tutorials/colab/2.xarray_netcdf_tutorial.ipynb

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    "# Using Xarray with NetCDF and DAP4\n",
    "\n",
    "This notebook contains a simple example of how to use Xarray with the NetCDF Python interface and DAP4 to pull data from remote DAP4 servers into your Python program's memory space so that the data may be analyized and displayed.\n",
    "\n",
    "The Xarray paclage \"makes working with labelled multi-dimensional arrays in Python simple, efficient, and fun!\"\n",
    "\n",
    "This tutorial utilizes the NASA Global High Resolution Sea Surface Temperature from the GOES-16 satellite. You may wish to review the summary on our Tutorial Datasets page before continuing.\n",
    "\n",
    "To read more about this package, see __[Xarray](https://docs.xarray.dev/en/stable/index.html)__ and __[netCDF4](https://unidata.github.io/netcdf4-python/)__ which both include information on OPeNDAP.\n",
    "\n",
    "You can run this tutorial in your browser using Colab.<br>\n",
    "\n",
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/OPENDAP/NASA-tutorials/blob/main/tutorials/colab/2.xarray_netcdf_tutorial.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>\n",
    "\n",
    "<br>\n",
    "\n",
    "### Contents\n",
    "[Imports](#What-you-need-to-import) Packages you need to read data from the OPeNDAP server<br>\n",
    "[Opening the remote dataset](#Open-the-remote-dataset-using-the-URL) The initial interaction with the remote dataset<br>\n",
    "[Looking at a variable](#Looking-at-a-variable) Get attribute information about a specific variable<br>\n",
    "[Getting useful information about variables](#Getting-useful-information-a-variable) More about variables<br>\n",
    "[Accessing data](#Accessing-data) Read values in the arrays<br>\n",
    "[Plotting data](#Plotting-data) This section imports some more packages<br>\n",
    "[References](#References)<br>\n",
    "\n",
    "Copyright (C) 2023 OPeNDAP, Inc. This Jupyter Notebook is made available under the Creative Commons Attribution license 4.0.\n"
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    "## What you need to import\n",
    "\n",
    "To use the netCDF4 Python package to read from OPeNDAP servers, including the NASA Cloud-based server, you only need to include the `netCDF4` package. Later in this notebook we will show some plots that use other packages for the graphics."
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      "Requirement already satisfied: netcdf4 in /Users/jimg/opt/miniforge3/envs/opendap/lib/python3.9/site-packages (1.6.2)\r\n",
      "Requirement already satisfied: numpy>=1.9 in /Users/jimg/opt/miniforge3/envs/opendap/lib/python3.9/site-packages (from netcdf4) (1.23.5)\r\n",
      "Requirement already satisfied: cftime in /Users/jimg/opt/miniforge3/envs/opendap/lib/python3.9/site-packages (from netcdf4) (1.6.2)\r\n",
      "Requirement already satisfied: xarray in /Users/jimg/opt/miniforge3/envs/opendap/lib/python3.9/site-packages (2022.11.0)\r\n",
      "Requirement already satisfied: packaging>=21.0 in /Users/jimg/opt/miniforge3/envs/opendap/lib/python3.9/site-packages (from xarray) (23.0)\r\n",
      "Requirement already satisfied: numpy>=1.20 in /Users/jimg/opt/miniforge3/envs/opendap/lib/python3.9/site-packages (from xarray) (1.23.5)\r\n",
      "Requirement already satisfied: pandas>=1.3 in /Users/jimg/opt/miniforge3/envs/opendap/lib/python3.9/site-packages (from xarray) (1.5.3)\r\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/jimg/opt/miniforge3/envs/opendap/lib/python3.9/site-packages (from pandas>=1.3->xarray) (2.8.2)\r\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/jimg/opt/miniforge3/envs/opendap/lib/python3.9/site-packages (from pandas>=1.3->xarray) (2022.7)\r\n",
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    "# Use pip3 to install netCDF4 until conda has a version that authenticates with EDL.\n",
    "# For xarray, install netCDF4 explicitly since the default netCDF4 version will fail\n",
    "# to properly apply the CF scale_factor attribute.\n",
    "\n",
    "! pip3 install netcdf4 xarray earthaccess\n",
    "\n",
    "! git clone https://github.com/OPENDAP/NASA-tutorials.git\n",
    "\n",
    "# Create dodsrc files\n",
    "! ./NASA-tutorials/tutorials/setup_dodsrc.sh\n",
    "\n",
    "import xarray as xr\n",
    "import numpy as np    # Note strictly needed since xarray imports this, but added for completeness"
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    "### Tutorial configuration\n",
    "\n",
    "Set the variables below to switch between data served using the NASA Cloud-based server and the OPeNDAP test server and to toggle verbose output mode.\n",
    "By default we will use data served from our test server since that does not require authentication. If you want to see those same data from the NASA server\n",
    "you will need to obtain credentials for NASA's __[Earthdata Login](https://urs.earthdata.nasa.gov/)__ and for the (simple) steps to\n",
    "__[configure netCDF4](https://opendap.github.io/documentation/tutorials/ClientAuthentication_at.html#_matlab_ferret_other_applications_that_use_netcdf_c)__\n",
    "to use those.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "    Be sure to start the URL with the <b><tt>dap4:</tt></b> prefix and not the <b><tt>https:</tt></b>\n",
    "    prefix when using <b>Xarray</b> with the <b>netCDF4 Python</b> package and accessing data served by \n",
    "    NASA's OPeNDAP in the Cloud server. The OPeNDAP in the Cloud server will otherwise \n",
    "    send responses that (often) cannot represent some of the variables in the dataset.\n",
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      "The netCDF4 library version must be 4.9.0 or be >= 4.9.3 for EDL authentication to work.\n",
      "dataset_url: dap4://opendap.earthdata.nasa.gov/collections/C2036877806-POCLOUD/granules/20220812010000-OSISAF-L3C_GHRSST-SSTsubskin-GOES16-ssteqc_goes16_20220812_010000-v02.0-fv01.0\n"
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    "USE_NASA_CLOUD_BASED_DATA = True\n",
    "\n",
    "VERBOSE = True\n",
    "\n",
    "# Detect authentication has already happened. This is easy to override.\n",
    "ALREADY_AUTH = False\n",
    "import subprocess\n",
    "# shell exit 0 is 'true'\n",
    "if not subprocess.call(['bash', './is_authenticated.sh']):\n",
    "    ALREADY_AUTH = True\n",
    "\n",
    "if(USE_NASA_CLOUD_BASED_DATA):\n",
    "    print(f\"The netCDF4 library version must be 4.9.0 or be >= 4.9.3 for EDL authentication to work.\")\n",
    "    # print(f\"The netCDF4 library version is {nc4.getlibversion()}\")\n",
    "    if not ALREADY_AUTH:\n",
    "        import earthaccess\n",
    "        auth = earthaccess.login(strategy=\"interactive\", persist=True)\n",
    "        subprocess.call(['bash', './setup_dodsrc.sh'])\n",
    "    dataset_url = \"dap4://opendap.earthdata.nasa.gov/collections/C2036877806-POCLOUD/granules/20220812010000-OSISAF-L3C_GHRSST-SSTsubskin-GOES16-ssteqc_goes16_20220812_010000-v02.0-fv01.0\"\n",
    "else:\n",
    "    dataset_url = \"dap4://test.opendap.org/opendap/tutorials/20220812010000-OSISAF-L3C_GHRSST-SSTsubskin-GOES16-ssteqc_goes16_20220812_010000-v02.0-fv01.0.nc\"\n",
    "\n",
    "# define 'vprint()'' as print() if VERBOSE is True, otherwise define it as a function that does nothing.\n",
    "vprint = print if VERBOSE else lambda *a, **k: None\n",
    "\n",
    "vprint(f\"dataset_url: {dataset_url}\")"
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    "## Open the remote dataset using the URL\n",
    "\n",
    "When a URL is passed to the xarray.open_dataset() function, there is a delay as the information about this dataset is read from the remote site. The delay in opening a remote dataset using xarray with the netCDF backend (the default) is proportional to the total size of the dataset. In other tutorials, we will explore ways to reduce this initial wait. \n",
    "\n",
    "If the returned <em>Xarray Dataset</em> object (<em>remote_data</em> in this example) is printed, we can see that information that includes the data model, file format and dataset attributes. The Xarray Dataset object has a particularly nice presentation, so we show that here. It can also be printed using the python <em>print()</em> function.\n",
    "\n",
    "The variables' names are used to access the values (shown later)."
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       ".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;\n",
       "Dimensions:                    (time: 1, lat: 2400, lon: 2400)\n",
       "Coordinates:\n",
       "  * lon                        (lon) float32 -135.0 -134.9 ... -15.07 -15.02\n",
       "  * time                       (time) datetime64[ns] 2022-08-12T01:00:00\n",
       "  * lat                        (lat) float32 -59.97 -59.92 ... 59.92 59.97\n",
       "Data variables: (12/16)\n",
       "    wind_speed                 (time, lat, lon) float32 ...\n",
       "    or_longitude               (time, lat, lon) float32 ...\n",
       "    sses_standard_deviation    (time, lat, lon) float32 ...\n",
       "    sst_dtime                  (time, lat, lon) timedelta64[ns] ...\n",
       "    solar_zenith_angle         (time, lat, lon) float32 ...\n",
       "    sea_ice_fraction           (time, lat, lon) float32 ...\n",
       "    ...                         ...\n",
       "    quality_level              (time, lat, lon) float32 ...\n",
       "    dt_analysis                (time, lat, lon) float32 ...\n",
       "    satellite_zenith_angle     (time, lat, lon) float32 ...\n",
       "    adi_dtime_from_sst         (time, lat, lon) float32 ...\n",
       "    or_latitude                (time, lat, lon) float32 ...\n",
       "    sses_bias                  (time, lat, lon) float32 ...\n",
       "Attributes: (12/48)\n",
       "    Conventions:                CF-1.4\n",
       "    title:                      Sea Surface Temperature\n",
       "    summary:                    The L3C product derived from GOES16/ABI brigh...\n",
       "    references:                 Geostationary Sea Surface Temperature Product...\n",
       "    institution:                OSISAF\n",
       "    comment:                    \n",
       "    ...                         ...\n",
       "    date_created:               20220812T015542Z\n",
       "    start_time:                 20220812T004042Z\n",
       "    time_coverage_start:        20220812T004042Z\n",
       "    stop_time:                  20220812T011929Z\n",
       "    time_coverage_end:          20220812T011929Z\n",
       "    netcdf_version_id:          4.6.3</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-fa750f75-d0ef-4bd6-906d-c7caf822c52d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fa750f75-d0ef-4bd6-906d-c7caf822c52d' 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>: 1</li><li><span class='xr-has-index'>lat</span>: 2400</li><li><span class='xr-has-index'>lon</span>: 2400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-81ddab7c-8a51-4920-89d1-a124ea703cc9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-81ddab7c-8a51-4920-89d1-a124ea703cc9' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-135.0 -134.9 ... -15.07 -15.02</div><input id='attrs-3c3b097f-e1f6-4009-919a-ae190ccc66e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c3b097f-e1f6-4009-919a-ae190ccc66e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c726f8f-130d-4ee9-9f8e-9ff2d7a4f594' class='xr-var-data-in' type='checkbox'><label for='data-8c726f8f-130d-4ee9-9f8e-9ff2d7a4f594' 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</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.00009</dd><dt><span>valid_max :</span></dt><dd>180.00003</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array([-134.975, -134.925, -134.875, ...,  -15.125,  -15.075,  -15.025],\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'>2022-08-12T01:00:00</div><input id='attrs-ba14a19e-2bd6-468d-ab49-94b20b4cb4cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba14a19e-2bd6-468d-ab49-94b20b4cb4cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-278e472b-0551-4569-ae45-014f537ff819' class='xr-var-data-in' type='checkbox'><label for='data-278e472b-0551-4569-ae45-014f537ff819' 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 sst file</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-08-12T01:00:00.000000000&#x27;], 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'>-59.97 -59.92 ... 59.92 59.97</div><input id='attrs-164d9fcf-6329-41ff-81ea-23431fd45414' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-164d9fcf-6329-41ff-81ea-23431fd45414' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfeacd3a-55dc-4c2d-855b-5744e2db6cf8' class='xr-var-data-in' type='checkbox'><label for='data-cfeacd3a-55dc-4c2d-855b-5744e2db6cf8' 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</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.000046</dd><dt><span>valid_max :</span></dt><dd>90.000015</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array([-59.975, -59.925, -59.875, ...,  59.875,  59.925,  59.975],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a8ce63a8-5783-498b-94d6-e44da404c60b' class='xr-section-summary-in' type='checkbox'  ><label for='section-a8ce63a8-5783-498b-94d6-e44da404c60b' class='xr-section-summary' >Data variables: <span>(16)</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>wind_speed</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-f4211916-0ed0-4da7-ae71-9fc0c2ab4d6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4211916-0ed0-4da7-ae71-9fc0c2ab4d6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28ee71f4-59a4-46e0-84f8-a9c3b3bee006' class='xr-var-data-in' type='checkbox'><label for='data-28ee71f4-59a4-46e0-84f8-a9c3b3bee006' 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>10m wind speed</dd><dt><span>standard_name :</span></dt><dd>wind_speed</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>height :</span></dt><dd>10 m</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>time_offset :</span></dt><dd>0.0</dd><dt><span>source :</span></dt><dd>WSP-ECMWF-Forecast</dd><dt><span>comment :</span></dt><dd>These wind speeds were created by the ECMWF and represent winds at 10 metres above the sea surface. </dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>or_longitude</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-e2fd6a6d-60dc-4c1a-9d3a-dafecc67cef6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2fd6a6d-60dc-4c1a-9d3a-dafecc67cef6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4b34aea-a66a-45cc-9060-4b02e7736921' class='xr-var-data-in' type='checkbox'><label for='data-d4b34aea-a66a-45cc-9060-4b02e7736921' 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>original longitude of the SST value</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>-18000</dd><dt><span>valid_max :</span></dt><dd>18000</dd><dt><span>comment :</span></dt><dd>Original longitude of the SST value</dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sses_standard_deviation</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-925b90c3-000c-4757-b8e9-33d63f0d7761' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-925b90c3-000c-4757-b8e9-33d63f0d7761' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a544d6c0-bbdc-4a05-9b8e-b9bbda583b07' class='xr-var-data-in' type='checkbox'><label for='data-a544d6c0-bbdc-4a05-9b8e-b9bbda583b07' 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>SSES standard deviation</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>comment :</span></dt><dd>Standard deviation estimate derived using the techniques described at http://www.ghrsst.org/SSES-Description-of-schemes.html</dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst_dtime</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d312be2d-04d3-4acb-8e82-e27b9a4e3fdf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d312be2d-04d3-4acb-8e82-e27b9a4e3fdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ada250e0-a578-4e7a-a5f7-97ef429ad3cc' class='xr-var-data-in' type='checkbox'><label for='data-ada250e0-a578-4e7a-a5f7-97ef429ad3cc' 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>time difference from reference time</dd><dt><span>valid_min :</span></dt><dd>-2147483647</dd><dt><span>valid_max :</span></dt><dd>2147483647</dd><dt><span>comment :</span></dt><dd>time plus sst_dtime gives seconds after 00:00:00 UTC January 1, 1981</dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=timedelta64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solar_zenith_angle</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-e62485f9-e852-449b-98ca-6dc36f9c288c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e62485f9-e852-449b-98ca-6dc36f9c288c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8ae84bb-051a-4c6c-9df7-23444f36470e' class='xr-var-data-in' type='checkbox'><label for='data-c8ae84bb-051a-4c6c-9df7-23444f36470e' 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>solar zenith angle</dd><dt><span>units :</span></dt><dd>angular_degree</dd><dt><span>valid_min :</span></dt><dd>-90</dd><dt><span>valid_max :</span></dt><dd>90</dd><dt><span>comment :</span></dt><dd>The solar zenith angle at the time of the SST observations.</dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_ice_fraction</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-55101139-b562-4e7a-9271-5e21c82355f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55101139-b562-4e7a-9271-5e21c82355f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec995074-a90b-4eac-be20-bb668cef6b00' class='xr-var-data-in' type='checkbox'><label for='data-ec995074-a90b-4eac-be20-bb668cef6b00' 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>sea ice fraction</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd> </dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>time_offset :</span></dt><dd>0.0</dd><dt><span>source :</span></dt><dd>ICE-OSISAF</dd><dt><span>comment :</span></dt><dd>Fractional sea ice cover from OSISAF ice product</dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>l2p_flags</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aabad346-7bc2-488d-b8d9-9d03bd4b6bd0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aabad346-7bc2-488d-b8d9-9d03bd4b6bd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d571a77a-14f3-4930-b41d-78ec053b26ce' class='xr-var-data-in' type='checkbox'><label for='data-d571a77a-14f3-4930-b41d-78ec053b26ce' 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>L2P flags</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>15</dd><dt><span>flag_meanings :</span></dt><dd>microwave land ice lake</dd><dt><span>flag_masks :</span></dt><dd>[1 2 4 8]</dd><dt><span>comment :</span></dt><dd>These flags are important to properly use the data.</dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sources_of_adi</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-5159d236-7378-4615-80f5-845c87938212' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5159d236-7378-4615-80f5-845c87938212' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14d69b4d-9adb-4f41-9d9c-f3206bf74efb' class='xr-var-data-in' type='checkbox'><label for='data-14d69b4d-9adb-4f41-9d9c-f3206bf74efb' 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>sources of aerosol dynamic indicator</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>2</dd><dt><span>flag_meanings :</span></dt><dd>no_data AOD-NAAPS-ADI SDI-OSISAF-ADI</dd><dt><span>flag_values :</span></dt><dd>[0 1 2]</dd><dt><span>comment :</span></dt><dd>This variable provides a pixel by pixel description of where aerosol optical depth were derived from.</dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aerosol_dynamic_indicator</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-747ebd5e-6752-497a-b9b5-f8cefccaccd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-747ebd5e-6752-497a-b9b5-f8cefccaccd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3e21318-65ae-41d5-80a3-5411bfeed501' class='xr-var-data-in' type='checkbox'><label for='data-f3e21318-65ae-41d5-80a3-5411bfeed501' 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>aerosol dynamic indicator</dd><dt><span>units :</span></dt><dd> </dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>source :</span></dt><dd>sources_of_adi</dd><dt><span>comment :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=float32]</pre></div></li><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'>...</div><input id='attrs-497594a1-615a-415e-8798-d14e4a9ef2a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-497594a1-615a-415e-8798-d14e4a9ef2a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c3faa4c-c8f2-4b2b-819f-96149e3f0ef6' class='xr-var-data-in' type='checkbox'><label for='data-8c3faa4c-c8f2-4b2b-819f-96149e3f0ef6' 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>sea surface subskin temperature</dd><dt><span>standard_name :</span></dt><dd>sea_surface_subskin_temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-300</dd><dt><span>valid_max :</span></dt><dd>4500</dd><dt><span>depth :</span></dt><dd>1 millimeter</dd><dt><span>source :</span></dt><dd>GOES_Imager</dd><dt><span>comment :</span></dt><dd>Temperature of the subskin of the ocean</dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quality_level</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-057b6a65-09a4-4c07-a790-0a0ac6ad57fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-057b6a65-09a4-4c07-a790-0a0ac6ad57fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-543a0744-ffbb-4ff8-9815-102a53de98d7' class='xr-var-data-in' type='checkbox'><label for='data-543a0744-ffbb-4ff8-9815-102a53de98d7' 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>quality level of SST pixel</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>5</dd><dt><span>flag_meanings :</span></dt><dd>no_data bad_data worst_quality low_quality acceptable_quality best_quality</dd><dt><span>flag_values :</span></dt><dd>[0 1 2 3 4 5]</dd><dt><span>comment :</span></dt><dd>These are the overall quality indicators and are used for all GHRSST SSTs</dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dt_analysis</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-e6d450df-b692-4102-b6fc-7cac9f224f26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6d450df-b692-4102-b6fc-7cac9f224f26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4117005f-58b1-4b1c-8444-bd35ff774126' class='xr-var-data-in' type='checkbox'><label for='data-4117005f-58b1-4b1c-8444-bd35ff774126' 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>deviation from SST analysis or reference climatology</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>reference :</span></dt><dd>OSTIA</dd><dt><span>comment :</span></dt><dd>The difference between this SST and the previous day&amp;apos;s SST analysis</dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>satellite_zenith_angle</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-ae4ea567-dc16-47ad-9172-9554950cd54a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae4ea567-dc16-47ad-9172-9554950cd54a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ada2bc0-e03d-4fc3-8548-619155562177' class='xr-var-data-in' type='checkbox'><label for='data-8ada2bc0-e03d-4fc3-8548-619155562177' 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>satellite zenith angle</dd><dt><span>units :</span></dt><dd>angular_degree</dd><dt><span>valid_min :</span></dt><dd>-90</dd><dt><span>valid_max :</span></dt><dd>90</dd><dt><span>comment :</span></dt><dd>The satellite zenith angle at the time of the SST observations.</dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adi_dtime_from_sst</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-944c57de-5853-4f49-8ea1-54c44b829c6d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-944c57de-5853-4f49-8ea1-54c44b829c6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14cb06b1-9c47-473e-9eb9-3d9e9ab2f857' class='xr-var-data-in' type='checkbox'><label for='data-14cb06b1-9c47-473e-9eb9-3d9e9ab2f857' 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>time difference of ADI data from sst measurement</dd><dt><span>units :</span></dt><dd>hour</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>comment :</span></dt><dd>Difference in hours between the ADI and SST data</dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>or_latitude</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-42cf1bca-b928-46c1-a491-40f4b9157b08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42cf1bca-b928-46c1-a491-40f4b9157b08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d8e8043-0753-49d0-ac54-3bfca8bdc67e' class='xr-var-data-in' type='checkbox'><label for='data-6d8e8043-0753-49d0-ac54-3bfca8bdc67e' 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>original latitude of the SST value</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>-9000</dd><dt><span>valid_max :</span></dt><dd>9000</dd><dt><span>comment :</span></dt><dd>Original latitude of the SST value</dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sses_bias</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-c9ff2739-388c-46d8-a51d-120c59748129' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9ff2739-388c-46d8-a51d-120c59748129' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68402957-372e-4d44-b5b3-d59a41f42ece' class='xr-var-data-in' type='checkbox'><label for='data-68402957-372e-4d44-b5b3-d59a41f42ece' 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>SSES bias estimate</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>comment :</span></dt><dd>Bias estimate derived using the techniques described at http://www.ghrsst.org/SSES-Description-of-schemes.html</dd></dl></div><div class='xr-var-data'><pre>[5760000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-963017e6-9caa-44c8-8ad8-4ac932b85406' class='xr-section-summary-in' type='checkbox'  ><label for='section-963017e6-9caa-44c8-8ad8-4ac932b85406' 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>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dd71563e-773e-4414-9883-6ea3a6a76be5' class='xr-index-data-in' type='checkbox'/><label for='index-dd71563e-773e-4414-9883-6ea3a6a76be5' 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([-134.97500610351562,  -134.9250030517578,            -134.875,\n",
       "               -134.8249969482422, -134.77499389648438, -134.72500610351562,\n",
       "               -134.6750030517578,            -134.625,  -134.5749969482422,\n",
       "              -134.52499389648438,\n",
       "              ...\n",
       "              -15.475000381469727, -15.425000190734863,             -15.375,\n",
       "              -15.324999809265137, -15.274999618530273, -15.225000381469727,\n",
       "              -15.175000190734863,             -15.125, -15.074999809265137,\n",
       "              -15.024999618530273],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=2400))</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-ba56ab52-d827-4f72-add9-73e620acb2fc' class='xr-index-data-in' type='checkbox'/><label for='index-ba56ab52-d827-4f72-add9-73e620acb2fc' 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;2022-08-12 01:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, 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><div></div><input id='index-f0c006b1-b173-474a-9fc0-bded2481ee4a' class='xr-index-data-in' type='checkbox'/><label for='index-f0c006b1-b173-474a-9fc0-bded2481ee4a' 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([-59.974998474121094,  -59.92499923706055,             -59.875,\n",
       "               -59.82500076293945, -59.775001525878906, -59.724998474121094,\n",
       "               -59.67499923706055,             -59.625,  -59.57500076293945,\n",
       "              -59.525001525878906,\n",
       "              ...\n",
       "               59.525001525878906,   59.57500076293945,              59.625,\n",
       "                59.67499923706055,  59.724998474121094,  59.775001525878906,\n",
       "                59.82500076293945,              59.875,   59.92499923706055,\n",
       "               59.974998474121094],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=2400))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-36506d0a-b664-4455-b68c-a92682e09cd2' class='xr-section-summary-in' type='checkbox'  ><label for='section-36506d0a-b664-4455-b68c-a92682e09cd2' class='xr-section-summary' >Attributes: <span>(48)</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-1.4</dd><dt><span>title :</span></dt><dd>Sea Surface Temperature</dd><dt><span>summary :</span></dt><dd>The L3C product derived from GOES16/ABI brightness temperatures.</dd><dt><span>references :</span></dt><dd>Geostationary Sea Surface Temperature Product User Manual, http://www.osi-saf.org</dd><dt><span>institution :</span></dt><dd>OSISAF</dd><dt><span>comment :</span></dt><dd></dd><dt><span>license :</span></dt><dd>All intellectual property rights of the Ocean &amp;amp; Sea Ice SAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT&amp;apos;s copyright credit must be shown by displaying the words &amp;apos;Copyright EUMETSAT&amp;apos; under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the Ocean &amp;amp; Sea Ice SAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the Ocean &amp;amp; Sea Ice SAF products.</dd><dt><span>id :</span></dt><dd>GOES16-OSISAF-L3C-v1.0</dd><dt><span>product_id :</span></dt><dd>OSI-207-b</dd><dt><span>naming_authority :</span></dt><dd>org.ghrsst</dd><dt><span>product_version :</span></dt><dd>1.0</dd><dt><span>gds_version_id :</span></dt><dd>2.0</dd><dt><span>file_quality_level :</span></dt><dd>3</dd><dt><span>spatial_resolution :</span></dt><dd>0.05 degree</dd><dt><span>northernmost_latitude :</span></dt><dd>60.000008</dd><dt><span>southernmost_latitude :</span></dt><dd>-60.000023</dd><dt><span>easternmost_longitude :</span></dt><dd>-15.000006</dd><dt><span>westernmost_longitude :</span></dt><dd>-135.00009</dd><dt><span>source :</span></dt><dd>GOES_ABI</dd><dt><span>platform :</span></dt><dd>GOES16</dd><dt><span>sensor :</span></dt><dd>GOES_ABI</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>metadata_link :</span></dt><dd>N/A</dd><dt><span>keywords :</span></dt><dd>Oceans &amp;gt; Ocean Temperature &amp;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_lat_resolution :</span></dt><dd>0.05</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.05</dd><dt><span>acknowledgment :</span></dt><dd>In case SAF data (pre-operational or operational) has been used for the study described in a paper the following sentence would be an appropriate reference to the funding coming from EUMETSAT: The data from the EUMETSAT Satellite Application Facility on Ocean &amp;amp; Sea Ice  used in this study are accessible through the SAF&amp;apos;s homepage http://www.osi-saf.org</dd><dt><span>creator_name :</span></dt><dd>O&amp;amp;SI SAF</dd><dt><span>creator_email :</span></dt><dd>osi-saf.helpdesk@meteo.fr</dd><dt><span>creator_url :</span></dt><dd>http://www.osi-saf.org</dd><dt><span>project :</span></dt><dd>Group for High Resolution Sea Surface Temperature</dd><dt><span>publisher_name :</span></dt><dd>The 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>L3C</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>history :</span></dt><dd>METEO-FRANCE GEOSAFO v1.1.8</dd><dt><span>uuid :</span></dt><dd>DF556788-19E1-11ED-A08A-48DF370DAD10</dd><dt><span>date_created :</span></dt><dd>20220812T015542Z</dd><dt><span>start_time :</span></dt><dd>20220812T004042Z</dd><dt><span>time_coverage_start :</span></dt><dd>20220812T004042Z</dd><dt><span>stop_time :</span></dt><dd>20220812T011929Z</dd><dt><span>time_coverage_end :</span></dt><dd>20220812T011929Z</dd><dt><span>netcdf_version_id :</span></dt><dd>4.6.3</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                    (time: 1, lat: 2400, lon: 2400)\n",
       "Coordinates:\n",
       "  * lon                        (lon) float32 -135.0 -134.9 ... -15.07 -15.02\n",
       "  * time                       (time) datetime64[ns] 2022-08-12T01:00:00\n",
       "  * lat                        (lat) float32 -59.97 -59.92 ... 59.92 59.97\n",
       "Data variables: (12/16)\n",
       "    wind_speed                 (time, lat, lon) float32 ...\n",
       "    or_longitude               (time, lat, lon) float32 ...\n",
       "    sses_standard_deviation    (time, lat, lon) float32 ...\n",
       "    sst_dtime                  (time, lat, lon) timedelta64[ns] ...\n",
       "    solar_zenith_angle         (time, lat, lon) float32 ...\n",
       "    sea_ice_fraction           (time, lat, lon) float32 ...\n",
       "    ...                         ...\n",
       "    quality_level              (time, lat, lon) float32 ...\n",
       "    dt_analysis                (time, lat, lon) float32 ...\n",
       "    satellite_zenith_angle     (time, lat, lon) float32 ...\n",
       "    adi_dtime_from_sst         (time, lat, lon) float32 ...\n",
       "    or_latitude                (time, lat, lon) float32 ...\n",
       "    sses_bias                  (time, lat, lon) float32 ...\n",
       "Attributes: (12/48)\n",
       "    Conventions:                CF-1.4\n",
       "    title:                      Sea Surface Temperature\n",
       "    summary:                    The L3C product derived from GOES16/ABI brigh...\n",
       "    references:                 Geostationary Sea Surface Temperature Product...\n",
       "    institution:                OSISAF\n",
       "    comment:                    \n",
       "    ...                         ...\n",
       "    date_created:               20220812T015542Z\n",
       "    start_time:                 20220812T004042Z\n",
       "    time_coverage_start:        20220812T004042Z\n",
       "    stop_time:                  20220812T011929Z\n",
       "    time_coverage_end:          20220812T011929Z\n",
       "    netcdf_version_id:          4.6.3"
      ]
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    "%%time\n",
    "remote_data = xr.open_dataset(dataset_url)\n",
    "\n",
    "# Instead of using print(), let's use xarray's built-in preint representation\n",
    "\n",
    "remote_data"
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    "## Looking at a variable\n",
    "\n",
    "Because information about each variable was read during the xarray.open_dataset() call, the response for examining information for each variable is fast. We can click on the triangle next to 'Data variables:' above and see the name, dimensions and datatype of each variable in the data set. Use that name as an index into the remote_datga object.\n",
    "\n",
    "The Dataset object retruend by the xarray.open_dataset() method is a collection similar to a python dictionary \"of DataArray objects with aligned dimensions.\" Each named element is a <em>DataArray</em>. See the __[terminology](https://docs.xarray.dev/en/stable/user-guide/terminology.html)__ section of the Xarray documentation for more information. \n",
    "\n",
    "Let's look at the Sea Surface Temperature variable (i.e., <em>sea_surface_temperature</em>). As with the object returned by open_dataset(), the object returned when accessing remote_data['sea_surface_temperature'] has a very nice print representation."
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     "text": [
      "CPU times: user 761 µs, sys: 1.87 ms, total: 2.63 ms\n",
      "Wall time: 2.73 ms\n"
     ]
    },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sea_surface_temperature&#x27; (time: 1, lat: 2400, lon: 2400)&gt;\n",
       "[5760000 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -135.0 -134.9 -134.9 -134.8 ... -15.12 -15.07 -15.02\n",
       "  * time     (time) datetime64[ns] 2022-08-12T01:00:00\n",
       "  * lat      (lat) float32 -59.97 -59.92 -59.88 -59.83 ... 59.88 59.92 59.97\n",
       "Attributes:\n",
       "    long_name:      sea surface subskin temperature\n",
       "    standard_name:  sea_surface_subskin_temperature\n",
       "    units:          kelvin\n",
       "    valid_min:      -300\n",
       "    valid_max:      4500\n",
       "    depth:          1 millimeter\n",
       "    source:         GOES_Imager\n",
       "    comment:        Temperature of the subskin of the ocean</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>: 1</li><li><span class='xr-has-index'>lat</span>: 2400</li><li><span class='xr-has-index'>lon</span>: 2400</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-69e73492-df20-48b2-ad2f-a86d0bbb8b6d' class='xr-array-in' type='checkbox' checked><label for='section-69e73492-df20-48b2-ad2f-a86d0bbb8b6d' 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>...</span></div><div class='xr-array-data'><pre>[5760000 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-687d71b7-13ee-4458-8d85-31905210d597' class='xr-section-summary-in' type='checkbox'  checked><label for='section-687d71b7-13ee-4458-8d85-31905210d597' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-135.0 -134.9 ... -15.07 -15.02</div><input id='attrs-165da235-7f1d-4fd8-af86-7e69d9381464' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-165da235-7f1d-4fd8-af86-7e69d9381464' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60d58096-17a1-4cbd-b12e-5e3c3774f491' class='xr-var-data-in' type='checkbox'><label for='data-60d58096-17a1-4cbd-b12e-5e3c3774f491' 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</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.00009</dd><dt><span>valid_max :</span></dt><dd>180.00003</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array([-134.975, -134.925, -134.875, ...,  -15.125,  -15.075,  -15.025],\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'>2022-08-12T01:00:00</div><input id='attrs-e8621bcc-0aaf-453d-abc0-ae202a2fe6f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8621bcc-0aaf-453d-abc0-ae202a2fe6f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1abc6eee-f65e-48c9-be65-cce62b48bc72' class='xr-var-data-in' type='checkbox'><label for='data-1abc6eee-f65e-48c9-be65-cce62b48bc72' 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 sst file</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-08-12T01:00:00.000000000&#x27;], 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'>-59.97 -59.92 ... 59.92 59.97</div><input id='attrs-15ef6dbc-71bf-4886-a20b-574d4eaf8a27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15ef6dbc-71bf-4886-a20b-574d4eaf8a27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84bf843c-e2f6-464a-b72f-8c26fc8e691f' class='xr-var-data-in' type='checkbox'><label for='data-84bf843c-e2f6-464a-b72f-8c26fc8e691f' 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</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.000046</dd><dt><span>valid_max :</span></dt><dd>90.000015</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array([-59.975, -59.925, -59.875, ...,  59.875,  59.925,  59.975],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-109578fb-f31c-4770-8d67-d99a88fb3204' class='xr-section-summary-in' type='checkbox'  ><label for='section-109578fb-f31c-4770-8d67-d99a88fb3204' 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>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bf8bd9cc-7915-4fc0-820a-600315eca867' class='xr-index-data-in' type='checkbox'/><label for='index-bf8bd9cc-7915-4fc0-820a-600315eca867' 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([-134.97500610351562,  -134.9250030517578,            -134.875,\n",
       "               -134.8249969482422, -134.77499389648438, -134.72500610351562,\n",
       "               -134.6750030517578,            -134.625,  -134.5749969482422,\n",
       "              -134.52499389648438,\n",
       "              ...\n",
       "              -15.475000381469727, -15.425000190734863,             -15.375,\n",
       "              -15.324999809265137, -15.274999618530273, -15.225000381469727,\n",
       "              -15.175000190734863,             -15.125, -15.074999809265137,\n",
       "              -15.024999618530273],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=2400))</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-036d740e-1fdb-4424-b101-448a1acb6583' class='xr-index-data-in' type='checkbox'/><label for='index-036d740e-1fdb-4424-b101-448a1acb6583' 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;2022-08-12 01:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, 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><div></div><input id='index-5a0e8026-b2d4-470f-9cfb-4b130f19f4bd' class='xr-index-data-in' type='checkbox'/><label for='index-5a0e8026-b2d4-470f-9cfb-4b130f19f4bd' 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([-59.974998474121094,  -59.92499923706055,             -59.875,\n",
       "               -59.82500076293945, -59.775001525878906, -59.724998474121094,\n",
       "               -59.67499923706055,             -59.625,  -59.57500076293945,\n",
       "              -59.525001525878906,\n",
       "              ...\n",
       "               59.525001525878906,   59.57500076293945,              59.625,\n",
       "                59.67499923706055,  59.724998474121094,  59.775001525878906,\n",
       "                59.82500076293945,              59.875,   59.92499923706055,\n",
       "               59.974998474121094],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=2400))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a4187fa4-6918-48e8-9b42-5e4843529f5f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a4187fa4-6918-48e8-9b42-5e4843529f5f' 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>long_name :</span></dt><dd>sea surface subskin temperature</dd><dt><span>standard_name :</span></dt><dd>sea_surface_subskin_temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-300</dd><dt><span>valid_max :</span></dt><dd>4500</dd><dt><span>depth :</span></dt><dd>1 millimeter</dd><dt><span>source :</span></dt><dd>GOES_Imager</dd><dt><span>comment :</span></dt><dd>Temperature of the subskin of the ocean</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sea_surface_temperature' (time: 1, lat: 2400, lon: 2400)>\n",
       "[5760000 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -135.0 -134.9 -134.9 -134.8 ... -15.12 -15.07 -15.02\n",
       "  * time     (time) datetime64[ns] 2022-08-12T01:00:00\n",
       "  * lat      (lat) float32 -59.97 -59.92 -59.88 -59.83 ... 59.88 59.92 59.97\n",
       "Attributes:\n",
       "    long_name:      sea surface subskin temperature\n",
       "    standard_name:  sea_surface_subskin_temperature\n",
       "    units:          kelvin\n",
       "    valid_min:      -300\n",
       "    valid_max:      4500\n",
       "    depth:          1 millimeter\n",
       "    source:         GOES_Imager\n",
       "    comment:        Temperature of the subskin of the ocean"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "SST = remote_data['sea_surface_temperature'][::1,::1,::1]\n",
    "\n",
    "SST    # Use the DataArray object print representation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f34c4f3f",
   "metadata": {},
   "source": [
    "## Getting useful information a variable\n",
    "\n",
    "With Xarray, the DataArray object returned by remote_data[<em>variable name</em>] can be used to examine information about the variable. There is a bit more information about the variable than is imediately available with the netCDF API. For example, the 'Coordiantes:' information includes the begining and ending values of those arrays. \n",
    "\n",
    "Also included in the DataArray print rep is information about each of the variable's Attributes. This is where important <em>use metadata</em> can often be found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2793d1b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-14T02:32:08.004563Z",
     "start_time": "2023-07-14T02:32:07.994293Z"
    },
    "tags": []
   },
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       "  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",
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       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
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       "\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.DataArray &#x27;sea_surface_temperature&#x27; (time: 1, lat: 2400, lon: 2400)&gt;\n",
       "[5760000 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -135.0 -134.9 -134.9 -134.8 ... -15.12 -15.07 -15.02\n",
       "  * time     (time) datetime64[ns] 2022-08-12T01:00:00\n",
       "  * lat      (lat) float32 -59.97 -59.92 -59.88 -59.83 ... 59.88 59.92 59.97\n",
       "Attributes:\n",
       "    long_name:      sea surface subskin temperature\n",
       "    standard_name:  sea_surface_subskin_temperature\n",
       "    units:          kelvin\n",
       "    valid_min:      -300\n",
       "    valid_max:      4500\n",
       "    depth:          1 millimeter\n",
       "    source:         GOES_Imager\n",
       "    comment:        Temperature of the subskin of the ocean</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>: 1</li><li><span class='xr-has-index'>lat</span>: 2400</li><li><span class='xr-has-index'>lon</span>: 2400</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-53a63e11-9bb7-4b56-8a03-181bc2a0c016' class='xr-array-in' type='checkbox' checked><label for='section-53a63e11-9bb7-4b56-8a03-181bc2a0c016' 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>...</span></div><div class='xr-array-data'><pre>[5760000 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-a6dc195f-940e-4e01-94d5-4a7784cfbc06' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a6dc195f-940e-4e01-94d5-4a7784cfbc06' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-135.0 -134.9 ... -15.07 -15.02</div><input id='attrs-a283a56d-09cc-4ed2-9de1-c65c56c69c65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a283a56d-09cc-4ed2-9de1-c65c56c69c65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff3dbab4-5fb3-4d82-9697-5beeeab1cbe8' class='xr-var-data-in' type='checkbox'><label for='data-ff3dbab4-5fb3-4d82-9697-5beeeab1cbe8' 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</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.00009</dd><dt><span>valid_max :</span></dt><dd>180.00003</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array([-134.975, -134.925, -134.875, ...,  -15.125,  -15.075,  -15.025],\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'>2022-08-12T01:00:00</div><input id='attrs-9ac7b4ac-c114-46df-969e-3ae80d30a630' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ac7b4ac-c114-46df-969e-3ae80d30a630' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03da0f3d-c662-40f7-8f69-45727f0ab85c' class='xr-var-data-in' type='checkbox'><label for='data-03da0f3d-c662-40f7-8f69-45727f0ab85c' 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 sst file</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-08-12T01:00:00.000000000&#x27;], 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'>-59.97 -59.92 ... 59.92 59.97</div><input id='attrs-cb8025df-ba69-42cb-ad32-b8b524b122f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb8025df-ba69-42cb-ad32-b8b524b122f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0bbac40-9e56-41e9-a071-a7ed303cc684' class='xr-var-data-in' type='checkbox'><label for='data-d0bbac40-9e56-41e9-a071-a7ed303cc684' 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</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.000046</dd><dt><span>valid_max :</span></dt><dd>90.000015</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array([-59.975, -59.925, -59.875, ...,  59.875,  59.925,  59.975],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3f162dc1-b753-4859-bf13-069aee234df6' class='xr-section-summary-in' type='checkbox'  ><label for='section-3f162dc1-b753-4859-bf13-069aee234df6' 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>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-686f2b97-c282-4b4f-973e-7d2c52615d0e' class='xr-index-data-in' type='checkbox'/><label for='index-686f2b97-c282-4b4f-973e-7d2c52615d0e' 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([-134.97500610351562,  -134.9250030517578,            -134.875,\n",
       "               -134.8249969482422, -134.77499389648438, -134.72500610351562,\n",
       "               -134.6750030517578,            -134.625,  -134.5749969482422,\n",
       "              -134.52499389648438,\n",
       "              ...\n",
       "              -15.475000381469727, -15.425000190734863,             -15.375,\n",
       "              -15.324999809265137, -15.274999618530273, -15.225000381469727,\n",
       "              -15.175000190734863,             -15.125, -15.074999809265137,\n",
       "              -15.024999618530273],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=2400))</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-185d2727-9666-4bbc-b8d4-6b026f5f7da0' class='xr-index-data-in' type='checkbox'/><label for='index-185d2727-9666-4bbc-b8d4-6b026f5f7da0' 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;2022-08-12 01:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, 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><div></div><input id='index-dcd46506-eace-4235-ac3e-fb7207e37029' class='xr-index-data-in' type='checkbox'/><label for='index-dcd46506-eace-4235-ac3e-fb7207e37029' 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([-59.974998474121094,  -59.92499923706055,             -59.875,\n",
       "               -59.82500076293945, -59.775001525878906, -59.724998474121094,\n",
       "               -59.67499923706055,             -59.625,  -59.57500076293945,\n",
       "              -59.525001525878906,\n",
       "              ...\n",
       "               59.525001525878906,   59.57500076293945,              59.625,\n",
       "                59.67499923706055,  59.724998474121094,  59.775001525878906,\n",
       "                59.82500076293945,              59.875,   59.92499923706055,\n",
       "               59.974998474121094],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=2400))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-57475f38-504b-4c18-a773-29add2d69204' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57475f38-504b-4c18-a773-29add2d69204' 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>long_name :</span></dt><dd>sea surface subskin temperature</dd><dt><span>standard_name :</span></dt><dd>sea_surface_subskin_temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-300</dd><dt><span>valid_max :</span></dt><dd>4500</dd><dt><span>depth :</span></dt><dd>1 millimeter</dd><dt><span>source :</span></dt><dd>GOES_Imager</dd><dt><span>comment :</span></dt><dd>Temperature of the subskin of the ocean</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sea_surface_temperature' (time: 1, lat: 2400, lon: 2400)>\n",
       "[5760000 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -135.0 -134.9 -134.9 -134.8 ... -15.12 -15.07 -15.02\n",
       "  * time     (time) datetime64[ns] 2022-08-12T01:00:00\n",
       "  * lat      (lat) float32 -59.97 -59.92 -59.88 -59.83 ... 59.88 59.92 59.97\n",
       "Attributes:\n",
       "    long_name:      sea surface subskin temperature\n",
       "    standard_name:  sea_surface_subskin_temperature\n",
       "    units:          kelvin\n",
       "    valid_min:      -300\n",
       "    valid_max:      4500\n",
       "    depth:          1 millimeter\n",
       "    source:         GOES_Imager\n",
       "    comment:        Temperature of the subskin of the ocean"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95cba44f",
   "metadata": {},
   "source": [
    "## Accessing data\n",
    "\n",
    "The DataArray object has methods/accessors that return all raw data, attributes, etc. The following cells show some examples. Most important is the `values` accessor that returns a <em>numpy</em> array object that can be used with many other packages. Here is how to access that object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ebeac73d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-14T02:32:10.579948Z",
     "start_time": "2023-07-14T02:32:10.485248Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "        ...,\n",
       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
       "        [   nan,    nan,    nan, ..., 288.28, 288.28, 288.28],\n",
       "        [   nan,    nan,    nan, ..., 286.6 , 286.6 ,    nan]]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9353761b",
   "metadata": {},
   "source": [
    "### Other information in the DataArry object\n",
    "You can access the Attributes, Dimensions and Coordinates using the `attrs`, `dims`, and `coords` accessors. These\n",
    "accessors return the same information as in the special print representation, but using Python data structures that\n",
    "can be passed to Python code.\n",
    "\n",
    "See __[Data Structures](https://docs.xarray.dev/en/stable/user-guide/data-structures.html)__ in teh Xarray\n",
    "documentation for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "faa6a934",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'long_name': 'sea surface subskin temperature',\n",
       " 'standard_name': 'sea_surface_subskin_temperature',\n",
       " 'units': 'kelvin',\n",
       " 'valid_min': -300,\n",
       " 'valid_max': 4500,\n",
       " 'depth': '1 millimeter',\n",
       " 'source': 'GOES_Imager',\n",
       " 'comment': 'Temperature of the subskin of the ocean'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST.attrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be1e0c9e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('time', 'lat', 'lon')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST.dims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2334f839",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * lon      (lon) float32 -135.0 -134.9 -134.9 -134.8 ... -15.12 -15.07 -15.02\n",
       "  * time     (time) datetime64[ns] 2022-08-12T01:00:00\n",
       "  * lat      (lat) float32 -59.97 -59.92 -59.88 -59.83 ... 59.88 59.92 59.97"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST.coords"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acc3f788",
   "metadata": {},
   "source": [
    "## Plotting data\n",
    "\n",
    "Xarray has extensive support for plotting data - much more than we will cover here. However, the simple `plot()` method of the DataArray is very useful. Compare its output below to that of the netCDF4 notebook. The Xarray `plot()` method provides axis labels, color bar, converts the axis labels to the coordinate variables and their values, etc., all automatically.\n",
    "\n",
    "Using `matplotlib` and `cartopy` in conjunction with xarray, these plots can be customized to different projections, include coastlines and more See __[Plotting - Maps](https://docs.xarray.dev/en/stable/user-guide/plotting.html#maps)__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "10a9b9f6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x157e0a510>"
      ]
     },
     "execution_count": 10,
     "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": [
    "SST.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "170d8bb7",
   "metadata": {},
   "source": [
    "## References\n",
    "Xarray documentation, https://docs.xarray.dev/en/stable/index.html, Accessed 2023-06-26<br>\n",
    "Welcome to Pangeo at AOES. Making Maps using Cartopy. https://kpegion.github.io/Pangeo-at-AOES/examples/cartopy-tutorial.html<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f866edd7",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}