{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "67b3c9b3-525c-48a6-8303-5c273414dd03",
   "metadata": {},
   "source": [
    "# Access to WCS data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3fb40e6-375e-4f69-af40-981c43e75caf",
   "metadata": {},
   "source": [
    "The CEDA data server at <https://data.cci.ceda.ac.uk/> provides access via the OGC Web Coverage Service (WCS) to CCI datasets. This notebook demonstrates how to retrieve CCI data via WCS from the CEDA server, using [OWSLib](https://www.osgeo.org/projects/owslib/) as the client."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2235e0d6-1621-4706-bffd-958a533935c9",
   "metadata": {},
   "source": [
    "If `owslib` is not already installed in the Python environment, uncomment the following cell to install it with mamba."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d04d1309-a597-45e2-837e-b65c0ef55dbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! mamba install --yes owslib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fc8b05f-829c-4862-9184-b02f27eec8fd",
   "metadata": {},
   "source": [
    "Import some required libraries, including OWSLib as the WCS client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5528c8e9-a35c-41d1-a4f6-da6d5b3aa1af",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen abc>:106: FutureWarning: xarray subclass VectorDataCube should explicitly define __slots__\n"
     ]
    }
   ],
   "source": [
    "from owslib.wcs import WebCoverageService\n",
    "import xarray as xr\n",
    "from esa_climate_toolbox.core import list_ecvs, list_ecv_datasets\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "199e2d64-ac53-48dc-9c5c-f9304cf70177",
   "metadata": {},
   "source": [
    "Define a simple convenience class `CciWcs` to manage the web service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d8fbee0a-2a34-4af3-b516-c59456dbad35",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CciWcs:\n",
    "\n",
    "    def __init__(self, dataset_id: str):\n",
    "        self.service = WebCoverageService(\n",
    "            f\"https://data.cci.ceda.ac.uk/thredds/wcs/{dataset_id}?service=WCS&version=1.0.0\",\n",
    "            version=\"1.0.0\"\n",
    "        )\n",
    "        \n",
    "    def coverage(self, data_id: str, bbox: tuple[float]) -> xr.Dataset:\n",
    "        coverage = self.service.getCoverage(\n",
    "          identifier=data_id,\n",
    "          bbox=bbox,\n",
    "          format=\"NetCDF3\",\n",
    "          timeout=600\n",
    "        )\n",
    "        with open(\"coverage.nc\", \"wb\") as fh:\n",
    "            fh.write(coverage.read())\n",
    "        return xr.open_dataset(\"coverage.nc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d4225c-29bc-4b34-84d3-965507ada12b",
   "metadata": {},
   "source": [
    "List the available ECVs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "776c09d7-fd9e-4d6c-8b28-f9f2d3e02831",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SEASTATE',\n",
       " 'AEROSOL',\n",
       " 'SEAICE',\n",
       " 'ICESHEETS',\n",
       " 'BIOMASS',\n",
       " 'RD',\n",
       " 'PERMAFROST',\n",
       " 'OC',\n",
       " 'SOILMOISTURE',\n",
       " 'SST',\n",
       " 'VEGETATION',\n",
       " 'FIRE',\n",
       " 'GHG',\n",
       " 'WATERVAPOUR',\n",
       " 'SNOW',\n",
       " 'LC',\n",
       " 'SEASURFACESALINITY',\n",
       " 'LAKES',\n",
       " 'LST',\n",
       " 'OZONE',\n",
       " 'CLOUD',\n",
       " 'SEALEVEL']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_ecvs()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6afdcd18-0e4c-4fd3-94ed-bc143c5d0319",
   "metadata": {},
   "source": [
    "List the available datasets for the Land Surface Temperature (LST) ECV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "30abcf0b-fa0b-4acd-8b23-e46247382852",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "esacci.LST.3-hours.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.r1\n",
      "esacci.LST.day.L3C.LST.AATSR.Envisat.ATSR_3.3-00.DAY\n",
      "esacci.LST.day.L3C.LST.AATSR.Envisat.ATSR_3.3-00.NIGHT\n",
      "esacci.LST.day.L3C.LST.ATSR-2.ERS-2.ATSR_2.3-00.DAY\n",
      "esacci.LST.day.L3C.LST.ATSR-2.ERS-2.ATSR_2.3-00.NIGHT\n",
      "esacci.LST.day.L3C.LST.MODIS.Aqua.MODISA.3-00.DAY\n",
      "esacci.LST.day.L3C.LST.MODIS.Aqua.MODISA.3-00.NIGHT\n",
      "esacci.LST.day.L3C.LST.MODIS.Terra.MODIST.3-00.DAY\n",
      "esacci.LST.day.L3C.LST.MODIS.Terra.MODIST.3-00.NIGHT\n",
      "esacci.LST.day.L3C.LST.SLSTR.Sentinel-3A.SLSTRA.3-00.DAY\n",
      "esacci.LST.day.L3C.LST.SLSTR.Sentinel-3A.SLSTRA.3-00.NIGHT\n",
      "esacci.LST.day.L3C.LST.SLSTR.Sentinel-3B.SLSTRB.3-00.DAY\n",
      "esacci.LST.day.L3C.LST.SLSTR.Sentinel-3B.SLSTRB.3-00.NIGHT\n",
      "esacci.LST.day.L3C.LST.multi-sensor.multi-platform.SSMI_SSMIS.v2-33.ASC\n",
      "esacci.LST.day.L3C.LST.multi-sensor.multi-platform.SSMI_SSMIS.v2-33.DES\n",
      "esacci.LST.day.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.DAY\n",
      "esacci.LST.day.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.NIGHT\n",
      "esacci.LST.mon.L3C.LST.AATSR.Envisat.ATSR_3.3-00.DAY\n",
      "esacci.LST.mon.L3C.LST.AATSR.Envisat.ATSR_3.3-00.NIGHT\n",
      "esacci.LST.mon.L3C.LST.ATSR-2.ERS-2.ATSR_2.3-00.DAY\n",
      "esacci.LST.mon.L3C.LST.ATSR-2.ERS-2.ATSR_2.3-00.NIGHT\n",
      "esacci.LST.mon.L3C.LST.MODIS.Aqua.MODISA.3-00.DAY\n",
      "esacci.LST.mon.L3C.LST.MODIS.Aqua.MODISA.3-00.NIGHT\n",
      "esacci.LST.mon.L3C.LST.MODIS.Terra.MODIST.3-00.DAY\n",
      "esacci.LST.mon.L3C.LST.MODIS.Terra.MODIST.3-00.NIGHT\n",
      "esacci.LST.mon.L3C.LST.SLSTR.Sentinel-3A.SLSTRA.3-00.DAY\n",
      "esacci.LST.mon.L3C.LST.SLSTR.Sentinel-3A.SLSTRA.3-00.NIGHT\n",
      "esacci.LST.mon.L3C.LST.SLSTR.Sentinel-3B.SLSTRB.3-00.DAY\n",
      "esacci.LST.mon.L3C.LST.SLSTR.Sentinel-3B.SLSTRB.3-00.NIGHT\n",
      "esacci.LST.mon.L3C.LST.multi-sensor.multi-platform.SSMI_SSMIS.v2-33.ASC\n",
      "esacci.LST.mon.L3C.LST.multi-sensor.multi-platform.SSMI_SSMIS.v2-33.DES\n",
      "esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.DAY\n",
      "esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.NIGHT\n",
      "esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.00:00UTC\n",
      "esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.03:00UTC\n",
      "esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.06:00UTC\n",
      "esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.09:00UTC\n",
      "esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.12:00UTC\n",
      "esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.15:00UTC\n",
      "esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.18:00UTC\n",
      "esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.21:00UTC\n",
      "esacci.LST.yr.L3C.LST.multi-sensor.multi-platform.SSMI_SSMIS.v2-33.ASC\n",
      "esacci.LST.yr.L3C.LST.multi-sensor.multi-platform.SSMI_SSMIS.v2-33.DES\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\".join(map(lambda x: x[0], list_ecv_datasets(\"LST\"))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b93cd2-b850-4f5d-b084-739df91dc49f",
   "metadata": {},
   "source": [
    "Select a dataset from the list and store its name in a variable for use in the following steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1fee267e-fcac-4250-89ee-4b9702525636",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_id = \"esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.DAY\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26cd437b-2757-46fb-8b90-1ef155254c80",
   "metadata": {},
   "source": [
    "Create a local representation of the WCS service for the selected dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "308d8762-31b1-463a-be95-d4ff01efbaee",
   "metadata": {},
   "outputs": [],
   "source": [
    "cciwcs = CciWcs(ds_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faca383a-7160-4051-91b2-c0657efc2a31",
   "metadata": {},
   "source": [
    "List the data variables available from this service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f3862efe-33f3-4f52-8250-e709563e047b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['solaz',\n",
       " 'lst_unc_ran',\n",
       " 'lst_unc_loc_cor',\n",
       " 'dtime',\n",
       " 'lst_unc_loc_sfc',\n",
       " 'lst',\n",
       " 'lcc',\n",
       " 'n',\n",
       " 'satze',\n",
       " 'solze',\n",
       " 'lst_unc_loc_atm',\n",
       " 'lst_uncertainty',\n",
       " 'sataz']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(cciwcs.service.contents.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17bc8217-4908-4609-8eff-c434f43c63d9",
   "metadata": {},
   "source": [
    "Select the `lst` variable from the list and store its name in a variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "48d1d285-f467-49c2-b3cd-4987cf639813",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_id = \"lst\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ff96c65-5a8b-42ed-9b66-e4b5d763febd",
   "metadata": {},
   "source": [
    "Check the bounding box for the selected dataset and variable; we can see that it is of global extent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4725a332-8eef-4049-8fe8-73803bb1fc6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-179.9949951171875, -89.99500274658203, 179.9949951171875, 89.99498748779297)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cciwcs.service.contents[data_id].boundingBoxWGS84"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c23ee1a3-254e-4e99-8842-cfc64492327a",
   "metadata": {},
   "source": [
    "For a quick demonstration with a small amount of data, request a coverage for a small sub-region and return it as a xarray dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e74065db-84e3-4964-96f0-a5077137203a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = cciwcs.coverage(\n",
    "    data_id=\"lst\",\n",
    "    bbox=(9.7, 53.3, 10.3, 53.5)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "051f5ab3-7a9b-4f23-98cb-0243eb2d9278",
   "metadata": {},
   "source": [
    "Show some of the metadata for the returned coverage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "560c4944-1d81-447c-a41b-80e51b9244b0",
   "metadata": {},
   "outputs": [
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       "}\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: 2MB\n",
       "Dimensions:  (time: 294, lat: 21, lon: 61)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2kB 1995-08-01 1995-09-01 ... 2020-12-01\n",
       "  * lat      (lat) float32 84B 53.31 53.31 53.32 53.34 ... 53.48 53.49 53.51\n",
       "  * lon      (lon) float32 244B 9.705 9.715 9.725 9.735 ... 10.28 10.29 10.31\n",
       "Data variables:\n",
       "    lst      (time, lat, lon) float32 2MB ...\n",
       "Attributes: (12/43)\n",
       "    source:                     ESA LST CCI IRCDR L3S V2.00\n",
       "    title:                      ESA LST CCI land surface temperature time ser...\n",
       "    institution:                University of Leicester\n",
       "    history:                    2023-05-24 17:10:44: The CCI Open Data Portal...\n",
       "    references:                 https://climate.esa.int/en/projects/land-surf...\n",
       "    Conventions:                CF-1.8\n",
       "    ...                         ...\n",
       "    key_variables:              land_surface_temperature\n",
       "    format_version:             CCI Data Standards v2.2\n",
       "    spatial_resolution:         0.01 degree\n",
       "    doi:                        10.5285/785ef9d3965442669bff899540747e28\n",
       "    tracking_id:                66f44ae9-1873-48be-a2df-c349022a9a97\n",
       "    History:                    Translated to CF-1.0 Conventions by Netcdf-Ja...</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-85cc9f35-1429-4b06-b53d-b649b0aed4e2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-85cc9f35-1429-4b06-b53d-b649b0aed4e2' 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>: 294</li><li><span class='xr-has-index'>lat</span>: 21</li><li><span class='xr-has-index'>lon</span>: 61</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-740e68f8-2e89-4039-a256-a409431b5f83' class='xr-section-summary-in' type='checkbox'  checked><label for='section-740e68f8-2e89-4039-a256-a409431b5f83' 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'>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'>1995-08-01 ... 2020-12-01</div><input id='attrs-f13a8f63-cda4-401f-8e93-4ae3587c118b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f13a8f63-cda4-401f-8e93-4ae3587c118b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9569868e-edfd-4c2d-adc8-09cc09dc12fb' class='xr-var-data-in' type='checkbox'><label for='data-9569868e-edfd-4c2d-adc8-09cc09dc12fb' 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>reference time of file</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1995-08-01T00:00:00.000000000&#x27;, &#x27;1995-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1995-10-01T00:00:00.000000000&#x27;, ..., &#x27;2020-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-11-01T00:00:00.000000000&#x27;, &#x27;2020-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><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'>53.31 53.31 53.32 ... 53.49 53.51</div><input id='attrs-a5cb6050-0acf-44e3-abd0-4a881615f9d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5cb6050-0acf-44e3-abd0-4a881615f9d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-771497eb-9889-498b-8b14-12307ffce3ad' class='xr-var-data-in' type='checkbox'><label for='data-771497eb-9889-498b-8b14-12307ffce3ad' 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>latitude_coordinates</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>reference_datum :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>_ChunkSizes :</span></dt><dd>1000</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>array([53.305004, 53.315   , 53.324993, 53.335003, 53.344997, 53.354992,\n",
       "       53.365   , 53.374996, 53.38499 , 53.395   , 53.404995, 53.415005,\n",
       "       53.425   , 53.434994, 53.445004, 53.454998, 53.464993, 53.475002,\n",
       "       53.484997, 53.49499 , 53.505   ], 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'>9.705 9.715 9.725 ... 10.29 10.31</div><input id='attrs-939419e5-4c8a-47d3-aa91-ee58fb36326f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-939419e5-4c8a-47d3-aa91-ee58fb36326f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c2a0299-907f-4ddb-9a09-89deaa4bb2f1' class='xr-var-data-in' type='checkbox'><label for='data-3c2a0299-907f-4ddb-9a09-89deaa4bb2f1' 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>longitude_coordinates</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>reference_datum :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>_ChunkSizes :</span></dt><dd>1000</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>array([ 9.704997,  9.714992,  9.725001,  9.734996,  9.74499 ,  9.755   ,\n",
       "        9.764995,  9.774989,  9.784999,  9.794993,  9.805003,  9.814998,\n",
       "        9.824992,  9.835002,  9.844996,  9.854991,  9.865001,  9.874995,\n",
       "        9.88499 ,  9.895   ,  9.904994,  9.914989,  9.924998,  9.934993,\n",
       "        9.945003,  9.954997,  9.964992,  9.975001,  9.984996,  9.99499 ,\n",
       "       10.005   , 10.014995, 10.024989, 10.034999, 10.044993, 10.055003,\n",
       "       10.064998, 10.074992, 10.085002, 10.094996, 10.104991, 10.115001,\n",
       "       10.124995, 10.13499 , 10.145   , 10.154994, 10.164989, 10.174998,\n",
       "       10.184993, 10.195003, 10.204997, 10.214992, 10.225001, 10.234996,\n",
       "       10.24499 , 10.255   , 10.264995, 10.274989, 10.284999, 10.294993,\n",
       "       10.305003], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c89247d0-6f03-4f08-9976-ecae3311f22d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c89247d0-6f03-4f08-9976-ecae3311f22d' 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>lst</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'>...</div><input id='attrs-9527c4e4-c01c-481b-933d-62e4134cdf81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9527c4e4-c01c-481b-933d-62e4134cdf81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71f365b7-5dd2-4493-a241-db2e803dd51e' class='xr-var-data-in' type='checkbox'><label for='data-71f365b7-5dd2-4493-a241-db2e803dd51e' 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>land surface temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>_ChunkSizes :</span></dt><dd>[   1 1000 1000]</dd></dl></div><div class='xr-var-data'><pre>[376614 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-35c9458f-f67d-48c4-9153-a464d4792871' class='xr-section-summary-in' type='checkbox'  ><label for='section-35c9458f-f67d-48c4-9153-a464d4792871' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-4eab57f9-85dc-4a04-ac68-261fa3301ec1' class='xr-index-data-in' type='checkbox'/><label for='index-4eab57f9-85dc-4a04-ac68-261fa3301ec1' 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;1995-08-01&#x27;, &#x27;1995-09-01&#x27;, &#x27;1995-10-01&#x27;, &#x27;1995-11-01&#x27;,\n",
       "               &#x27;1995-12-01&#x27;, &#x27;1996-07-01&#x27;, &#x27;1996-08-01&#x27;, &#x27;1996-09-01&#x27;,\n",
       "               &#x27;1996-10-01&#x27;, &#x27;1996-11-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2020-03-01&#x27;, &#x27;2020-04-01&#x27;, &#x27;2020-05-01&#x27;, &#x27;2020-06-01&#x27;,\n",
       "               &#x27;2020-07-01&#x27;, &#x27;2020-08-01&#x27;, &#x27;2020-09-01&#x27;, &#x27;2020-10-01&#x27;,\n",
       "               &#x27;2020-11-01&#x27;, &#x27;2020-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=294, freq=None))</pre></div></li><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-c2431aee-c6c5-4e26-98d2-7b693b7d22a4' class='xr-index-data-in' type='checkbox'/><label for='index-c2431aee-c6c5-4e26-98d2-7b693b7d22a4' 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([ 53.30500411987305, 53.314998626708984,  53.32499313354492,\n",
       "        53.33500289916992,  53.34499740600586,   53.3549919128418,\n",
       "         53.3650016784668, 53.374996185302734,  53.38499069213867,\n",
       "        53.39500045776367,  53.40499496459961,  53.41500473022461,\n",
       "        53.42499923706055, 53.434993743896484, 53.445003509521484,\n",
       "        53.45499801635742,  53.46499252319336,  53.47500228881836,\n",
       "         53.4849967956543, 53.494991302490234, 53.505001068115234],\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-85034ed3-5a70-49ae-a96f-4d67f2726519' class='xr-index-data-in' type='checkbox'/><label for='index-85034ed3-5a70-49ae-a96f-4d67f2726519' 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([ 9.704997062683105,  9.714991569519043,  9.725001335144043,\n",
       "         9.73499584197998,  9.744990348815918,  9.755000114440918,\n",
       "        9.764994621276855,  9.774989128112793,  9.784998893737793,\n",
       "         9.79499340057373,   9.80500316619873,  9.814997673034668,\n",
       "        9.824992179870605,  9.835001945495605,  9.844996452331543,\n",
       "         9.85499095916748,   9.86500072479248,  9.874995231628418,\n",
       "        9.884989738464355,  9.894999504089355,  9.904994010925293,\n",
       "         9.91498851776123,   9.92499828338623,  9.934992790222168,\n",
       "        9.945002555847168,  9.954997062683105,  9.964991569519043,\n",
       "        9.975001335144043,   9.98499584197998,  9.994990348815918,\n",
       "       10.005000114440918, 10.014994621276855, 10.024989128112793,\n",
       "       10.034998893737793,  10.04499340057373,  10.05500316619873,\n",
       "       10.064997673034668, 10.074992179870605, 10.085001945495605,\n",
       "       10.094996452331543,  10.10499095916748,  10.11500072479248,\n",
       "       10.124995231628418, 10.134989738464355, 10.144999504089355,\n",
       "       10.154994010925293,  10.16498851776123,  10.17499828338623,\n",
       "       10.184992790222168, 10.195002555847168, 10.204997062683105,\n",
       "       10.214991569519043, 10.225001335144043,  10.23499584197998,\n",
       "       10.244990348815918, 10.255000114440918, 10.264994621276855,\n",
       "       10.274989128112793, 10.284998893737793,  10.29499340057373,\n",
       "        10.30500316619873],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81b227b0-c425-4c26-a478-a239695b28ba' class='xr-section-summary-in' type='checkbox'  ><label for='section-81b227b0-c425-4c26-a478-a239695b28ba' class='xr-section-summary' >Attributes: <span>(43)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>ESA LST CCI IRCDR L3S V2.00</dd><dt><span>title :</span></dt><dd>ESA LST CCI land surface temperature time series from multiple IR sensors on LEO platforms.</dd><dt><span>institution :</span></dt><dd>University of Leicester</dd><dt><span>history :</span></dt><dd>2023-05-24 17:10:44: The CCI Open Data Portal aggregated all files in the dataset over the time variable for OPeNDAP access. For earlier history information see the original individual files at data.cci.ceda.ac.uk</dd><dt><span>references :</span></dt><dd>https://climate.esa.int/en/projects/land-surface-temperature</dd><dt><span>Conventions :</span></dt><dd>CF-1.8</dd><dt><span>product_version :</span></dt><dd>2.00</dd><dt><span>summary :</span></dt><dd>This file contains level L3S global land surface temperatures from SLSTRA. L3S data are derived from more than one sensor combined on a space and/or time grid.</dd><dt><span>keywords :</span></dt><dd>Earth Science; Land Surface; Land Temperature; Land Surface Temperature</dd><dt><span>id :</span></dt><dd>esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.DAY</dd><dt><span>naming_authority :</span></dt><dd>le.ac.uk</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global change Master Directory (GCMD) Science Keywords</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>comment :</span></dt><dd>These data were produced as part of the ESA LST CCI project.</dd><dt><span>date_created :</span></dt><dd>20230524T161044Z</dd><dt><span>creator_name :</span></dt><dd>University of Leicester Surface Temperature Group</dd><dt><span>creator_url :</span></dt><dd>https://climate.esa.int/en/projects/land-surface-temperature</dd><dt><span>creator_email :</span></dt><dd>djg20@le.ac.uk</dd><dt><span>project :</span></dt><dd>Climate Change Initiative - European Space Agency</dd><dt><span>geospatial_lat_min :</span></dt><dd>53.30500411987305</dd><dt><span>geospatial_lat_max :</span></dt><dd>53.505001068115234</dd><dt><span>geospatial_lon_min :</span></dt><dd>9.704997062683105</dd><dt><span>geospatial_lon_max :</span></dt><dd>10.30500316619873</dd><dt><span>geospatial_vertical_min :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>time_coverage_start :</span></dt><dd>19950801T000000</dd><dt><span>time_coverage_end :</span></dt><dd>20201231T235959</dd><dt><span>time_coverage_duration :</span></dt><dd>P9284DT23H59M59S</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table v71</dd><dt><span>license :</span></dt><dd>ESA CCI Data Policy: free and open access</dd><dt><span>platform :</span></dt><dd>ERS-2,Envisat,Sentinel-3A,Terra</dd><dt><span>sensor :</span></dt><dd>AATSR,ATSR-2,MODIS,SLSTRA</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_lon_resolution :</span></dt><dd>0.01</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.01</dd><dt><span>key_variables :</span></dt><dd>land_surface_temperature</dd><dt><span>format_version :</span></dt><dd>CCI Data Standards v2.2</dd><dt><span>spatial_resolution :</span></dt><dd>0.01 degree</dd><dt><span>doi :</span></dt><dd>10.5285/785ef9d3965442669bff899540747e28</dd><dt><span>tracking_id :</span></dt><dd>66f44ae9-1873-48be-a2df-c349022a9a97</dd><dt><span>History :</span></dt><dd>Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2)\n",
       "Original Dataset = esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.DAY; Translation Date = 2025-03-21T11:09:46.355Z</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 2MB\n",
       "Dimensions:  (time: 294, lat: 21, lon: 61)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2kB 1995-08-01 1995-09-01 ... 2020-12-01\n",
       "  * lat      (lat) float32 84B 53.31 53.31 53.32 53.34 ... 53.48 53.49 53.51\n",
       "  * lon      (lon) float32 244B 9.705 9.715 9.725 9.735 ... 10.28 10.29 10.31\n",
       "Data variables:\n",
       "    lst      (time, lat, lon) float32 2MB ...\n",
       "Attributes: (12/43)\n",
       "    source:                     ESA LST CCI IRCDR L3S V2.00\n",
       "    title:                      ESA LST CCI land surface temperature time ser...\n",
       "    institution:                University of Leicester\n",
       "    history:                    2023-05-24 17:10:44: The CCI Open Data Portal...\n",
       "    references:                 https://climate.esa.int/en/projects/land-surf...\n",
       "    Conventions:                CF-1.8\n",
       "    ...                         ...\n",
       "    key_variables:              land_surface_temperature\n",
       "    format_version:             CCI Data Standards v2.2\n",
       "    spatial_resolution:         0.01 degree\n",
       "    doi:                        10.5285/785ef9d3965442669bff899540747e28\n",
       "    tracking_id:                66f44ae9-1873-48be-a2df-c349022a9a97\n",
       "    History:                    Translated to CF-1.0 Conventions by Netcdf-Ja..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaa221de-37e6-47df-bedc-89728598d08d",
   "metadata": {},
   "source": [
    "Use the coverage data to plot a map of land surface temperature for a specific point in time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9a933410-db5c-4d25-ba10-21e64ca6d560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa880314140>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.lst.isel(time=100).plot.imshow()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3766cf9-9d90-4a3f-9621-0d89ff20a1f3",
   "metadata": {},
   "source": [
    "Use the coverage data to plot a graph of land surface temperature against time for a specific location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d39d33e9-3799-4a2c-8beb-c1249ea8f603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa87d47d7c0>]"
      ]
     },
     "execution_count": 14,
     "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": [
    "ds.lst.sel(lat=53.4, lon=10.0, method=\"nearest\").plot()"
   ]
  }
 ],
 "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": 5
}