#x27;0b1000&#x27;, &#x27;0b10000&#x27;, &#x27;0b100000&#x27;, &#x27;0b1000000&#x27;, &#x27;0b10000000&#x27;]</dd><dt><span>dtype :</span></dt><dd>int16</dd><dt><span>flag_meanings :</span></dt><dd>[&#x27;no_data_inconsistency_detected&#x27;, &#x27;snow_coverage_or_temperature_below_zero&#x27;, &#x27;dense_vegetation&#x27;, &#x27;combination_of_flag_values_1_and_2&#x27;, &#x27;soil_moisture_value_exceeds_physical_boundary&#x27;, &#x27;combination_of_flag_values_1_and_8&#x27;, &#x27;combination_of_flag_values_2_and_8&#x27;, &#x27;barren_ground_advisory_flag&#x27;, &#x27;combination_of_flag_values_1_and_64&#x27;, &#x27;combination_of_flag_values_2_and_64&#x27;, &#x27;combination_of_flag_values_1_and_2_and_64&#x27;, &#x27;combination_of_flag_values_8_and_64&#x27;, &#x27;combination_of_flag_values_1_and_8_and_64&#x27;, &#x27;combination_of_flag_values_2_and_8_and_64&#x27;, &#x27;combination_of_flag_values_1_and_2_and_4_and_8_and_16_and_32_and_64&#x27;]</dd><dt><span>flag_values :</span></dt><dd>[0, 1, 2, 3, 8, 9, 10, 64, 65, 66, 67, 72, 73, 74, 127]</dd><dt><span>long_name :</span></dt><dd>Flag</dd><dt><span>standard_name :</span></dt><dd>soil_moisture_content status_flag</dd><dt><span>valid_range :</span></dt><dd>[0, 255]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 60.90 GiB </td>\n",
       "                        <td> 31.64 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (15767, 720, 1440) </td>\n",
       "                        <td> (16, 720, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1972 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                        <th> Bytes </th>\n",
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       "                        <th> Bytes </th>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 60.90 GiB </td>\n",
       "                        <td> 31.64 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (15767, 720, 1440) </td>\n",
       "                        <td> (16, 720, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1972 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t0</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(16, 720, 720), meta=np.ndarray&gt;</div><input id='attrs-04e1ee2d-0daa-45ad-9b6e-0661e0d583f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04e1ee2d-0daa-45ad-9b6e-0661e0d583f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b348585-639a-4c24-a886-0f3a25160678' class='xr-var-data-in' type='checkbox'><label for='data-7b348585-639a-4c24-a886-0f3a25160678' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd><dt><span>dtype :</span></dt><dd>float64</dd><dt><span>long_name :</span></dt><dd>Observation Timestamp</dd><dt><span>units :</span></dt><dd>days after 1970-01-01 00:00:00 UTC</dd><dt><span>valid_range :</span></dt><dd>[3225.0, 3227.0]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 121.80 GiB </td>\n",
       "                        <td> 63.28 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (15767, 720, 1440) </td>\n",
       "                        <td> (16, 720, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1972 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c2f388e-9caa-4f5d-93cd-4e24c20977f6' class='xr-section-summary-in' type='checkbox'  ><label for='section-6c2f388e-9caa-4f5d-93cd-4e24c20977f6' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b8c4c4c5-f67d-4066-8e89-f2e7a78cc202' class='xr-index-data-in' type='checkbox'/><label for='index-b8c4c4c5-f67d-4066-8e89-f2e7a78cc202' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 89.875,  89.625,  89.375,  89.125,  88.875,  88.625,  88.375,  88.125,\n",
       "        87.875,  87.625,\n",
       "       ...\n",
       "       -87.625, -87.875, -88.125, -88.375, -88.625, -88.875, -89.125, -89.375,\n",
       "       -89.625, -89.875],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7960a475-c9eb-41cc-bc48-9c95036015bc' class='xr-index-data-in' type='checkbox'/><label for='index-7960a475-c9eb-41cc-bc48-9c95036015bc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625, -178.375,\n",
       "       -178.125, -177.875, -177.625,\n",
       "       ...\n",
       "        177.625,  177.875,  178.125,  178.375,  178.625,  178.875,  179.125,\n",
       "        179.375,  179.625,  179.875],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-3d25be9d-2a4e-4773-9630-524ec1767868' class='xr-index-data-in' type='checkbox'/><label for='index-3d25be9d-2a4e-4773-9630-524ec1767868' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1978-11-01&#x27;, &#x27;1978-11-02&#x27;, &#x27;1978-11-03&#x27;, &#x27;1978-11-04&#x27;,\n",
       "               &#x27;1978-11-05&#x27;, &#x27;1978-11-06&#x27;, &#x27;1978-11-07&#x27;, &#x27;1978-11-08&#x27;,\n",
       "               &#x27;1978-11-09&#x27;, &#x27;1978-11-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-12-22&#x27;, &#x27;2021-12-23&#x27;, &#x27;2021-12-24&#x27;, &#x27;2021-12-25&#x27;,\n",
       "               &#x27;2021-12-26&#x27;, &#x27;2021-12-27&#x27;, &#x27;2021-12-28&#x27;, &#x27;2021-12-29&#x27;,\n",
       "               &#x27;2021-12-30&#x27;, &#x27;2021-12-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=15767, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bdf069cf-7d0c-4b78-8a29-bd645e6e0ff5' class='xr-section-summary-in' type='checkbox'  ><label for='section-bdf069cf-7d0c-4b78-8a29-bd645e6e0ff5' class='xr-section-summary' >Attributes: <span>(44)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.9</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>This dataset was produced with funding of the ESA CCI+ Soil Moisture project; ESRIN Contract No: 4000126684/19/I-NB</dd><dt><span>contact :</span></dt><dd>cci_sm_contact@eodc.eu</dd><dt><span>creator_email :</span></dt><dd>cci_sm_developer@eodc.eu</dd><dt><span>creator_name :</span></dt><dd>Department of Geodesy and Geoinformation, Vienna University of Technology</dd><dt><span>creator_url :</span></dt><dd>https://climers.geo.tuwien.ac.at/</dd><dt><span>date_created :</span></dt><dd>File created: 2022-04-06 12:31:39.526359</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25 degree</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25 degree</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>0.0</dd><dt><span>history :</span></dt><dd>2022-04-06 12:29:35 - product produced\n",
       "2023-08-09 17:33:51 - converted by nc2zarr, version 1.1.2.dev0</dd><dt><span>id :</span></dt><dd>ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-20211231000000-fv07.1.nc</dd><dt><span>institution :</span></dt><dd>TU Wien (AUT); VanderSat B.V. (NL)</dd><dt><span>key_variables :</span></dt><dd>sm</dd><dt><span>keywords :</span></dt><dd>soil moisture, soil water content, microwave remote sensing, active, passive, combined, climate data record</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>Data use is free and open for all registered users.</dd><dt><span>naming_authority :</span></dt><dd>TU Wien</dd><dt><span>platform :</span></dt><dd>Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1, MIRAS, SMAP, GPM, FengYun-3B, FengYun-3C, FengYun-3D; ERS-1, ERS-2, METOP-A, METOP-B</dd><dt><span>processing_level :</span></dt><dd>Quality-controlled, super-collocated (L3S) Satellite Soil Moisture (SM) data from multiple sensors</dd><dt><span>product_version :</span></dt><dd>v07.1</dd><dt><span>project :</span></dt><dd>Climate Change Initiative - European Space Agency</dd><dt><span>references :</span></dt><dd>http://www.esa-soilmoisture-cci.org; Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F.,  Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017) ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001; Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dorigo, W. (2019) Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth System Science Data 11, 717-739, https://doi.org/10.5194/essd-11-717-2019; Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. https://doi.org/10.1109/TGRS.2017.2734070</dd><dt><span>sensor :</span></dt><dd>SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP_radiometer, GMI, VIRR-3B, VIRR-3C, VIRR-3D; AMI-WS, ASCAT-A, ASCAT-B, ASCAT-C</dd><dt><span>source :</span></dt><dd>WARP 5.5R1.1/AMI-WS/ERS12 Level 2 Soil Moisture; WARP 5.4R1.0/AMI-WS/ERS2 Level 2 Soil Moisture; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling;; LPRMv7/SMMR/Nimbus 7 L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/SSMI/F08, F11, F13 DMSP L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/TMI/TRMM L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/AMSR-E/Aqua L2B Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/WINDSAT/CORIOLIS L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/AMSR2/GCOM-W1 L3 Surface Soil Moisture, Ancillary Params; LPRMv7/SMOS/MIRAS L3 Surface Soil Moisture, CATDS Level 3 Brightness Temperatures (L3TB) version 300 RE03 &amp; RE04; LPRMv7/SMAP_radiometer/SMAP L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/GMI/GPM L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3B L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3C L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3D L3 Surface Soil Moisture, Ancillary Params, and quality flags;;</dd><dt><span>spatial_resolution :</span></dt><dd>25km</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table v77</dd><dt><span>summary :</span></dt><dd>This dataset was produced with funding of the ESA CCI+ Soil Moisture project; ESRIN Contract No: 4000126684/19/I-NB</dd><dt><span>time_coverage_duration :</span></dt><dd>P43Y</dd><dt><span>time_coverage_end :</span></dt><dd>2021-12-31 00:00:00</dd><dt><span>time_coverage_end_product :</span></dt><dd>20211231T235959Z</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1D</dd><dt><span>time_coverage_start :</span></dt><dd>1978-11-01 00:00:00</dd><dt><span>time_coverage_start_product :</span></dt><dd>19781101T000000Z</dd><dt><span>title :</span></dt><dd>ESA CCI Surface Soil Moisture COMBINED active+passive Product</dd><dt><span>tracking_id :</span></dt><dd>ad35798e-58e0-488f-b5b9-593874a47700</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.Dataset> Size: 654GB\n",
       "Dimensions:         (time: 15767, lat: 720, lon: 1440)\n",
       "Coordinates:\n",
       "  * lat             (lat) float64 6kB 89.88 89.62 89.38 ... -89.38 -89.62 -89.88\n",
       "  * lon             (lon) float64 12kB -179.9 -179.6 -179.4 ... 179.6 179.9\n",
       "  * time            (time) datetime64[ns] 126kB 1978-11-01 ... 2021-12-31\n",
       "Data variables:\n",
       "    dnflag          (time, lat, lon) float32 65GB dask.array<chunksize=(16, 720, 720), meta=np.ndarray>\n",
       "    flag            (time, lat, lon) float32 65GB dask.array<chunksize=(16, 720, 720), meta=np.ndarray>\n",
       "    freqbandID      (time, lat, lon) float32 65GB dask.array<chunksize=(16, 720, 720), meta=np.ndarray>\n",
       "    mode            (time, lat, lon) float32 65GB dask.array<chunksize=(16, 720, 720), meta=np.ndarray>\n",
       "    sensor          (time, lat, lon) float64 131GB dask.array<chunksize=(16, 720, 720), meta=np.ndarray>\n",
       "    sm              (time, lat, lon) float32 65GB dask.array<chunksize=(16, 720, 720), meta=np.ndarray>\n",
       "    sm_uncertainty  (time, lat, lon) float32 65GB dask.array<chunksize=(16, 720, 720), meta=np.ndarray>\n",
       "    t0              (time, lat, lon) float64 131GB dask.array<chunksize=(16, 720, 720), meta=np.ndarray>\n",
       "Attributes: (12/44)\n",
       "    Conventions:                  CF-1.9\n",
       "    cdm_data_type:                Grid\n",
       "    comment:                      This dataset was produced with funding of t...\n",
       "    contact:                      cci_sm_contact@eodc.eu\n",
       "    creator_email:                cci_sm_developer@eodc.eu\n",
       "    creator_name:                 Department of Geodesy and Geoinformation, V...\n",
       "    ...                           ...\n",
       "    time_coverage_end_product:    20211231T235959Z\n",
       "    time_coverage_resolution:     P1D\n",
       "    time_coverage_start:          1978-11-01 00:00:00\n",
       "    time_coverage_start_product:  19781101T000000Z\n",
       "    title:                        ESA CCI Surface Soil Moisture COMBINED acti...\n",
       "    tracking_id:                  ad35798e-58e0-488f-b5b9-593874a47700"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube = zarr_store.open_data('ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-1978-2021-fv07.1.zarr')\n",
    "cube"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subsets of the data may easily be created like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
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       "  padding-bottom: 5px !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
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       ".xr-var-data > table {\n",
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       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
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       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
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       "dl.xr-attrs {\n",
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       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2GB\n",
       "Dimensions:         (time: 366, lat: 324, lon: 324)\n",
       "Coordinates:\n",
       "  * lat             (lat) float64 3kB 40.38 40.12 39.88 ... -39.88 -40.12 -40.38\n",
       "  * lon             (lon) float64 3kB -23.38 -23.12 -22.88 ... 56.88 57.12 57.38\n",
       "  * time            (time) datetime64[ns] 3kB 2000-01-01 ... 2000-12-31\n",
       "Data variables:\n",
       "    dnflag          (time, lat, lon) float32 154MB dask.array&lt;chunksize=(13, 324, 94), meta=np.ndarray&gt;\n",
       "    flag            (time, lat, lon) float32 154MB dask.array&lt;chunksize=(13, 324, 94), meta=np.ndarray&gt;\n",
       "    freqbandID      (time, lat, lon) float32 154MB dask.array&lt;chunksize=(13, 324, 94), meta=np.ndarray&gt;\n",
       "    mode            (time, lat, lon) float32 154MB dask.array&lt;chunksize=(13, 324, 94), meta=np.ndarray&gt;\n",
       "    sensor          (time, lat, lon) float64 307MB dask.array&lt;chunksize=(13, 324, 94), meta=np.ndarray&gt;\n",
       "    sm              (time, lat, lon) float32 154MB dask.array&lt;chunksize=(13, 324, 94), meta=np.ndarray&gt;\n",
       "    sm_uncertainty  (time, lat, lon) float32 154MB dask.array&lt;chunksize=(13, 324, 94), meta=np.ndarray&gt;\n",
       "    t0              (time, lat, lon) float64 307MB dask.array&lt;chunksize=(13, 324, 94), meta=np.ndarray&gt;\n",
       "Attributes: (12/44)\n",
       "    Conventions:                  CF-1.9\n",
       "    cdm_data_type:                Grid\n",
       "    comment:                      This dataset was produced with funding of t...\n",
       "    contact:                      cci_sm_contact@eodc.eu\n",
       "    creator_email:                cci_sm_developer@eodc.eu\n",
       "    creator_name:                 Department of Geodesy and Geoinformation, V...\n",
       "    ...                           ...\n",
       "    time_coverage_end_product:    20211231T235959Z\n",
       "    time_coverage_resolution:     P1D\n",
       "    time_coverage_start:          1978-11-01 00:00:00\n",
       "    time_coverage_start_product:  19781101T000000Z\n",
       "    title:                        ESA CCI Surface Soil Moisture COMBINED acti...\n",
       "    tracking_id:                  ad35798e-58e0-488f-b5b9-593874a47700</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-beb4e43c-3618-44c1-9a25-055f1626e936' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-beb4e43c-3618-44c1-9a25-055f1626e936' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 366</li><li><span class='xr-has-index'>lat</span>: 324</li><li><span class='xr-has-index'>lon</span>: 324</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e729e28f-1aa7-4026-865d-4e2ccbe8279f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e729e28f-1aa7-4026-865d-4e2ccbe8279f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>40.38 40.12 39.88 ... -40.12 -40.38</div><input id='attrs-a088edb4-5237-4821-9152-93112ebd9714' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a088edb4-5237-4821-9152-93112ebd9714' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-484bc118-e96e-4bed-9eba-2f84e3228fab' class='xr-var-data-in' type='checkbox'><label for='data-484bc118-e96e-4bed-9eba-2f84e3228fab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_range :</span></dt><dd>[-90.0, 90.0]</dd></dl></div><div class='xr-var-data'><pre>array([ 40.375,  40.125,  39.875, ..., -39.875, -40.125, -40.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-23.38 -23.12 ... 57.12 57.38</div><input id='attrs-a2aa4d57-80b5-4ea5-ac8e-be898e07ad7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2aa4d57-80b5-4ea5-ac8e-be898e07ad7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f90732b5-6059-4bc2-a2f7-2a6652f85919' class='xr-var-data-in' type='checkbox'><label for='data-f90732b5-6059-4bc2-a2f7-2a6652f85919' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_range :</span></dt><dd>[-180.0, 180.0]</dd></dl></div><div class='xr-var-data'><pre>array([-23.375, -23.125, -22.875, ...,  56.875,  57.125,  57.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2000-12-31</div><input id='attrs-017d9971-dfb3-4532-bcba-ed442029eba5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-017d9971-dfb3-4532-bcba-ed442029eba5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2297f4f4-22c2-43ca-a2cf-5d49c42f2650' class='xr-var-data-in' type='checkbox'><label for='data-2297f4f4-22c2-43ca-a2cf-5d49c42f2650' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-01-03T00:00:00.000000000&#x27;, ..., &#x27;2000-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-12-30T00:00:00.000000000&#x27;, &#x27;2000-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-76a594b6-e781-4d9b-8645-ef646cb942f5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-76a594b6-e781-4d9b-8645-ef646cb942f5' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>dnflag</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 324, 94), meta=np.ndarray&gt;</div><input id='attrs-dee14ee0-9ae6-4b95-a444-51b77db41261' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dee14ee0-9ae6-4b95-a444-51b77db41261' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ed84cce-ec2f-4afb-80d3-85f1ce89013d' class='xr-var-data-in' type='checkbox'><label for='data-2ed84cce-ec2f-4afb-80d3-85f1ce89013d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon</dd><dt><span>bit_meanings :</span></dt><dd>[&#x27;NaN&#x27;, &#x27;day&#x27;, &#x27;night&#x27;]</dd><dt><span>bits :</span></dt><dd>[&#x27;0b0&#x27;, &#x27;0b1&#x27;, &#x27;0b10&#x27;]</dd><dt><span>dtype :</span></dt><dd>int8</dd><dt><span>flag_meanings :</span></dt><dd>[&#x27;NaN&#x27;, &#x27;day&#x27;, &#x27;night&#x27;, &#x27;day_night_combination&#x27;]</dd><dt><span>flag_values :</span></dt><dd>[0, 1, 2, 3]</dd><dt><span>long_name :</span></dt><dd>Day / Night Flag</dd><dt><span>valid_range :</span></dt><dd>[0, 3]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                        <td> </td>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 146.57 MiB </td>\n",
       "                        <td> 4.55 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (366, 324, 324) </td>\n",
       "                        <td> (16, 324, 230) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 48 chunks in 3 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-fa30e06c-454e-4326-bf34-5c6a6a34bad9' class='xr-section-summary-in' type='checkbox'  ><label for='section-fa30e06c-454e-4326-bf34-5c6a6a34bad9' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6b86f1a1-cfe3-460f-b039-4fa8697a83f4' class='xr-index-data-in' type='checkbox'/><label for='index-6b86f1a1-cfe3-460f-b039-4fa8697a83f4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 40.375,  40.125,  39.875,  39.625,  39.375,  39.125,  38.875,  38.625,\n",
       "        38.375,  38.125,\n",
       "       ...\n",
       "       -38.125, -38.375, -38.625, -38.875, -39.125, -39.375, -39.625, -39.875,\n",
       "       -40.125, -40.375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=324))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b0a2e085-b745-4a65-ab28-e470a91fe640' class='xr-index-data-in' type='checkbox'/><label for='index-b0a2e085-b745-4a65-ab28-e470a91fe640' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-23.375, -23.125, -22.875, -22.625, -22.375, -22.125, -21.875, -21.625,\n",
       "       -21.375, -21.125,\n",
       "       ...\n",
       "        55.125,  55.375,  55.625,  55.875,  56.125,  56.375,  56.625,  56.875,\n",
       "        57.125,  57.375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=324))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-641adbab-db6f-4cbe-a50e-1cb1f2374616' class='xr-index-data-in' type='checkbox'/><label for='index-641adbab-db6f-4cbe-a50e-1cb1f2374616' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-01&#x27;, &#x27;2000-01-02&#x27;, &#x27;2000-01-03&#x27;, &#x27;2000-01-04&#x27;,\n",
       "               &#x27;2000-01-05&#x27;, &#x27;2000-01-06&#x27;, &#x27;2000-01-07&#x27;, &#x27;2000-01-08&#x27;,\n",
       "               &#x27;2000-01-09&#x27;, &#x27;2000-01-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2000-12-22&#x27;, &#x27;2000-12-23&#x27;, &#x27;2000-12-24&#x27;, &#x27;2000-12-25&#x27;,\n",
       "               &#x27;2000-12-26&#x27;, &#x27;2000-12-27&#x27;, &#x27;2000-12-28&#x27;, &#x27;2000-12-29&#x27;,\n",
       "               &#x27;2000-12-30&#x27;, &#x27;2000-12-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=366, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-30a36949-476e-4aa8-8d3f-f4b0407c026e' class='xr-section-summary-in' type='checkbox'  ><label for='section-30a36949-476e-4aa8-8d3f-f4b0407c026e' class='xr-section-summary' >Attributes: <span>(44)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.9</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>This dataset was produced with funding of the ESA CCI+ Soil Moisture project; ESRIN Contract No: 4000126684/19/I-NB</dd><dt><span>contact :</span></dt><dd>cci_sm_contact@eodc.eu</dd><dt><span>creator_email :</span></dt><dd>cci_sm_developer@eodc.eu</dd><dt><span>creator_name :</span></dt><dd>Department of Geodesy and Geoinformation, Vienna University of Technology</dd><dt><span>creator_url :</span></dt><dd>https://climers.geo.tuwien.ac.at/</dd><dt><span>date_created :</span></dt><dd>File created: 2022-04-06 12:31:39.526359</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25 degree</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25 degree</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>0.0</dd><dt><span>history :</span></dt><dd>2022-04-06 12:29:35 - product produced\n",
       "2023-08-09 17:33:51 - converted by nc2zarr, version 1.1.2.dev0</dd><dt><span>id :</span></dt><dd>ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-20211231000000-fv07.1.nc</dd><dt><span>institution :</span></dt><dd>TU Wien (AUT); VanderSat B.V. (NL)</dd><dt><span>key_variables :</span></dt><dd>sm</dd><dt><span>keywords :</span></dt><dd>soil moisture, soil water content, microwave remote sensing, active, passive, combined, climate data record</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>Data use is free and open for all registered users.</dd><dt><span>naming_authority :</span></dt><dd>TU Wien</dd><dt><span>platform :</span></dt><dd>Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1, MIRAS, SMAP, GPM, FengYun-3B, FengYun-3C, FengYun-3D; ERS-1, ERS-2, METOP-A, METOP-B</dd><dt><span>processing_level :</span></dt><dd>Quality-controlled, super-collocated (L3S) Satellite Soil Moisture (SM) data from multiple sensors</dd><dt><span>product_version :</span></dt><dd>v07.1</dd><dt><span>project :</span></dt><dd>Climate Change Initiative - European Space Agency</dd><dt><span>references :</span></dt><dd>http://www.esa-soilmoisture-cci.org; Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F.,  Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017) ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001; Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dorigo, W. (2019) Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth System Science Data 11, 717-739, https://doi.org/10.5194/essd-11-717-2019; Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. https://doi.org/10.1109/TGRS.2017.2734070</dd><dt><span>sensor :</span></dt><dd>SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP_radiometer, GMI, VIRR-3B, VIRR-3C, VIRR-3D; AMI-WS, ASCAT-A, ASCAT-B, ASCAT-C</dd><dt><span>source :</span></dt><dd>WARP 5.5R1.1/AMI-WS/ERS12 Level 2 Soil Moisture; WARP 5.4R1.0/AMI-WS/ERS2 Level 2 Soil Moisture; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling;; LPRMv7/SMMR/Nimbus 7 L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/SSMI/F08, F11, F13 DMSP L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/TMI/TRMM L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/AMSR-E/Aqua L2B Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/WINDSAT/CORIOLIS L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/AMSR2/GCOM-W1 L3 Surface Soil Moisture, Ancillary Params; LPRMv7/SMOS/MIRAS L3 Surface Soil Moisture, CATDS Level 3 Brightness Temperatures (L3TB) version 300 RE03 &amp; RE04; LPRMv7/SMAP_radiometer/SMAP L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/GMI/GPM L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3B L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3C L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3D L3 Surface Soil Moisture, Ancillary Params, and quality flags;;</dd><dt><span>spatial_resolution :</span></dt><dd>25km</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table v77</dd><dt><span>summary :</span></dt><dd>This dataset was produced with funding of the ESA CCI+ Soil Moisture project; ESRIN Contract No: 4000126684/19/I-NB</dd><dt><span>time_coverage_duration :</span></dt><dd>P43Y</dd><dt><span>time_coverage_end :</span></dt><dd>2021-12-31 00:00:00</dd><dt><span>time_coverage_end_product :</span></dt><dd>20211231T235959Z</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1D</dd><dt><span>time_coverage_start :</span></dt><dd>1978-11-01 00:00:00</dd><dt><span>time_coverage_start_product :</span></dt><dd>19781101T000000Z</dd><dt><span>title :</span></dt><dd>ESA CCI Surface Soil Moisture COMBINED active+passive Product</dd><dt><span>tracking_id :</span></dt><dd>ad35798e-58e0-488f-b5b9-593874a47700</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 2GB\n",
       "Dimensions:         (time: 366, lat: 324, lon: 324)\n",
       "Coordinates:\n",
       "  * lat             (lat) float64 3kB 40.38 40.12 39.88 ... -39.88 -40.12 -40.38\n",
       "  * lon             (lon) float64 3kB -23.38 -23.12 -22.88 ... 56.88 57.12 57.38\n",
       "  * time            (time) datetime64[ns] 3kB 2000-01-01 ... 2000-12-31\n",
       "Data variables:\n",
       "    dnflag          (time, lat, lon) float32 154MB dask.array<chunksize=(13, 324, 94), meta=np.ndarray>\n",
       "    flag            (time, lat, lon) float32 154MB dask.array<chunksize=(13, 324, 94), meta=np.ndarray>\n",
       "    freqbandID      (time, lat, lon) float32 154MB dask.array<chunksize=(13, 324, 94), meta=np.ndarray>\n",
       "    mode            (time, lat, lon) float32 154MB dask.array<chunksize=(13, 324, 94), meta=np.ndarray>\n",
       "    sensor          (time, lat, lon) float64 307MB dask.array<chunksize=(13, 324, 94), meta=np.ndarray>\n",
       "    sm              (time, lat, lon) float32 154MB dask.array<chunksize=(13, 324, 94), meta=np.ndarray>\n",
       "    sm_uncertainty  (time, lat, lon) float32 154MB dask.array<chunksize=(13, 324, 94), meta=np.ndarray>\n",
       "    t0              (time, lat, lon) float64 307MB dask.array<chunksize=(13, 324, 94), meta=np.ndarray>\n",
       "Attributes: (12/44)\n",
       "    Conventions:                  CF-1.9\n",
       "    cdm_data_type:                Grid\n",
       "    comment:                      This dataset was produced with funding of t...\n",
       "    contact:                      cci_sm_contact@eodc.eu\n",
       "    creator_email:                cci_sm_developer@eodc.eu\n",
       "    creator_name:                 Department of Geodesy and Geoinformation, V...\n",
       "    ...                           ...\n",
       "    time_coverage_end_product:    20211231T235959Z\n",
       "    time_coverage_resolution:     P1D\n",
       "    time_coverage_start:          1978-11-01 00:00:00\n",
       "    time_coverage_start_product:  19781101T000000Z\n",
       "    title:                        ESA CCI Surface Soil Moisture COMBINED acti...\n",
       "    tracking_id:                  ad35798e-58e0-488f-b5b9-593874a47700"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_cube = cube.sel({\n",
    "    'lat': slice(40.40, -40.40),\n",
    "    'lon': slice(-23.40, 57.40),\n",
    "    'time': slice('2000-01-01', '2000-12-31')\n",
    "    }\n",
    ")\n",
    "sub_cube"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and we can plot the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe83bff1d30>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub_cube.sm.sel(time='2000-07-01 12:00:00', method='nearest').plot.imshow(cmap='Greys_r', figsize=(8, 8))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}