notebooks/opendap/MUR-OPeNDAP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OPeNDAP Access\n",
    "\n",
    "#### Notebook Objectives:\n",
    "- Search the common metadata repository (CMR) for the MUR dataset\n",
    "- Obtain OPeNDAP links from CMR search\n",
    "- Download data from OPeNDAP links and open via xarray to visualize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://ghrc.nsstc.nasa.gov/opendap/globalir/data/2020/0525/globir.20146.0000\n",
    "from netCDF4 import Dataset\n",
    "import xarray as xr\n",
    "import dask\n",
    "import os\n",
    "import requests\n",
    "\n",
    "#Allows us to visualize the dask progress for parallel operations\n",
    "from dask.diagnostics import ProgressBar\n",
    "ProgressBar().register()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Before you start\n",
    "\n",
    "Before you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\n",
    "\n",
    "You will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: [Authentication for NASA Earthdata](https://nasa-openscapes.github.io/2021-Cloud-Hackathon/tutorials/04_NASA_Earthdata_Authentication.html#authentication-via-netrc-file) \n",
    "\n",
    "In this notebook, we will be calling the authentication in the below cell, a work around if you do not yet have a netrc file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please provide your Earthdata Login credentials to allow data access\n",
      "Your credentials will only be passed to urs.earthdata.nasa.gov and will not be exposed in Jupyter\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Username: nickles\n",
      " ···········\n"
     ]
    }
   ],
   "source": [
    "import urllib\n",
    "from urllib import request, parse\n",
    "from http.cookiejar import CookieJar\n",
    "import json\n",
    "import getpass\n",
    "import netrc\n",
    "\n",
    "def setup_earthdata_login_auth(endpoint):\n",
    "    \"\"\"\n",
    "    Set up the request library so that it authenticates against the given Earthdata Login\n",
    "    endpoint and is able to track cookies between requests.  This looks in the .netrc file\n",
    "    first and if no credentials are found, it prompts for them.\n",
    "    Valid endpoints include:\n",
    "        urs.earthdata.nasa.gov - Earthdata Login production\n",
    "    \"\"\"\n",
    "    try:\n",
    "        username, _, password = netrc.netrc().authenticators(endpoint)\n",
    "    except (FileNotFoundError, TypeError):\n",
    "        # FileNotFound = There's no .netrc file\n",
    "        # TypeError = The endpoint isn't in the netrc file, causing the above to try unpacking None\n",
    "        print('Please provide your Earthdata Login credentials to allow data access')\n",
    "        print('Your credentials will only be passed to %s and will not be exposed in Jupyter' % (endpoint))\n",
    "        username = input('Username:')\n",
    "        password = getpass.getpass()\n",
    "\n",
    "    \n",
    "    manager = request.HTTPPasswordMgrWithDefaultRealm()\n",
    "    manager.add_password(None, endpoint, username, password)\n",
    "    auth = request.HTTPBasicAuthHandler(manager)\n",
    "\n",
    "    jar = CookieJar()\n",
    "    processor = request.HTTPCookieProcessor(jar)\n",
    "    opener = request.build_opener(auth, processor)\n",
    "    request.install_opener(opener)\n",
    "\n",
    "edl=\"urs.earthdata.nasa.gov\"\n",
    "\n",
    "setup_earthdata_login_auth(edl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CMR Link to use\n",
    "#https://cmr.earthdata.nasa.gov/search/granules.umm_json?collection_concept_id=C1625128926-GHRC_CLOUD&temporal=2019-01-01T10:00:00Z,2019-12-31T23:59:59Z\n",
    "r = requests.get('https://cmr.earthdata.nasa.gov/search/granules.umm_json?collection_concept_id=C1996881146-POCLOUD&temporal=2019-01-01T10:00:00Z,2019-02-01T00:00:00Z&pageSize=365')\n",
    "response_body = r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190131090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n",
       " 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190201090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "od_files = []\n",
    "for itm in response_body['items']:\n",
    "    for urls in itm['umm']['RelatedUrls']:\n",
    "        if 'OPeNDAP' in urls['Description']:\n",
    "            od_files.append(urls['URL'])\n",
    "\n",
    "od_files\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(od_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      " opening https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190101090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
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      " opening https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190122090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      " opening https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190123090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      " opening https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190124090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      " opening https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190125090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      " opening https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190126090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      " opening https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190127090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      " opening https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190128090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      " opening https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190129090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      " opening https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190130090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      " opening https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190131090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      " opening https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190201090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n"
     ]
    }
   ],
   "source": [
    "for f in od_files:\n",
    "    print (\" opening \" + f)\n",
    "    data_url = f'{f}.dap.nc4'\n",
    "    \n",
    "    \n",
    "    # The notation below is [start index, step, end index]\n",
    "    # lat[ /lat= 0..17998] start index. = -90\n",
    "    # lon[ /lon= 0..35999] start index. = -180\n",
    "    # time[ /time= 0..0] \n",
    "    required_variables = {'analysed_sst[0:1:0][000:1:9000][000:1:9000]',\n",
    "                          'analysis_error[0:1:0][000:1:9000][000:1:9000]',\n",
    "                          'lat[000:1:9000]',\n",
    "                          'lon[000:1:9000]',\n",
    "                         'time[0:1:0]'}\n",
    "\n",
    "    #upper latitude, left longitude, lower latitude, right longitude\n",
    "\n",
    "    basename = os.path.basename(data_url)\n",
    "    request_params = {'dap4.ce': ';'.join(required_variables)}\n",
    "    #identity encoding to work around an issue with server side response compression (??)\n",
    "    response = requests.get(data_url, params=request_params,  headers={'Accept-Encoding': 'identity'})\n",
    "\n",
    "    if response.ok:\n",
    "        with open(basename, 'wb') as file_handler:\n",
    "            file_handler.write(response.content)\n",
    "    else:\n",
    "        print(f'Request failed: {response.text}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "cloud_data = xr.open_mfdataset('*.dap.nc4', engine='h5netcdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       ".xr-icon-file-text2 {\n",
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       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (time: 32, lat: 9001, lon: 9001)\n",
       "Coordinates:\n",
       "  * lat             (lat) float32 -89.99 -89.98 -89.97 -89.96 ... -0.01 0.0 0.01\n",
       "  * lon             (lon) float32 -180.0 -180.0 -180.0 ... -90.01 -90.0 -89.99\n",
       "  * time            (time) datetime64[ns] 2019-01-01T09:00:00 ... 2019-02-01T...\n",
       "Data variables:\n",
       "    analysed_sst    (time, lat, lon) float32 dask.array&lt;chunksize=(1, 9001, 9001), meta=np.ndarray&gt;\n",
       "    analysis_error  (time, lat, lon) float32 dask.array&lt;chunksize=(1, 9001, 9001), meta=np.ndarray&gt;\n",
       "Attributes: (12/48)\n",
       "    Conventions:                CF-1.5\n",
       "    title:                      Daily MUR SST, Final product\n",
       "    summary:                    A merged, multi-sensor L4 Foundation SST anal...\n",
       "    references:                 http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n",
       "    institution:                Jet Propulsion Laboratory\n",
       "    history:                    created at nominal 4-day latency; replaced nr...\n",
       "    ...                         ...\n",
       "    publisher_name:             GHRSST Project Office\n",
       "    publisher_url:              http://www.ghrsst.org\n",
       "    publisher_email:            ghrsst-po@nceo.ac.uk\n",
       "    processing_level:           L4\n",
       "    cdm_data_type:              grid\n",
       "    history_json:               [{&quot;$schema&quot;:&quot;https:\\/\\/harmony.earthdata.nasa...</pre><div class='xr-wrap' hidden><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-96eb8ff2-b464-4f03-9e78-eebe7b87ac2e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-96eb8ff2-b464-4f03-9e78-eebe7b87ac2e' 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>: 32</li><li><span class='xr-has-index'>lat</span>: 9001</li><li><span class='xr-has-index'>lon</span>: 9001</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3f7473e6-b519-4bad-b982-7509262a275a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f7473e6-b519-4bad-b982-7509262a275a' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.99 -89.98 -89.97 ... 0.0 0.01</div><input id='attrs-8b9e531a-25c9-44da-9186-c2240f30e87d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b9e531a-25c9-44da-9186-c2240f30e87d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbbb421e-1e5f-4b89-9341-afdd7e59601b' class='xr-var-data-in' type='checkbox'><label for='data-dbbb421e-1e5f-4b89-9341-afdd7e59601b' 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.]</dd><dt><span>valid_max :</span></dt><dd>[90.]</dd><dt><span>comment :</span></dt><dd>none</dd><dt><span>origname :</span></dt><dd>lat</dd><dt><span>fullnamepath :</span></dt><dd>/lat</dd></dl></div><div class='xr-var-data'><pre>array([-8.999e+01, -8.998e+01, -8.997e+01, ..., -1.000e-02,  0.000e+00,\n",
       "        1.000e-02], 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'>-180.0 -180.0 ... -90.0 -89.99</div><input id='attrs-2d74b371-3683-43e6-a811-ea5dcee770bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d74b371-3683-43e6-a811-ea5dcee770bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed876da1-31a9-4932-b172-5c7ce65d7d35' class='xr-var-data-in' type='checkbox'><label for='data-ed876da1-31a9-4932-b172-5c7ce65d7d35' 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.]</dd><dt><span>valid_max :</span></dt><dd>[180.]</dd><dt><span>comment :</span></dt><dd>none</dd><dt><span>origname :</span></dt><dd>lon</dd><dt><span>fullnamepath :</span></dt><dd>/lon</dd></dl></div><div class='xr-var-data'><pre>array([-179.99, -179.98, -179.97, ...,  -90.01,  -90.  ,  -89.99],\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'>2019-01-01T09:00:00 ... 2019-02-...</div><input id='attrs-4e4cb3a2-349c-4d05-851e-79430a2a23c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e4cb3a2-349c-4d05-851e-79430a2a23c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9989ab7a-e1b5-4b27-a7ff-675d8302b1fc' class='xr-var-data-in' type='checkbox'><label for='data-9989ab7a-e1b5-4b27-a7ff-675d8302b1fc' 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 field</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>comment :</span></dt><dd>Nominal time of analyzed fields</dd><dt><span>origname :</span></dt><dd>time</dd><dt><span>fullnamepath :</span></dt><dd>/time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-01-01T09:00:00.000000000&#x27;, &#x27;2019-01-02T09:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-03T09:00:00.000000000&#x27;, &#x27;2019-01-04T09:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-05T09:00:00.000000000&#x27;, &#x27;2019-01-06T09:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-07T09:00:00.000000000&#x27;, &#x27;2019-01-08T09:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-09T09:00:00.000000000&#x27;, &#x27;2019-01-10T09:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-11T09:00:00.000000000&#x27;, &#x27;2019-01-12T09:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-13T09:00:00.000000000&#x27;, &#x27;2019-01-14T09:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-15T09:00:00.000000000&#x27;, &#x27;2019-01-16T09:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-17T09:00:00.000000000&#x27;, &#x27;2019-01-18T09:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-19T09:00:00.000000000&#x27;, &#x27;2019-01-20T09:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-21T09:00:00.000000000&#x27;, &#x27;2019-01-22T09:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-23T09:00:00.000000000&#x27;, &#x27;2019-01-24T09:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-25T09:00:00.000000000&#x27;, &#x27;2019-01-26T09:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-27T09:00:00.000000000&#x27;, &#x27;2019-01-28T09:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-29T09:00:00.000000000&#x27;, &#x27;2019-01-30T09:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-31T09:00:00.000000000&#x27;, &#x27;2019-02-01T09:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c2745d77-4dda-48e7-beb0-6bae2d06956c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c2745d77-4dda-48e7-beb0-6bae2d06956c' class='xr-section-summary' >Data variables: <span>(2)</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>analysed_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'>dask.array&lt;chunksize=(1, 9001, 9001), meta=np.ndarray&gt;</div><input id='attrs-9836bf13-00cc-416e-a93b-24be93b08e9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9836bf13-00cc-416e-a93b-24be93b08e9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b1c2be4-fc60-40a4-842a-53e5b07a4197' class='xr-var-data-in' type='checkbox'><label for='data-7b1c2be4-fc60-40a4-842a-53e5b07a4197' 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>analysed sea surface temperature</dd><dt><span>standard_name :</span></dt><dd>sea_surface_foundation_temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>[-32767]</dd><dt><span>valid_max :</span></dt><dd>[32767]</dd><dt><span>comment :</span></dt><dd>\\&quot;Final\\&quot; version using Multi-Resolution Variational Analysis (MRVA) method for interpolation</dd><dt><span>source :</span></dt><dd>MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRR19_G-NAVO, AVHRRMTA_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF</dd><dt><span>origname :</span></dt><dd>analysed_sst</dd><dt><span>fullnamepath :</span></dt><dd>/analysed_sst</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analysis_error</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 9001, 9001), meta=np.ndarray&gt;</div><input id='attrs-f41d112b-de80-4550-a537-46e61a9952b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f41d112b-de80-4550-a537-46e61a9952b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ce45776-4cde-44db-859c-b9fc8f51dd50' class='xr-var-data-in' type='checkbox'><label for='data-1ce45776-4cde-44db-859c-b9fc8f51dd50' 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>estimated error standard deviation of analysed_sst</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>[0]</dd><dt><span>valid_max :</span></dt><dd>[32767]</dd><dt><span>comment :</span></dt><dd>none</dd><dt><span>origname :</span></dt><dd>analysis_error</dd><dt><span>fullnamepath :</span></dt><dd>/analysis_error</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-5ec32455-3d6e-45e4-92d9-fe318cf5ff3f' class='xr-section-summary-in' type='checkbox'  ><label for='section-5ec32455-3d6e-45e4-92d9-fe318cf5ff3f' 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-1.5</dd><dt><span>title :</span></dt><dd>Daily MUR SST, Final product</dd><dt><span>summary :</span></dt><dd>A merged, multi-sensor L4 Foundation SST analysis product from JPL.</dd><dt><span>references :</span></dt><dd>http://podaac.jpl.nasa.gov/Multi-scale_Ultra-high_Resolution_MUR-SST</dd><dt><span>institution :</span></dt><dd>Jet Propulsion Laboratory</dd><dt><span>history :</span></dt><dd>created at nominal 4-day latency; replaced nrt (1-day latency) version.\n",
       "2022-11-07 18:15:24 GMT hyrax-1.16.8-94 https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190101090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.dap.nc4?dap4.ce=lat%5B000%3A1%3A9000%5D%3Banalysed_sst%5B0%3A1%3A0%5D%5B000%3A1%3A9000%5D%5B000%3A1%3A9000%5D%3Banalysis_error%5B0%3A1%3A0%5D%5B000%3A1%3A9000%5D%5B000%3A1%3A9000%5D%3Btime%5B0%3A1%3A0%5D%3Blon%5B000%3A1%3A9000%5D\n",
       "</dd><dt><span>comment :</span></dt><dd>MUR = \\&quot;Multi-scale Ultra-high Reolution\\&quot;</dd><dt><span>license :</span></dt><dd>These data are available free of charge under data policy of JPL PO.DAAC.</dd><dt><span>id :</span></dt><dd>MUR-JPL-L4-GLOB-v04.1</dd><dt><span>naming_authority :</span></dt><dd>org.ghrsst</dd><dt><span>product_version :</span></dt><dd>04.1</dd><dt><span>uuid :</span></dt><dd>27665bc0-d5fc-11e1-9b23-0800200c9a66</dd><dt><span>gds_version_id :</span></dt><dd>2.0</dd><dt><span>netcdf_version_id :</span></dt><dd>4.1</dd><dt><span>date_created :</span></dt><dd>20190110T004403Z</dd><dt><span>start_time :</span></dt><dd>20190101T090000Z</dd><dt><span>stop_time :</span></dt><dd>20190101T090000Z</dd><dt><span>time_coverage_start :</span></dt><dd>20181231T210000Z</dd><dt><span>time_coverage_end :</span></dt><dd>20190101T210000Z</dd><dt><span>file_quality_level :</span></dt><dd>3</dd><dt><span>source :</span></dt><dd>MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRR19_G-NAVO, AVHRRMTA_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF</dd><dt><span>platform :</span></dt><dd>Terra, Aqua, GCOM-W, NOAA-19, MetOp-A, Buoys/Ships</dd><dt><span>sensor :</span></dt><dd>MODIS, AMSR2, AVHRR, in-situ</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Observation Dataset v1.0</dd><dt><span>metadata_link :</span></dt><dd>http://podaac.jpl.nasa.gov/ws/metadata/dataset/?format=iso&amp;shortName=MUR-JPL-L4-GLOB-v04.1</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>southernmost_latitude :</span></dt><dd>[-90.]</dd><dt><span>northernmost_latitude :</span></dt><dd>[90.]</dd><dt><span>westernmost_longitude :</span></dt><dd>[-180.]</dd><dt><span>easternmost_longitude :</span></dt><dd>[180.]</dd><dt><span>spatial_resolution :</span></dt><dd>0.01 degrees</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees north</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.01 degrees</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.01 degrees</dd><dt><span>acknowledgment :</span></dt><dd>Please acknowledge the use of these data with the following statement:  These data were provided by JPL under support by NASA MEaSUREs program.</dd><dt><span>creator_name :</span></dt><dd>JPL MUR SST project</dd><dt><span>creator_email :</span></dt><dd>ghrsst@podaac.jpl.nasa.gov</dd><dt><span>creator_url :</span></dt><dd>http://mur.jpl.nasa.gov</dd><dt><span>project :</span></dt><dd>NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Program</dd><dt><span>publisher_name :</span></dt><dd>GHRSST Project Office</dd><dt><span>publisher_url :</span></dt><dd>http://www.ghrsst.org</dd><dt><span>publisher_email :</span></dt><dd>ghrsst-po@nceo.ac.uk</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>history_json :</span></dt><dd>[{&quot;$schema&quot;:&quot;https:\\/\\/harmony.earthdata.nasa.gov\\/schemas\\/history\\/0.1.0\\/history-0.1.0.json&quot;,&quot;date_time&quot;:&quot;2022-11-07T18:15:24.699+0000&quot;,&quot;program&quot;:&quot;hyrax&quot;,&quot;version&quot;:&quot;1.16.8-94&quot;,&quot;parameters&quot;:[{&quot;request_url&quot;:&quot;https:\\/\\/opendap.earthdata.nasa.gov\\/providers\\/POCLOUD\\/collections\\/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)\\/granules\\/20190101090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.dap.nc4?dap4.ce=lat%5B000%3A1%3A9000%5D%3Banalysed_sst%5B0%3A1%3A0%5D%5B000%3A1%3A9000%5D%5B000%3A1%3A9000%5D%3Banalysis_error%5B0%3A1%3A0%5D%5B000%3A1%3A9000%5D%5B000%3A1%3A9000%5D%3Btime%5B0%3A1%3A0%5D%3Blon%5B000%3A1%3A9000%5D&quot;},{&quot;decoded_constraint&quot;:&quot;dap4.ce=lat[000:1:9000];analysed_sst[0:1:0][000:1:9000][000:1:9000];analysis_error[0:1:0][000:1:9000][000:1:9000];time[0:1:0];lon[000:1:9000]&quot;}]}]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (time: 32, lat: 9001, lon: 9001)\n",
       "Coordinates:\n",
       "  * lat             (lat) float32 -89.99 -89.98 -89.97 -89.96 ... -0.01 0.0 0.01\n",
       "  * lon             (lon) float32 -180.0 -180.0 -180.0 ... -90.01 -90.0 -89.99\n",
       "  * time            (time) datetime64[ns] 2019-01-01T09:00:00 ... 2019-02-01T...\n",
       "Data variables:\n",
       "    analysed_sst    (time, lat, lon) float32 dask.array<chunksize=(1, 9001, 9001), meta=np.ndarray>\n",
       "    analysis_error  (time, lat, lon) float32 dask.array<chunksize=(1, 9001, 9001), meta=np.ndarray>\n",
       "Attributes: (12/48)\n",
       "    Conventions:                CF-1.5\n",
       "    title:                      Daily MUR SST, Final product\n",
       "    summary:                    A merged, multi-sensor L4 Foundation SST anal...\n",
       "    references:                 http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n",
       "    institution:                Jet Propulsion Laboratory\n",
       "    history:                    created at nominal 4-day latency; replaced nr...\n",
       "    ...                         ...\n",
       "    publisher_name:             GHRSST Project Office\n",
       "    publisher_url:              http://www.ghrsst.org\n",
       "    publisher_email:            ghrsst-po@nceo.ac.uk\n",
       "    processing_level:           L4\n",
       "    cdm_data_type:              grid\n",
       "    history_json:               [{\"$schema\":\"https:\\/\\/harmony.earthdata.nasa..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cloud_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 29.9s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([3.66360932e+08, 1.41839843e+08, 1.33124088e+08, 1.42820817e+08,\n",
       "        1.34985851e+08, 1.21022644e+08, 1.70274605e+08, 3.11394382e+08,\n",
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       " array([271.35   , 274.6785 , 278.007  , 281.3355 , 284.664  , 287.9925 ,\n",
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       "       dtype=float32),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram\n",
    "cloud_data['analysed_sst'].plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  1.8s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1ede51eca30>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose one time segment, plot the data\n",
    "cloud_data['analysed_sst'].isel(time=4).plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  0.3s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ede66ec760>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a single point over time\n",
    "cloud_data['analysed_sst'].isel(lat=7000, lon=7000).plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}