examples/notebooks/datastores/7_cmems_data_store.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c65134c1-6961-4497-b1aa-7ba6f5497aae",
   "metadata": {},
   "source": [
    "## xcube CMEMS Data Store "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b35e6b-22c6-4977-a286-b26e1db65f66",
   "metadata": {},
   "source": [
    "*Please checkout the general introduction to xcube data stores in the Jupyter Notebook Getting Started before jumping into this notebook :)*\n",
    "\n",
    "This notebook provides a walk-through demonstrating how to use xcube and the xcube-cmems plugin to read and explore data from the CMEMS API.\n",
    "\n",
    "In order to run this notebook you need to install the xcube_cmems plugin. You may do so by executing the following line in your terminal:\n",
    "\n",
    "``` $  conda install -c conda-forge xcube-cmems ```\n",
    "\n",
    "*Or you can install the xcube_cmems plugin from sources by following the instructions on https://github.com/dcs4cop/xcube-cmems.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "214307bc-7def-433f-876e-28540a6c2fe7",
   "metadata": {},
   "source": [
    "set `CMEMS_USERNAME` and `CMEMS_PASSWORD` as environment variables\n",
    "If you do not have cmems user yet, you can register for an [account](https://resources.marine.copernicus.eu/registration-form)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6853a608-7671-4e17-b5d3-b76646fb7f0c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "# os.environ[\"CMEMS_USERNAME\"] =  \"\"\n",
    "# os.environ[\"CMEMS_PASSWORD\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "27cc3635-2205-4198-b395-c6f41fd5be70",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# mandatory imports\n",
    "from xcube.core.store import find_data_store_extensions\n",
    "from xcube.core.store import get_data_store_params_schema\n",
    "from xcube.core.store import new_data_store\n",
    "\n",
    "# Utilities for notebook visualization\n",
    "from IPython.display import JSON"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07588f05-9208-4e5d-99ba-a43c6d785ee7",
   "metadata": {},
   "source": [
    "Check whether the `cmems` store is among the available stores, if not please follow the installation information from the top of this notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "05ba150a-c235-4c3a-8074-bae302ee2f06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "cmems": {
        "description": "Cmems DataStore"
       },
       "file": {
        "description": "Data store that uses a local filesystem"
       },
       "memory": {
        "description": "Data store that uses a in-memory filesystem"
       },
       "s3": {
        "description": "Data store that uses a AWS S3 compatible object storage"
       }
      },
      "text/plain": [
       "<IPython.core.display.JSON object>"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "application/json": {
       "expanded": false,
       "root": "root"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JSON({e.name: e.metadata for e in find_data_store_extensions()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fc036ec9-042a-446d-b452-b3e477b06de6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "additionalProperties": false,
       "properties": {
        "cas_url": {
         "default": "https://cmems-cas.cls.fr/cas/login",
         "type": "string"
        },
        "cmems_password": {
         "description": "Preferably set by environment variable CMEMS_PASSWORD",
         "title": "CMEMS User Password",
         "type": "string"
        },
        "cmems_username": {
         "description": "Preferably set by environment variable CMEMS_USERNAME ",
         "title": "CMEMS Username",
         "type": "string"
        },
        "csw_url": {
         "default": "https://cmems-catalog-ro.cls.fr/geonetwork/srv/eng/csw-MYOCEAN-CORE-PRODUCTS?",
         "type": "string"
        },
        "databases": {
         "default": [
          "nrt",
          "my"
         ],
         "type": "string"
        },
        "server": {
         "default": "cmems-du.eu/thredds/dodsC/",
         "type": "string"
        }
       },
       "type": "object"
      },
      "text/plain": [
       "<xcube.util.jsonschema.JsonObjectSchema at 0x7ffa787f0610>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_data_store_params_schema('cmems')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "57287c3c-7bd5-42d2-b768-ba5380b6069f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xcube_cmems.store.CmemsDataStore at 0x7ffa7879ca90>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store = new_data_store('cmems')\n",
    "store"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d912e0d-01d0-4741-b8ed-c3cb476d833c",
   "metadata": {},
   "source": [
    "Get all the data ids in `cmems` datastore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af82449a-60d7-4465-9233-1e45e3d4c1c3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/json": [
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       "cmems_obs-si_ant_physic_my_drift-amsr_P3D",
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       "cmems_mod_arc_bgc_my_ecosmo_P1D-m",
       "cmems_mod_arc_bgc_my_ecosmo_P1M",
       "cmems_mod_arc_bgc_my_ecosmo_P1Y",
       "cmems_obs-oc_arc_bgc-plankton_nrt_l3-multi-1km_P1D",
       "cmems_obs-oc_arc_bgc-plankton_nrt_l3-olci-300m_P1D",
       "cmems_obs-oc_arc_bgc-reflectance_nrt_l3-multi-1km_P1D",
       "cmems_obs-oc_arc_bgc-reflectance_nrt_l3-olci-300m_P1D",
       "cmems_obs-oc_arc_bgc-transp_nrt_l3-multi-1km_P1D",
       "cmems_obs-oc_arc_bgc-transp_nrt_l3-olci-300m_P1D",
       "cmems_obs-oc_arc_bgc-optics_nrt_l3-multi-1km_P1D",
       "cmems_obs-oc_arc_bgc-optics_my_l3-multi-1km_P1M",
       "cmems_obs-oc_arc_bgc-plankton_my_l3-multi-1km_P1M",
       "cmems_obs-oc_arc_bgc-plankton_my_l3-olci-300m_P1D",
       "cmems_obs-oc_arc_bgc-reflectance_my_l3-multi-1km_P1M",
       "cmems_obs-oc_arc_bgc-reflectance_my_l3-olci-300m_P1D",
       "cmems_obs-oc_arc_bgc-transp_my_l3-multi-1km_P1M",
       "cmems_obs-oc_arc_bgc-transp_my_l3-olci-300m_P1D",
       "cmems_obs-oc_arc_bgc-plankton_nrt_l4-multi-1km_P1M",
       "cmems_obs-oc_arc_bgc-plankton_nrt_l4-olci-300m_P1M",
       "cmems_obs-oc_arc_bgc-transp_nrt_l4-olci-300m_P1M",
       "cmems_obs-oc_arc_bgc-plankton_my_l4-multi-1km_P1M",
       "cmems_obs-oc_arc_bgc-plankton_my_l4-olci-300m_P1M",
       "cmems_obs-si_arc_phy_nrt_l3_P1D",
       "cmems_obs-si_arc_phy-icetype_nrt_L4-auto_P1D",
       "cmems_obs-si_arc_phy-siconc_nrt_L4-auto_P1D",
       "cmems_obs-ins_arc_phybgcwav_mynrt_pointserie-latest_irr.html",
       "cmems_obs-ins_arc_phybgcwav_mynrt_profile-latest_irr.html",
       "cmems_obs-ins_arc_phybgcwav_mynrt_trajectory-latest_irr.html",
       "cmems_mod_arc_phy_anfc_topaz4_P1M-m",
       "dataset-topaz4-arc-1hr-myoceanv2-be",
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       "METNO-ARC-SEAICE_CONC-L4-NRT-OBS",
       "CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE",
       "CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE",
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       "CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE",
       "CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE",
       "CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE",
       "CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE",
       "CERSAT-GLO-SEAICE_6DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE",
       "cmems_obs-si_arc_phy_my_drift-amsr_P2D",
       "cmems_obs-si_arc_phy_my_drift-amsr_P3D",
       "cmems_obs-si_arc_phy_my_drift-amsr_P6D",
       "cdr_ice_thick_nh",
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       "dataset-wam-arctic-1hr3km-be",
       "cmems_mod_arc_wav_my_3km_PT1H-i",
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       "cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m",
       "cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m",
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       "cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m",
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       "cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m",
       "cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m",
       "cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m",
       "cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m",
       "cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m",
       "cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m",
       "cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m",
       "cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m",
       "cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m",
       "cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m",
       "cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m",
       "cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m",
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       "cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m",
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       "cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m",
       "cmems_obs-ins_nws_phybgcwav_mynrt_pointserie-latest_irr.html",
       "cmems_obs-ins_nws_phybgcwav_mynrt_profile-latest_irr.html",
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       "MetO-NWS-PHY-dm-BED",
       "MetO-NWS-PHY-dm-CUR",
       "MetO-NWS-PHY-dm-MLD",
       "MetO-NWS-PHY-dm-SAL",
       "MetO-NWS-PHY-dm-TEM",
       "MetO-NWS-PHY-hi-BED",
       "MetO-NWS-PHY-hi-CUR",
       "MetO-NWS-PHY-hi-MLD",
       "MetO-NWS-PHY-hi-SAL",
       "MetO-NWS-PHY-hi-SSC",
       "MetO-NWS-PHY-hi-SSH",
       "MetO-NWS-PHY-hi-SSS",
       "MetO-NWS-PHY-hi-SST",
       "MetO-NWS-PHY-hi-TEM",
       "MetO-NWS-PHY-qh-SSC",
       "MetO-NWS-PHY-qh-SSH",
       "cmems_mod_nws_phy-bottomt_anfc_7km-2D_P1D-m",
       "cmems_mod_nws_phy-mld_anfc_7km-2D_P1D-m",
       "cmems_mod_nws_phy-s_anfc_7km-3D_P1D-m",
       "cmems_mod_nws_phy-ssh_anfc_7km-2D_P1D-m",
       "cmems_mod_nws_phy-t_anfc_7km-3D_P1D-m",
       "cmems_mod_nws_phy-uv_anfc_7km-3D_P1D-m",
       "cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i",
       "cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i",
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       "cmems_mod_nws_phy-uv_my_7km-3D_P1M-m",
       "cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m",
       "MetO-NWS-WAV-hi",
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       "cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m",
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       "cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m",
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       "cmems_obs-ins_ibi_phybgcwav_mynrt_trajectory-latest_irr.html",
       "cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m",
       "cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m",
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       "cmems_obs-sst_bal_phy_my_l3s_P1D-m",
       "cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D",
       "cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D",
       "cmems_obs-oc_bal_bgc-plankton_my_l3-olci-300m_P1D",
       "cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D",
       "cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D",
       "cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D",
       "cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D",
       "cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M",
       "cmems_obs-oc_bal_bgc-plankton_my_l4-olci-300m_P1M",
       "cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D",
       "cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D",
       "cmems_obs-oc_bal_bgc-reflectance_nrt_l3-olci-300m_P1D",
       "cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D",
       "cmems_mod_bal_phy_anfc_PT15m-i",
       "cmems_mod_bal_phy_anfc_P1D-m",
       "cmems_mod_bal_phy_anfc_PT1h-i",
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       "cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1M",
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   "id": "4df9dd30-c230-4bdb-8d5d-725deeb36753",
   "metadata": {},
   "source": [
    "Get more info about a specific dataset."
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "96804082-5f50-4911-b176-af5fbe0a97ca",
   "metadata": {
    "tags": []
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   "outputs": [
    {
     "data": {
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        "Conventions": "CF-1.6",
        "Metadata_Conventions": "Unidata Dataset Discovery v1.0",
        "acknowledgment": "Please acknowledge the use of these data with the following statement: These data were provided by DMI and GHRSST",
        "cdm_data_type": "grid",
        "comment": "IN NO EVENT SHALL DMI OR ITS REPRESENTATIVES BE LIABLE FOR ANY DAMAGES WHATSOEVER INCLUDING, WITHOUT LIMITATION, SPECIAL, INDIRECT, INCIDENTAL OR CONSEQUENTIAL DAMAGES OR DAMAGES FOR LOSS OF BUSINESS PROFITS OR SAVINGS, BUSINESS INTERRUPTION, LOSS OF BUSINESS INFORMATION OR OTHER PECUNIARY LOSS ARISING OUT OF THE USE OF OR THE BUSINESS INTERRUPTION, LOSS OF BUSINESS INFORMATION OR OTHER PECUNIARY LOSS ARISING OUT OF THE USE OF OR THE INABILITY TO USE THIS DMI PRODUCT, EVEN IF DMI HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THIS LIMITATION SHALL APPLY TO CLAIMS OF PERSONAL INJURY TO THE EXTENT PERMITTED BY LAW. SOME COUNTRIES OR STATES DO NOT ALLOW THE EXCLUSION OR LIMITATION OF LIABILITY FOR CONSEQUENTIAL, SPECIAL, INDIRECT, INCIDENTAL DAMAGES AND, ACCORDINGLY, SOME PORTIONS OF THESE LIMITATIONS MAY NOT APPLY TO YOU. BY USING THIS PRODUCT, YOU HAVE ACCEPTED THAT THE ABOVE LIMITATIONS OR THE MAXIMUM LEGALLY APPLICABLE SUBSET OF THESE LIMITATIONS APPLY TO YOUR USE OF THIS PRODUCT. WARNING Some applications are unable to properly handle signed bytevalues. If values are encountered > 127, please subtract 256 from this reported value",
        "creator_email": "jlh@dmi.dk",
        "creator_name": "Jacob L. Hoeyer",
        "creator_url": "http://ocean.dmi.dk/satellite/",
        "date_created": "20230306T062824Z",
        "easternmost_longitude": 30,
        "file_quality_level": 3,
        "gds_version_id": "2.0",
        "geospatial_lat_resolution": 0.02,
        "geospatial_lat_units": "degrees north",
        "geospatial_lon_resolution": 0.02,
        "geospatial_lon_units": "degrees east",
        "history": "Version 1.0",
        "id": "DMI-L3S_GHRSST-SSTsubskin-night_SST_UHR_NRT-NSEABALTIC-",
        "institution": "Danish Meteorological Institute, DMI",
        "keywords": "Oceans > Ocean Temperature > Sea Surface Temperature",
        "keywords_vocabulary": "NASA Global Change Master Directory (GCMD) Science Keywords",
        "license": "these data are available free of charge under the GMES data policy",
        "metadata_link": "http://data.nodc.noaa.gov/NESDIS_DataCenters/metadata/index.html",
        "naming_authority": "org.ghrsst",
        "netcdf_version_id": "netcdf-4.3.3.1, 27 February 2015",
        "northernmost_latitude": 66,
        "platform": "NOAA-20, MetOpB, MSG2, SUOMI NPP, Sentinel 3",
        "processing_level": "L3S",
        "product_version": "Version 1.0",
        "project": "Copernicus Marine Environmental Monitoring Service (CMEMS) SST-TAC",
        "publisher_email": " ghrsst-po@nceo.ac.uk",
        "publisher_name": "GHRSST project office",
        "publisher_url": "http://www.ghrsst.org",
        "references": "Høyer, J. L., Le Borgne, P., & Eastwood, S. (2014). A bias correction method for Arctic satellite sea surface temperature observations. Remote Sensing of Environment, 146, 201-213.",
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        "southernmost_latitude": 48,
        "spatial_resolution": "  0.02 degrees",
        "standard_name_vocabulary": "NetCDF Climate and forecast (CF) Metadata Convention",
        "start_time": "20230306T000000Z",
        "stop_time": "20230307T000000Z",
        "summary": "DMI Sea Surface Temperature  supercollated level 3 field (L3S) using multisensor nighttime satellite SST observations",
        "time_coverage_end": "20230307T000000Z",
        "time_coverage_start": "20230306T000000Z",
        "title": "Baltic Sea - SST Analysis",
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         "attrs": {
          "axis": "Y",
          "comment": "geographical coordinates, WGS84 projection",
          "long_name": "latitude",
          "standard_name": "latitude",
          "units": "degrees_north",
          "valid_max": 66,
          "valid_min": 48
         },
         "dims": [
          "lat"
         ],
         "dtype": "float32",
         "name": "lat"
        },
        "lon": {
         "attrs": {
          "axis": "X",
          "comment": "geographical coordinates, WGS84 projection",
          "long_name": "longitude",
          "standard_name": "longitude",
          "units": "degrees_east",
          "valid_max": 30,
          "valid_min": -10
         },
         "dims": [
          "lon"
         ],
         "dtype": "float32",
         "name": "lon"
        },
        "time": {
         "attrs": {
          "axis": "T",
          "comment": "Nominal time of Level 3",
          "long_name": "reference time of sst field",
          "standard_name": "time"
         },
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          "time"
         ],
         "dtype": "datetime64[ns]",
         "name": "time"
        }
       },
       "crs": "WGS 84",
       "data_id": "DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE",
       "data_type": "dataset",
       "data_vars": {
        "mask": {
         "attrs": {
          "comment": "Mask can be used to further filter the data",
          "flag_masks": [
           1,
           2,
           4,
           8,
           16
          ],
          "flag_meanings": "water land optional_lake_surface sea_ice optional_river_surface",
          "long_name": "land sea ice lake bit mask",
          "source": "NAVOCEANO_landmask_v1.0, Myocean_HR_ice",
          "valid_max": 31,
          "valid_min": 0
         },
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          "lat",
          "lon"
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         "dtype": "float32",
         "name": "mask"
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          "long_name": "number of pixels from the L2Ps contributing to the SST value",
          "valid_max": 32767,
          "valid_min": 0
         },
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          "time",
          "lat",
          "lon"
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         "dtype": "float32",
         "name": "or_number_of_sst_pixels"
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           1,
           2,
           3,
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          "lon"
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         "dtype": "float32",
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          "valid_min": 0
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          "lat",
          "lon"
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          "lat",
          "lon"
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         "name": "sea_surface_temperature"
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           1,
           2,
           4,
           8,
           16,
           32,
           64,
           128,
           256,
           512
          ],
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          "lat",
          "lon"
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         "dtype": "float32",
         "name": "source_of_sst"
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          "comment": "SSES bias error",
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          "units": "kelvin",
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          "valid_min": -127
         },
         "dims": [
          "time",
          "lat",
          "lon"
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         "dtype": "float32",
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         },
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          "time",
          "lat",
          "lon"
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         "dtype": "float32",
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          "lat",
          "lon"
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         "dtype": "timedelta64[ns]",
         "name": "sst_dtime"
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          "comment": "Sum of original contributing pixel sst value squares",
          "long_name": "sum of contributing pixel sst value squares",
          "units": "Celsius2",
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          "valid_min": 0
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          "lat",
          "lon"
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         "dtype": "float32",
         "name": "sum_square_sst"
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        "sum_sst": {
         "attrs": {
          "comment": "Sum of original contributing pixel sst values",
          "long_name": "sum of contributing pixel sst values",
          "units": "Celsius",
          "valid_max": 90000,
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        "2023-03-06"
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      "text/plain": [
       "<xcube.core.store.descriptor.DatasetDescriptor at 0x7ffa30146eb0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
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    "store.describe_data('DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE')"
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   "id": "0910355d-19de-466d-ba73-5b3e5815c23c",
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    "The dataset can be opened by giving variables_names, time_range and bbox as params, but none of them are mandatory."
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   "id": "4bd24de6-2383-4489-8ff4-f30c9682b4db",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                  (time: 5, lat: 451, lon: 551)\n",
       "Coordinates:\n",
       "  * lat                      (lat) float32 53.0 53.02 53.04 ... 61.96 61.98 62.0\n",
       "  * lon                      (lon) float32 9.0 9.02 9.04 ... 19.96 19.98 20.0\n",
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       "    sea_surface_temperature  (time, lat, lon) float32 dask.array&lt;chunksize=(1, 451, 551), meta=np.ndarray&gt;\n",
       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.6\n",
       "    title:                      Baltic Sea - SST Analysis\n",
       "    summary:                    DMI Sea Surface Temperature  supercollated le...\n",
       "    references:                 Høyer, J. L., Le Borgne, P., &amp; Eastwood, S. (...\n",
       "    institution:                Danish Meteorological Institute, DMI\n",
       "    history:                    Version 1.0\n",
       "    ...                         ...\n",
       "    publisher_name:             GHRSST project office\n",
       "    publisher_url:              http://www.ghrsst.org\n",
       "    publisher_email:             ghrsst-po@nceo.ac.uk\n",
       "    processing_level:           L3S\n",
       "    cdm_data_type:              grid\n",
       "    comment:                    IN NO EVENT SHALL DMI OR ITS REPRESENTATIVES ...</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-a7f988ef-146f-4e89-b31d-e6e5f627f781' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a7f988ef-146f-4e89-b31d-e6e5f627f781' 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>: 5</li><li><span class='xr-has-index'>lat</span>: 451</li><li><span class='xr-has-index'>lon</span>: 551</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c2100a7d-473c-42c2-ab83-c60d5fb2300f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c2100a7d-473c-42c2-ab83-c60d5fb2300f' 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'>float32</div><div class='xr-var-preview xr-preview'>53.0 53.02 53.04 ... 61.98 62.0</div><input id='attrs-4dbb4907-42ca-4ca5-ba30-eb1661895e99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4dbb4907-42ca-4ca5-ba30-eb1661895e99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3579ab2-560c-43ec-94ce-acb6b944c024' class='xr-var-data-in' type='checkbox'><label for='data-f3579ab2-560c-43ec-94ce-acb6b944c024' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>valid_min :</span></dt><dd>48.0</dd><dt><span>valid_max :</span></dt><dd>66.0</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array([53.  , 53.02, 53.04, ..., 61.96, 61.98, 62.  ], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>9.0 9.02 9.04 ... 19.96 19.98 20.0</div><input id='attrs-9b70c710-373e-454d-882d-02c7083368c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b70c710-373e-454d-882d-02c7083368c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e678ad6-15a6-497b-a298-a8c370be6891' class='xr-var-data-in' type='checkbox'><label for='data-3e678ad6-15a6-497b-a298-a8c370be6891' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>valid_min :</span></dt><dd>-10.0</dd><dt><span>valid_max :</span></dt><dd>30.0</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array([ 9.  ,  9.02,  9.04, ..., 19.96, 19.98, 20.  ], dtype=float32)</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'>2022-01-01 ... 2022-01-05</div><input id='attrs-c9c4617d-a80a-45ff-9177-bd57c29ba1b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9c4617d-a80a-45ff-9177-bd57c29ba1b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da33e522-c728-4918-adc3-90b896f0dca9' class='xr-var-data-in' type='checkbox'><label for='data-da33e522-c728-4918-adc3-90b896f0dca9' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>reference time of sst field</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>comment :</span></dt><dd>Nominal time of Level 3</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-03T00:00:00.000000000&#x27;, &#x27;2022-01-04T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-05T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6e0d8d70-7ffc-4341-8185-79ac483f735a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e0d8d70-7ffc-4341-8185-79ac483f735a' class='xr-section-summary' >Data variables: <span>(1)</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>sea_surface_temperature</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=(1, 451, 551), meta=np.ndarray&gt;</div><input id='attrs-32536e3a-8211-4639-893c-6de2e809cfab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32536e3a-8211-4639-893c-6de2e809cfab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24a855b7-c159-49d6-875c-3af9597da328' class='xr-var-data-in' type='checkbox'><label for='data-24a855b7-c159-49d6-875c-3af9597da328' 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>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>long_name :</span></dt><dd>sea surface foundation temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>comment :</span></dt><dd>Foundation sea surface temperature of the ocean</dd><dt><span>source :</span></dt><dd>OSI-SAF and GHRSST L2P satellite SST products</dd><dt><span>valid_min :</span></dt><dd>-300</dd><dt><span>valid_max :</span></dt><dd>4500</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> 4.74 MiB </td>\n",
       "                        <td> 0.95 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5, 451, 551) </td>\n",
       "                        <td> (1, 451, 551) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 5 chunks in 4 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></ul></div></li><li class='xr-section-item'><input id='section-fe20bf12-c756-4779-b28d-60e186342812' class='xr-section-summary-in' type='checkbox'  ><label for='section-fe20bf12-c756-4779-b28d-60e186342812' 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><div></div><input id='index-8d9579d1-6ab4-4b0f-b7a4-6c89ef3abed1' class='xr-index-data-in' type='checkbox'/><label for='index-8d9579d1-6ab4-4b0f-b7a4-6c89ef3abed1' 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(Float64Index([              53.0,  53.02000045776367, 53.040000915527344,\n",
       "              53.060001373291016,  53.07999801635742, 53.099998474121094,\n",
       "              53.119998931884766,  53.13999938964844,  53.15999984741211,\n",
       "               53.18000030517578,\n",
       "              ...\n",
       "               61.81999969482422,  61.84000015258789,  61.86000061035156,\n",
       "              61.880001068115234, 61.900001525878906,  61.91999816894531,\n",
       "              61.939998626708984, 61.959999084472656,  61.97999954223633,\n",
       "                            62.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=451))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d4dd3c87-8bdd-4770-8fc1-3db0ddd331ba' class='xr-index-data-in' type='checkbox'/><label for='index-d4dd3c87-8bdd-4770-8fc1-3db0ddd331ba' 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(Float64Index([               9.0,  9.019999504089355,  9.039999961853027,\n",
       "               9.059999465942383,  9.079999923706055,   9.09999942779541,\n",
       "               9.119999885559082,  9.139999389648438,   9.15999984741211,\n",
       "               9.179999351501465,\n",
       "              ...\n",
       "               19.81999969482422,  19.84000015258789,  19.85999870300293,\n",
       "                19.8799991607666, 19.899999618530273, 19.920000076293945,\n",
       "              19.939998626708984, 19.959999084472656, 19.979999542236328,\n",
       "                            20.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=551))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b2824c5c-9d47-4a23-bfc0-bba85a0eb8ef' class='xr-index-data-in' type='checkbox'/><label for='index-b2824c5c-9d47-4a23-bfc0-bba85a0eb8ef' 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;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;, &#x27;2022-01-04&#x27;,\n",
       "               &#x27;2022-01-05&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e1d7b74-ccf5-499d-a6a5-14d9e274538b' class='xr-section-summary-in' type='checkbox'  ><label for='section-4e1d7b74-ccf5-499d-a6a5-14d9e274538b' class='xr-section-summary' >Attributes: <span>(47)</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-1.6</dd><dt><span>title :</span></dt><dd>Baltic Sea - SST Analysis</dd><dt><span>summary :</span></dt><dd>DMI Sea Surface Temperature  supercollated level 3 field (L3S) using multisensor nighttime satellite SST observations</dd><dt><span>references :</span></dt><dd>Høyer, J. L., Le Borgne, P., &amp; Eastwood, S. (2014). A bias correction method for Arctic satellite sea surface temperature observations. Remote Sensing of Environment, 146, 201-213.</dd><dt><span>institution :</span></dt><dd>Danish Meteorological Institute, DMI</dd><dt><span>history :</span></dt><dd>Version 1.0</dd><dt><span>license :</span></dt><dd>these data are available free of charge under the GMES data policy</dd><dt><span>id :</span></dt><dd>DMI-L3S_GHRSST-SSTsubskin-night_SST_UHR_NRT-NSEABALTIC-</dd><dt><span>naming_authority :</span></dt><dd>org.ghrsst</dd><dt><span>product_version :</span></dt><dd>Version 1.0</dd><dt><span>uuid :</span></dt><dd>a83cf24c-42e9-42ff-868b-c04c83e6f8d2</dd><dt><span>gds_version_id :</span></dt><dd>2.0</dd><dt><span>netcdf_version_id :</span></dt><dd>netcdf-4.3.3.1, 27 February 2015</dd><dt><span>date_created :</span></dt><dd>20230306T062824Z</dd><dt><span>file_quality_level :</span></dt><dd>3</dd><dt><span>spatial_resolution :</span></dt><dd>  0.02 degrees</dd><dt><span>start_time :</span></dt><dd>20230306T000000Z</dd><dt><span>time_coverage_start :</span></dt><dd>20230306T000000Z</dd><dt><span>stop_time :</span></dt><dd>20230307T000000Z</dd><dt><span>time_coverage_end :</span></dt><dd>20230307T000000Z</dd><dt><span>westernmost_longitude :</span></dt><dd>-10.0</dd><dt><span>easternmost_longitude :</span></dt><dd>30.0</dd><dt><span>southernmost_latitude :</span></dt><dd>48.0</dd><dt><span>northernmost_latitude :</span></dt><dd>66.0</dd><dt><span>source :</span></dt><dd>VIIRS_SST_NPP_NAR-OSISAF-L3C, SEVIRI_SST-OSISAF-L3C, EUR-L2P-AVHRR_METOP_B, NAVO-VIIRS_NPP, VIIRS_N20_ACSPO, MyOcean_HR_ICE</dd><dt><span>platform :</span></dt><dd>NOAA-20, MetOpB, MSG2, SUOMI NPP, Sentinel 3</dd><dt><span>sensor :</span></dt><dd>AVHRR, SEVIRI, VIIRS, SLSTR</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>metadata_link :</span></dt><dd>http://data.nodc.noaa.gov/NESDIS_DataCenters/metadata/index.html</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and forecast (CF) Metadata Convention</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees east</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.02</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.02</dd><dt><span>acknowledgment :</span></dt><dd>Please acknowledge the use of these data with the following statement: These data were provided by DMI and GHRSST</dd><dt><span>creator_name :</span></dt><dd>Jacob L. Hoeyer</dd><dt><span>creator_email :</span></dt><dd>jlh@dmi.dk</dd><dt><span>creator_url :</span></dt><dd>http://ocean.dmi.dk/satellite/</dd><dt><span>project :</span></dt><dd>Copernicus Marine Environmental Monitoring Service (CMEMS) SST-TAC</dd><dt><span>publisher_name :</span></dt><dd>GHRSST project office</dd><dt><span>publisher_url :</span></dt><dd>http://www.ghrsst.org</dd><dt><span>publisher_email :</span></dt><dd> ghrsst-po@nceo.ac.uk</dd><dt><span>processing_level :</span></dt><dd>L3S</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>comment :</span></dt><dd>IN NO EVENT SHALL DMI OR ITS REPRESENTATIVES BE LIABLE FOR ANY DAMAGES WHATSOEVER INCLUDING, WITHOUT LIMITATION, SPECIAL, INDIRECT, INCIDENTAL OR CONSEQUENTIAL DAMAGES OR DAMAGES FOR LOSS OF BUSINESS PROFITS OR SAVINGS, BUSINESS INTERRUPTION, LOSS OF BUSINESS INFORMATION OR OTHER PECUNIARY LOSS ARISING OUT OF THE USE OF OR THE BUSINESS INTERRUPTION, LOSS OF BUSINESS INFORMATION OR OTHER PECUNIARY LOSS ARISING OUT OF THE USE OF OR THE INABILITY TO USE THIS DMI PRODUCT, EVEN IF DMI HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THIS LIMITATION SHALL APPLY TO CLAIMS OF PERSONAL INJURY TO THE EXTENT PERMITTED BY LAW. SOME COUNTRIES OR STATES DO NOT ALLOW THE EXCLUSION OR LIMITATION OF LIABILITY FOR CONSEQUENTIAL, SPECIAL, INDIRECT, INCIDENTAL DAMAGES AND, ACCORDINGLY, SOME PORTIONS OF THESE LIMITATIONS MAY NOT APPLY TO YOU. BY USING THIS PRODUCT, YOU HAVE ACCEPTED THAT THE ABOVE LIMITATIONS OR THE MAXIMUM LEGALLY APPLICABLE SUBSET OF THESE LIMITATIONS APPLY TO YOUR USE OF THIS PRODUCT. WARNING Some applications are unable to properly handle signed bytevalues. If values are encountered &gt; 127, please subtract 256 from this reported value</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                  (time: 5, lat: 451, lon: 551)\n",
       "Coordinates:\n",
       "  * lat                      (lat) float32 53.0 53.02 53.04 ... 61.96 61.98 62.0\n",
       "  * lon                      (lon) float32 9.0 9.02 9.04 ... 19.96 19.98 20.0\n",
       "  * time                     (time) datetime64[ns] 2022-01-01 ... 2022-01-05\n",
       "Data variables:\n",
       "    sea_surface_temperature  (time, lat, lon) float32 dask.array<chunksize=(1, 451, 551), meta=np.ndarray>\n",
       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.6\n",
       "    title:                      Baltic Sea - SST Analysis\n",
       "    summary:                    DMI Sea Surface Temperature  supercollated le...\n",
       "    references:                 Høyer, J. L., Le Borgne, P., & Eastwood, S. (...\n",
       "    institution:                Danish Meteorological Institute, DMI\n",
       "    history:                    Version 1.0\n",
       "    ...                         ...\n",
       "    publisher_name:             GHRSST project office\n",
       "    publisher_url:              http://www.ghrsst.org\n",
       "    publisher_email:             ghrsst-po@nceo.ac.uk\n",
       "    processing_level:           L3S\n",
       "    cdm_data_type:              grid\n",
       "    comment:                    IN NO EVENT SHALL DMI OR ITS REPRESENTATIVES ..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = store.open_data('DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE', \n",
    "                          variable_names=['sea_surface_temperature'], \n",
    "                          bbox=[9, 53, 20, 62], \n",
    "                          time_range=('2022-01-01','2022-01-05'))\n",
    "dataset"
   ]
  },
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   "id": "5970a962-af5e-4f4f-a97d-c76767f1df35",
   "metadata": {
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   },
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sea_surface_temperature&#x27; (time: 5, lat: 451, lon: 551)&gt;\n",
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       "    standard_name:  sea_surface_temperature\n",
       "    long_name:      sea surface foundation temperature\n",
       "    units:          kelvin\n",
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       "    source:         OSI-SAF and GHRSST L2P satellite SST products\n",
       "    valid_min:      -300\n",
       "    valid_max:      4500</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sea_surface_temperature'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>lat</span>: 451</li><li><span class='xr-has-index'>lon</span>: 551</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9c00f02a-5ae9-4c9d-ba05-415fb262e730' class='xr-array-in' type='checkbox' checked><label for='section-9c00f02a-5ae9-4c9d-ba05-415fb262e730' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 451, 551), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"113\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,113.1700135577399 24.9485979497544,113.1700135577399\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"84.948598\" y=\"133.170014\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >551</text>\n",
       "  <text x=\"164.948598\" y=\"64.059306\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,64.059306)\">451</text>\n",
       "  <text x=\"7.474299\" y=\"125.695715\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,125.695715)\">5</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-6e083ec0-f6ef-4c42-ac9d-ef42e960180b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e083ec0-f6ef-4c42-ac9d-ef42e960180b' 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'>float32</div><div class='xr-var-preview xr-preview'>53.0 53.02 53.04 ... 61.98 62.0</div><input id='attrs-568eb01c-10d2-468d-a799-fa03b29d36f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-568eb01c-10d2-468d-a799-fa03b29d36f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70b4c9e1-722d-4c2f-bf49-e0494709eb01' class='xr-var-data-in' type='checkbox'><label for='data-70b4c9e1-722d-4c2f-bf49-e0494709eb01' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>valid_min :</span></dt><dd>48.0</dd><dt><span>valid_max :</span></dt><dd>66.0</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array([53.  , 53.02, 53.04, ..., 61.96, 61.98, 62.  ], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>9.0 9.02 9.04 ... 19.96 19.98 20.0</div><input id='attrs-0cbe4a33-d05d-4a97-9be3-1e31e7d78e0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cbe4a33-d05d-4a97-9be3-1e31e7d78e0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8ef8260-ed6b-44c2-a3a1-ecfb70ad0ce9' class='xr-var-data-in' type='checkbox'><label for='data-d8ef8260-ed6b-44c2-a3a1-ecfb70ad0ce9' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>valid_min :</span></dt><dd>-10.0</dd><dt><span>valid_max :</span></dt><dd>30.0</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array([ 9.  ,  9.02,  9.04, ..., 19.96, 19.98, 20.  ], dtype=float32)</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'>2022-01-01 ... 2022-01-05</div><input id='attrs-9017f3c4-1447-428b-ada8-90a2b72882be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9017f3c4-1447-428b-ada8-90a2b72882be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3cbe331-ac10-420f-9dfd-eed8e932b4f7' class='xr-var-data-in' type='checkbox'><label for='data-a3cbe331-ac10-420f-9dfd-eed8e932b4f7' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>reference time of sst field</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>comment :</span></dt><dd>Nominal time of Level 3</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-03T00:00:00.000000000&#x27;, &#x27;2022-01-04T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-05T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ec9bb486-dac7-4807-993b-b92fef183b20' class='xr-section-summary-in' type='checkbox'  ><label for='section-ec9bb486-dac7-4807-993b-b92fef183b20' 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><div></div><input id='index-bf2100fb-fae2-4563-9cc1-8111ebbbb059' class='xr-index-data-in' type='checkbox'/><label for='index-bf2100fb-fae2-4563-9cc1-8111ebbbb059' 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(Float64Index([              53.0,  53.02000045776367, 53.040000915527344,\n",
       "              53.060001373291016,  53.07999801635742, 53.099998474121094,\n",
       "              53.119998931884766,  53.13999938964844,  53.15999984741211,\n",
       "               53.18000030517578,\n",
       "              ...\n",
       "               61.81999969482422,  61.84000015258789,  61.86000061035156,\n",
       "              61.880001068115234, 61.900001525878906,  61.91999816894531,\n",
       "              61.939998626708984, 61.959999084472656,  61.97999954223633,\n",
       "                            62.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=451))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-82faff4d-4acc-4a51-94bb-5a9726e2d1de' class='xr-index-data-in' type='checkbox'/><label for='index-82faff4d-4acc-4a51-94bb-5a9726e2d1de' 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(Float64Index([               9.0,  9.019999504089355,  9.039999961853027,\n",
       "               9.059999465942383,  9.079999923706055,   9.09999942779541,\n",
       "               9.119999885559082,  9.139999389648438,   9.15999984741211,\n",
       "               9.179999351501465,\n",
       "              ...\n",
       "               19.81999969482422,  19.84000015258789,  19.85999870300293,\n",
       "                19.8799991607666, 19.899999618530273, 19.920000076293945,\n",
       "              19.939998626708984, 19.959999084472656, 19.979999542236328,\n",
       "                            20.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=551))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-038e2488-5bb5-4f3b-b859-2b2cc939a4a4' class='xr-index-data-in' type='checkbox'/><label for='index-038e2488-5bb5-4f3b-b859-2b2cc939a4a4' 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;2022-01-01&#x27;, &#x27;2022-01-02&#x27;, &#x27;2022-01-03&#x27;, &#x27;2022-01-04&#x27;,\n",
       "               &#x27;2022-01-05&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d8dd1546-e327-42a1-b678-e1a31bcfcb67' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d8dd1546-e327-42a1-b678-e1a31bcfcb67' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>long_name :</span></dt><dd>sea surface foundation temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>comment :</span></dt><dd>Foundation sea surface temperature of the ocean</dd><dt><span>source :</span></dt><dd>OSI-SAF and GHRSST L2P satellite SST products</dd><dt><span>valid_min :</span></dt><dd>-300</dd><dt><span>valid_max :</span></dt><dd>4500</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sea_surface_temperature' (time: 5, lat: 451, lon: 551)>\n",
       "dask.array<getitem, shape=(5, 451, 551), dtype=float32, chunksize=(1, 451, 551), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 53.0 53.02 53.04 53.06 ... 61.94 61.96 61.98 62.0\n",
       "  * lon      (lon) float32 9.0 9.02 9.04 9.06 9.08 ... 19.94 19.96 19.98 20.0\n",
       "  * time     (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n",
       "Attributes:\n",
       "    standard_name:  sea_surface_temperature\n",
       "    long_name:      sea surface foundation temperature\n",
       "    units:          kelvin\n",
       "    comment:        Foundation sea surface temperature of the ocean\n",
       "    source:         OSI-SAF and GHRSST L2P satellite SST products\n",
       "    valid_min:      -300\n",
       "    valid_max:      4500"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.sea_surface_temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6b098d67-cf83-4511-a8b3-00aef77d06cc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7ffa2cba4250>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.sea_surface_temperature.isel(time=-1).plot(figsize=(10, 10))"
   ]
  }
 ],
 "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": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}