{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ESA CCI Toolbox Data Access with Parameters\n",
    "\n",
    "This notebook shows how to open data cubes from the ESA Open Data Portal for a given time range and region:\n",
    "\n",
    "A **temporarily regular cube** with aggregated CCI data that fall into equal-size time periods;\n",
    "\n",
    "We start with necessary imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from xcube.core.store import new_data_store\n",
    "import IPython.display\n",
    "import shapely"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create the store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cci_store = new_data_store('esa-cci')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this demo, we are using the dataset with the id 'esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1'. Here is its metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "attrs": {
        "abstract": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.   This dataset comprises Level 3 Absorbing Aerosol Index (AAI) products, using the Multi-Sensor UVAI algorithm, Version 1.7.  L3 products are provided as daily and monthly gridded products as well as a monthly climatology. \n\nFor further details about these data products please see the linked documentation.",
        "catalog_url": "https://catalogue.ceda.ac.uk/uuid/2e656d34d016414c8d6bced18634772c",
        "cci_project": "AEROSOL",
        "comment": "Sun glint and solar eclipse events were filtered out.",
        "data_type": "AAI",
        "description": "Multi-Sensor AAI field for January 1979",
        "ecv": "AEROSOL",
        "history": "2017-07-24 09:13:47 - Product generated using create_msaai_monthly_products.pro",
        "institute": "Royal Netherlands Meteorological Institute",
        "platform_id": "multi-platform",
        "processing_level": "L3",
        "product_string": "MSAAI",
        "product_version": "1-7",
        "project": "Climate Change Initiative - European Space Agency",
        "publication_date": "2020-08-27T16:02:30",
        "references": "http://www.esa-aerosol-cci.org",
        "sensor_id": "multi-sensor",
        "source": "TOMS AAI version 8, GOME-1 AAI version 5.0, SCIAMACHY AAI version 5.1, OMI AAI version 1.1.1, GOME-2 AAI version 1.35",
        "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products  from the Multi-Sensor UV Absorbing Aerosol Index (MS UVAI) algorithm, Version 1.7",
        "uuid": "2e656d34d016414c8d6bced18634772c"
       },
       "bbox": [
        -180,
        -90,
        180,
        90
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       "coords": {
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         "attrs": {
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          "file_chunk_sizes": [
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          "fill_value": null,
          "long_name": "Latitudes of the centre of the grid cells",
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          "shape": [
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          "units": "degrees"
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          "units": "degrees"
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         "dims": [
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         "dtype": "float32",
         "name": "longitude"
        },
        "time": {
         "attrs": {
          "calendar": "proleptic_gregorian",
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          "size": 422,
          "standard_name": "time",
          "units": "seconds since 1970-01-01T00:00:00Z"
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         "dims": [
          "time"
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         "dtype": "int64",
         "name": "time"
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          "standard_name": "time_bnds",
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         "dims": [
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         "dtype": "int64",
         "name": "time_bnds"
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       "data_id": "esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1",
       "data_type": "dataset",
       "data_vars": {
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         "attrs": {
          "chunk_sizes": [
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          "data_type": "float32",
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          "file_chunk_sizes": [
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           360
          ],
          "file_dimensions": [
           "latitude",
           "longitude"
          ],
          "fill_value": null,
          "long_name": "Absorbing aerosol index averaged for each grid cell",
          "orig_data_type": "float32",
          "shape": [
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           360
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          "size": 64800,
          "units": "1"
         },
         "dims": [
          "time",
          "lat",
          "lon"
         ],
         "dtype": "float32",
         "name": "absorbing_aerosol_index"
        },
        "number_of_observations": {
         "attrs": {
          "chunk_sizes": [
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           360
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          "data_type": "int64",
          "dimensions": [
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           "longitude"
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         "dims": [
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          "lon"
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         "dtype": "int64",
         "name": "number_of_observations"
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        "number_of_positive_observations": {
         "attrs": {
          "chunk_sizes": [
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           360
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          "data_type": "int64",
          "dimensions": [
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           "longitude"
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          "file_dimensions": [
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           "longitude"
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          "fill_value": 9223372036854776000,
          "long_name": "Number of positive observations for each grid cell",
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           360
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          "size": 64800,
          "units": "1"
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         "dims": [
          "time",
          "lat",
          "lon"
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         "dtype": "int64",
         "name": "number_of_positive_observations"
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          "chunk_sizes": [
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           360
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          "data_type": "float32",
          "dimensions": [
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           "longitude"
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          "file_chunk_sizes": [
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          "file_dimensions": [
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         "dtype": "float32",
         "name": "positive_absorbing_aerosol_index"
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         "name": "solar_zenith_angle"
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        "lat": 180,
        "lon": 360,
        "time": 422
       },
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        "additionalProperties": false,
        "properties": {
         "bbox": {
          "items": [
           {
            "maximum": 180,
            "minimum": -180,
            "type": "number"
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           {
            "maximum": 90,
            "minimum": -90,
            "type": "number"
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           {
            "maximum": 180,
            "minimum": -180,
            "type": "number"
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           {
            "maximum": 90,
            "minimum": -90,
            "type": "number"
           }
          ],
          "type": "array"
         },
         "normalize_data": {
          "default": true,
          "type": "boolean"
         },
         "time_range": {
          "items": [
           {
            "format": "date",
            "maxDate": "2015-12-31",
            "minDate": "1978-11-01",
            "type": "string"
           },
           {
            "format": "date",
            "maxDate": "2015-12-31",
            "minDate": "1978-11-01",
            "type": "string"
           }
          ],
          "type": "array"
         },
         "variable_names": {
          "items": {
           "enum": [
            "absorbing_aerosol_index",
            "number_of_observations",
            "solar_zenith_angle",
            "positive_absorbing_aerosol_index",
            "number_of_positive_observations"
           ],
           "type": "string"
          },
          "type": "array"
         }
        },
        "type": "object"
       },
       "spatial_res": 1,
       "time_period": "1M",
       "time_range": [
        "1978-11-01",
        "2015-12-31"
       ]
      },
      "text/plain": [
       "<xcube.core.store.descriptor.DatasetDescriptor at 0x764f74bfdd30>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cci_store.describe_data('esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are focussing on Africa, so we define a bounding box accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x1 = -23.40  # degree\n",
    "y1 = -40.40  # degree\n",
    "x2 = 57.40  # degree\n",
    "y2 = 40.40  # degree\n",
    "\n",
    "bbox = (x1, y1, x2, y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the bounding box. If you don't see anything, please refer to [Ex0-DCFS-Setup](./Ex0-DCFS-Setup.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/geo+json": {
       "coordinates": [
        [
         [
          57.4,
          -40.4
         ],
         [
          57.4,
          40.4
         ],
         [
          -23.4,
          40.4
         ],
         [
          -23.4,
          -40.4
         ],
         [
          57.4,
          -40.4
         ]
        ]
       ],
       "type": "Polygon"
      },
      "text/plain": [
       "<IPython.display.GeoJSON object>"
      ]
     },
     "metadata": {
      "application/geo+json": {
       "expanded": false,
       "root": "root"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "IPython.display.GeoJSON(shapely.geometry.box(*bbox).__geo_interface__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our time range covers data from 1997: 1997-01-01 to 1997-12-01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Now to open the dataset. You may check its metadata first to make sure that your parameters are valid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "additionalProperties": false,
       "properties": {
        "bbox": {
         "items": [
          {
           "maximum": 180,
           "minimum": -180,
           "type": "number"
          },
          {
           "maximum": 90,
           "minimum": -90,
           "type": "number"
          },
          {
           "maximum": 180,
           "minimum": -180,
           "type": "number"
          },
          {
           "maximum": 90,
           "minimum": -90,
           "type": "number"
          }
         ],
         "type": "array"
        },
        "normalize_data": {
         "default": true,
         "type": "boolean"
        },
        "time_range": {
         "items": [
          {
           "format": "date",
           "maxDate": "2015-12-31",
           "minDate": "1978-11-01",
           "type": "string"
          },
          {
           "format": "date",
           "maxDate": "2015-12-31",
           "minDate": "1978-11-01",
           "type": "string"
          }
         ],
         "type": "array"
        },
        "variable_names": {
         "items": {
          "enum": [
           "absorbing_aerosol_index",
           "number_of_observations",
           "solar_zenith_angle",
           "positive_absorbing_aerosol_index",
           "number_of_positive_observations"
          ],
          "type": "string"
         },
         "type": "array"
        }
       },
       "type": "object"
      },
      "text/plain": [
       "<xcube.util.jsonschema.JsonObjectSchema at 0x764eaa8d3770>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cci_store.get_open_data_params_schema(data_id='esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1', opener_id='dataset:zarr:cciodp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select any valid combination from the paramater in *properties* above. The only mandatory parameter is the data_id. If you leave any parameter out, all available values will be considered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
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       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\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",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 308kB\n",
       "Dimensions:                  (time: 12, lat: 80, lon: 80, bnds: 2)\n",
       "Coordinates:\n",
       "  * lat                      (lat) float32 320B -39.5 -38.5 -37.5 ... 38.5 39.5\n",
       "  * lon                      (lon) float32 320B -22.5 -21.5 -20.5 ... 55.5 56.5\n",
       "  * time                     (time) datetime64[ns] 96B 2005-01-16T12:00:00 .....\n",
       "    time_bnds                (time, bnds) datetime64[ns] 192B dask.array&lt;chunksize=(12, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    absorbing_aerosol_index  (time, lat, lon) float32 307kB dask.array&lt;chunksize=(1, 80, 80), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:             CF-1.7\n",
       "    title:                   esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-pla...\n",
       "    date_created:            2025-12-05T16:05:50.977171\n",
       "    processing_level:        L3\n",
       "    time_coverage_start:     2005-01-01T00:00:00\n",
       "    time_coverage_end:       2006-01-01T00:00:00\n",
       "    time_coverage_duration:  P365DT0H0M0S\n",
       "    history:                 [{&#x27;program&#x27;: &#x27;xcube_cci.chunkstore.CciChunkStore...</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-0d470229-15b6-4e88-8b2f-0910bf477277' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0d470229-15b6-4e88-8b2f-0910bf477277' 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>: 12</li><li><span class='xr-has-index'>lat</span>: 80</li><li><span class='xr-has-index'>lon</span>: 80</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8797fb4d-ca44-4cd2-9c2a-b3881739f53d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8797fb4d-ca44-4cd2-9c2a-b3881739f53d' class='xr-section-summary' >Coordinates: <span>(4)</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'>-39.5 -38.5 -37.5 ... 38.5 39.5</div><input id='attrs-a1321d6e-e1fb-468a-853d-bdbfbc66ac18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1321d6e-e1fb-468a-853d-bdbfbc66ac18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-994dc904-f2e2-49c6-968f-e1cb3d7bddd7' class='xr-var-data-in' type='checkbox'><label for='data-994dc904-f2e2-49c6-968f-e1cb3d7bddd7' 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>long_name :</span></dt><dd>Latitudes of the centre of the grid cells</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>orig_data_type :</span></dt><dd>float32</dd><dt><span>fill_value :</span></dt><dd>nan</dd><dt><span>size :</span></dt><dd>80</dd><dt><span>shape :</span></dt><dd>[80]</dd><dt><span>chunk_sizes :</span></dt><dd>[80]</dd><dt><span>file_chunk_sizes :</span></dt><dd>[80]</dd><dt><span>data_type :</span></dt><dd>float32</dd><dt><span>dimensions :</span></dt><dd>[&#x27;latitude&#x27;]</dd><dt><span>file_dimensions :</span></dt><dd>[&#x27;latitude&#x27;]</dd></dl></div><div class='xr-var-data'><pre>array([-39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5],\n",
       "      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'>-22.5 -21.5 -20.5 ... 55.5 56.5</div><input id='attrs-ad7813ac-6d6e-45f5-9ec9-53532b7c713d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad7813ac-6d6e-45f5-9ec9-53532b7c713d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ce1d077-7f21-410a-a7b8-769a57eb071a' class='xr-var-data-in' type='checkbox'><label for='data-7ce1d077-7f21-410a-a7b8-769a57eb071a' 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>long_name :</span></dt><dd>Longitudes of the centre of the grid cells</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>orig_data_type :</span></dt><dd>float32</dd><dt><span>fill_value :</span></dt><dd>nan</dd><dt><span>size :</span></dt><dd>80</dd><dt><span>shape :</span></dt><dd>[80]</dd><dt><span>chunk_sizes :</span></dt><dd>[80]</dd><dt><span>file_chunk_sizes :</span></dt><dd>[80]</dd><dt><span>data_type :</span></dt><dd>float32</dd><dt><span>dimensions :</span></dt><dd>[&#x27;longitude&#x27;]</dd><dt><span>file_dimensions :</span></dt><dd>[&#x27;longitude&#x27;]</dd></dl></div><div class='xr-var-data'><pre>array([-22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5,\n",
       "       -12.5, -11.5, -10.5,  -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,\n",
       "        -2.5,  -1.5,  -0.5,   0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,\n",
       "         7.5,   8.5,   9.5,  10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,\n",
       "        17.5,  18.5,  19.5,  20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,\n",
       "        27.5,  28.5,  29.5,  30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,\n",
       "        37.5,  38.5,  39.5,  40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,\n",
       "        47.5,  48.5,  49.5,  50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5],\n",
       "      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'>2005-01-16T12:00:00 ... 2005-12-...</div><input id='attrs-ab809dd2-4b3e-4e71-8848-ee735dde43db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab809dd2-4b3e-4e71-8848-ee735dde43db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00567f20-47b6-4fdb-87b6-89e20ab18db9' class='xr-var-data-in' type='checkbox'><label for='data-00567f20-47b6-4fdb-87b6-89e20ab18db9' 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>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2005-01-16T12:00:00.000000000&#x27;, &#x27;2005-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-03-16T12:00:00.000000000&#x27;, &#x27;2005-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-05-16T12:00:00.000000000&#x27;, &#x27;2005-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-16T12:00:00.000000000&#x27;, &#x27;2005-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-16T00:00:00.000000000&#x27;, &#x27;2005-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-11-16T00:00:00.000000000&#x27;, &#x27;2005-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 2), meta=np.ndarray&gt;</div><input id='attrs-72506665-de8a-471e-8aff-7e44610066db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72506665-de8a-471e-8aff-7e44610066db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9d71174-f95f-44a0-bda4-6f1807d02bb3' class='xr-var-data-in' type='checkbox'><label for='data-b9d71174-f95f-44a0-bda4-6f1807d02bb3' 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_bnds</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> 192 B </td>\n",
       "                        <td> 192 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (12, 2) </td>\n",
       "                        <td> (12, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-4eed6e2e-f161-4564-a2c1-fe92eb21401a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4eed6e2e-f161-4564-a2c1-fe92eb21401a' 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>absorbing_aerosol_index</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, 80, 80), meta=np.ndarray&gt;</div><input id='attrs-0bded59c-5b61-42c4-8883-ee44c9dbad1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bded59c-5b61-42c4-8883-ee44c9dbad1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d364031-1a11-41fe-a92c-89e48100e17c' class='xr-var-data-in' type='checkbox'><label for='data-8d364031-1a11-41fe-a92c-89e48100e17c' 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>long_name :</span></dt><dd>Absorbing aerosol index averaged for each grid cell</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>orig_data_type :</span></dt><dd>float32</dd><dt><span>fill_value :</span></dt><dd>nan</dd><dt><span>size :</span></dt><dd>76800</dd><dt><span>shape :</span></dt><dd>[12, 80, 80]</dd><dt><span>chunk_sizes :</span></dt><dd>[1, 80, 80]</dd><dt><span>file_chunk_sizes :</span></dt><dd>[80, 80]</dd><dt><span>data_type :</span></dt><dd>float32</dd><dt><span>dimensions :</span></dt><dd>[&#x27;time&#x27;, &#x27;latitude&#x27;, &#x27;longitude&#x27;]</dd><dt><span>file_dimensions :</span></dt><dd>[&#x27;latitude&#x27;, &#x27;longitude&#x27;]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <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> 300.00 kiB </td>\n",
       "                        <td> 25.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (12, 80, 80) </td>\n",
       "                        <td> (1, 80, 80) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 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></ul></div></li><li class='xr-section-item'><input id='section-3e36858e-5d67-4a1a-afdd-0431451ee18b' class='xr-section-summary-in' type='checkbox'  ><label for='section-3e36858e-5d67-4a1a-afdd-0431451ee18b' 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-4bb0f73b-8ed2-41df-8189-e3efa383fedd' class='xr-index-data-in' type='checkbox'/><label for='index-4bb0f73b-8ed2-41df-8189-e3efa383fedd' 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([-39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</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-28a26d07-98c5-42bd-84d5-12a66b4c1af0' class='xr-index-data-in' type='checkbox'/><label for='index-28a26d07-98c5-42bd-84d5-12a66b4c1af0' 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([-22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5,\n",
       "       -12.5, -11.5, -10.5,  -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,\n",
       "        -2.5,  -1.5,  -0.5,   0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,\n",
       "         7.5,   8.5,   9.5,  10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,\n",
       "        17.5,  18.5,  19.5,  20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,\n",
       "        27.5,  28.5,  29.5,  30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,\n",
       "        37.5,  38.5,  39.5,  40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,\n",
       "        47.5,  48.5,  49.5,  50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</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-b9f463b9-d0d1-4520-b19c-81f73dc02be9' class='xr-index-data-in' type='checkbox'/><label for='index-b9f463b9-d0d1-4520-b19c-81f73dc02be9' 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;2005-01-16 12:00:00&#x27;, &#x27;2005-02-15 00:00:00&#x27;,\n",
       "               &#x27;2005-03-16 12:00:00&#x27;, &#x27;2005-04-16 00:00:00&#x27;,\n",
       "               &#x27;2005-05-16 12:00:00&#x27;, &#x27;2005-06-16 00:00:00&#x27;,\n",
       "               &#x27;2005-07-16 12:00:00&#x27;, &#x27;2005-08-16 12:00:00&#x27;,\n",
       "               &#x27;2005-09-16 00:00:00&#x27;, &#x27;2005-10-16 12:00:00&#x27;,\n",
       "               &#x27;2005-11-16 00:00:00&#x27;, &#x27;2005-12-16 12:00:00&#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-0d130d45-a077-492c-a6f7-2da4b0956ec1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0d130d45-a077-492c-a6f7-2da4b0956ec1' class='xr-section-summary' >Attributes: <span>(8)</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-1.7</dd><dt><span>title :</span></dt><dd>esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1</dd><dt><span>date_created :</span></dt><dd>2025-12-05T16:05:50.977171</dd><dt><span>processing_level :</span></dt><dd>L3</dd><dt><span>time_coverage_start :</span></dt><dd>2005-01-01T00:00:00</dd><dt><span>time_coverage_end :</span></dt><dd>2006-01-01T00:00:00</dd><dt><span>time_coverage_duration :</span></dt><dd>P365DT0H0M0S</dd><dt><span>history :</span></dt><dd>[{&#x27;program&#x27;: &#x27;xcube_cci.chunkstore.CciChunkStore&#x27;, &#x27;cube_params&#x27;: {&#x27;variable_names&#x27;: [&#x27;absorbing_aerosol_index&#x27;], &#x27;time_range&#x27;: [&#x27;2005-01-01T00:00:00&#x27;, &#x27;2005-12-31T00:00:00&#x27;], &#x27;bbox&#x27;: [-23.4, -40.4, 57.4, 40.4]}}]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 308kB\n",
       "Dimensions:                  (time: 12, lat: 80, lon: 80, bnds: 2)\n",
       "Coordinates:\n",
       "  * lat                      (lat) float32 320B -39.5 -38.5 -37.5 ... 38.5 39.5\n",
       "  * lon                      (lon) float32 320B -22.5 -21.5 -20.5 ... 55.5 56.5\n",
       "  * time                     (time) datetime64[ns] 96B 2005-01-16T12:00:00 .....\n",
       "    time_bnds                (time, bnds) datetime64[ns] 192B dask.array<chunksize=(12, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    absorbing_aerosol_index  (time, lat, lon) float32 307kB dask.array<chunksize=(1, 80, 80), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:             CF-1.7\n",
       "    title:                   esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-pla...\n",
       "    date_created:            2025-12-05T16:05:50.977171\n",
       "    processing_level:        L3\n",
       "    time_coverage_start:     2005-01-01T00:00:00\n",
       "    time_coverage_end:       2006-01-01T00:00:00\n",
       "    time_coverage_duration:  P365DT0H0M0S\n",
       "    history:                 [{'program': 'xcube_cci.chunkstore.CciChunkStore..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube = cci_store.open_data('esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1',\n",
    "                           variable_names=['absorbing_aerosol_index'],\n",
    "                           bbox=bbox,\n",
    "                           time_range=['2005-01-01', '2005-12-31'])\n",
    "cube"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may visualize the cube's time coordinates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x764ea886b250>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cube.time.diff(dim='time').plot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally, display the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x764ea88bfa10>"
      ]
     },
     "execution_count": 9,
     "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": [
    "cube.absorbing_aerosol_index.sel(time='2005-04-15 12:00:00', method='nearest').plot.imshow(cmap='Greys_r', figsize=(8, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}