tutorials/09_Zarr_Access.ipynb

{
 "cells": [
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   },
   "source": [
    "# 09. Zarr Access for NetCDF4 files\n",
    "\n",
    "## Timing:\n",
    "- Exercise: 45 minutes\n",
    "\n",
    "## Summary\n",
    "\n",
    "Zarr is an open source library for storing N-dimensional array data.  It supports multidimensional arrays with attributes and dimensions similar to NetCDF4, and it can be read by XArray.  Zarr is often used for data held in cloud object storage (like Amazon S3), because it is better optimized for these situations than NetCDF4.\n",
    "\n",
    "The [zarr-eosdis-store library](https://github.com/nasa/zarr-eosdis-store) allows NASA EOSDIS NetCDF4 files to be read more efficiently by transferring only file metadata and data needed for computation in a small number of requests, rather than moving the whole file or making many small requests.  It works by making the files directly readable by the [Zarr Python library](https://zarr.readthedocs.io) and XArray across a network.  To use it, files must have a corresponding metadata file ending in `.dmrpp`, which increasingly true for cloud-accessible EOSDIS data.  https://github.com/nasa/zarr-eosdis-store\n",
    "\n",
    "The zarr-eosdis-store library provides several benefits over downloading EOSDIS data files and accessing them using XArray, NetCDF4, or HDF5 Python libraries:\n",
    "\n",
    "* It only downloads the chunks of data you actually read, so if you don't read all variables or the full spatiotemporal extent of a file, you usually won't spend time downloading those portions of the file\n",
    "* It parallelizes and optimizes downloads for the portions of files you do read, so download speeds can be faster in general\n",
    "* It automatically interoperates with Earthdata Login if you have a .netrc file set up\n",
    "* It is aware of some EOSDIS cloud implementation quirks and provides caching that can save time for repeated requests to individual files\n",
    "\n",
    "It can also be faster than using XArray pointing NetCDF4 files with s3:// URLs, depending on the file's internal structure, and is often more convenient.\n",
    "\n",
    "Consider using this library when:\n",
    "1. The portion of the data file you need to use is much smaller than the full file, e.g. in cases of spatial subsets or reading a single variable from a file containing several\n",
    "1. s3:// URLs are not readily available\n",
    "1. Code need to run outside of the AWS cloud or us-west-2 region or in a hybrid cloud / non-cloud manner\n",
    "1. s3:// access using XArray seems slower than you would expect (possibly due to unoptimized internal file structure)\n",
    "1. No readily-available, public, cloud-optimized version of the data exists already. The example we show _is_ also available as an AWS Public Dataset: https://registry.opendata.aws/mur/\n",
    "1. Adding \".dmrpp\" to the end of a data URL returns a file\n",
    "\n",
    "### Objectives\n",
    "\n",
    "1. Build on prior knowledge from CMR and Earthdata Login tutorials\n",
    "2. Work through an example of using the EOSDIS Zarr Store to access data using XArray\n",
    "3. Learn about the Zarr format and library for accessing data in the cloud\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f18162e3-708d-4d9a-8d77-9b091cef08ac",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Exercise\n",
    "\n",
    "In this exercise, we will be using the eosdis-zarr-store library to aggregate and analyze a month of sea surface temperature for the Great Lakes region\n",
    "\n",
    "### Set up\n",
    "\n",
    "#### Import Required Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64eb592d-aa56-4c1c-a2c3-4ec1bba504dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Core libraries for this tutorial\n",
    "# Available via `pip install zarr zarr-eosdis-store`\n",
    "from eosdis_store import EosdisStore\n",
    "import xarray as xr\n",
    "\n",
    "# Other Python libraries\n",
    "import requests\n",
    "from pqdm.threads import pqdm\n",
    "from matplotlib import animation, pyplot as plt\n",
    "from IPython.core.display import display, HTML\n",
    "\n",
    "# Python standard library imports\n",
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff8e6fac-bb2f-4bba-b1b7-cd22b7b22136",
   "metadata": {},
   "source": [
    "Also set the width / height for plots we show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "203077c2-848b-4728-bf40-fd28e1c2f79f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = 12, 6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49e9ac2d-a300-4e28-a947-9dff9637630c",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Set Dataset, Time, and Region of Interest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d81f3f6-62db-42bb-a296-bbcd16982d16",
   "metadata": {},
   "source": [
    "Look in PO.DAAC's cloud archive for Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 Multiscale Ultrahigh Resolution (MUR) data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "738230cd-e952-4c07-9939-de09f10069b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_provider = 'POCLOUD'\n",
    "mur_short_name = 'MUR-JPL-L4-GLOB-v4.1'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a34bd47b-1adf-4b27-aec1-40d671eabcbb",
   "metadata": {},
   "source": [
    "Looking for data from the month of September over the Great Lakes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b27625f-86a3-4ed4-a060-300f74ef9a08",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = '2021-09-01T21:00:00Z'\n",
    "end_time = '2021-09-30T20:59:59Z'\n",
    "\n",
    "# Bounding box around the Great Lakes\n",
    "lats = slice(41, 49)\n",
    "lons = slice(-93, -76)\n",
    "\n",
    "# Some other possibly interesting bounding boxes:\n",
    "\n",
    "# Hawaiian Islands\n",
    "# lats = slice(18, 22.5)\n",
    "# lons = slice(-161, -154)\n",
    "\n",
    "# Mediterranean Sea\n",
    "# lats = slice(29, 45)\n",
    "# lons = slice(-7, 37)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6989068a-9092-4921-a317-f50f50cd37c0",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Find URLs for the dataset and AOI\n",
    "\n",
    "Set up a CMR granules search for our area of interest, as we saw in prior tutorials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c4db3e80-bddc-4d02-8ddf-a77deca40d1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmr_url = 'https://cmr.earthdata.nasa.gov/search/granules.json'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9908098b-b337-4355-b4a8-d702c422004d",
   "metadata": {},
   "source": [
    "Search for granules in our area of interest, expecting one granule per day of September"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "841f20f4-9b85-49fa-b2d2-b0c50cc9e603",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.get(cmr_url, \n",
    "                        params={\n",
    "                            'provider': data_provider,\n",
    "                            'short_name': mur_short_name, \n",
    "                            'temporal': f'{start_time},{end_time}',\n",
    "                            'bounding_box': f'{lons.start},{lats.start},{lons.stop},{lats.stop}',\n",
    "                            'page_size': 2000,\n",
    "                            }\n",
    "                       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bd10cf4a-9e7a-4b3c-bccd-ca7c4773a6db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210902090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210903090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210904090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210905090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210906090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210907090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210908090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210909090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210910090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210911090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210912090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210913090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210914090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210915090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210916090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210917090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210918090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210919090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210920090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210921090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210922090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210923090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210924090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210925090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210926090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210927090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210928090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210929090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n",
      "20210930090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n"
     ]
    }
   ],
   "source": [
    "granules = response.json()['feed']['entry']\n",
    "\n",
    "for granule in granules:\n",
    "    print(granule['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dc0f2fd8-8e19-45d4-981e-6d533d826278",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'boxes': ['-90 -180 90 180'],\n",
      " 'browse_flag': False,\n",
      " 'collection_concept_id': 'C1996881146-POCLOUD',\n",
      " 'coordinate_system': 'CARTESIAN',\n",
      " 'data_center': 'POCLOUD',\n",
      " 'dataset_id': 'GHRSST Level 4 MUR Global Foundation Sea Surface Temperature '\n",
      "               'Analysis (v4.1)',\n",
      " 'day_night_flag': 'UNSPECIFIED',\n",
      " 'granule_size': '9.059906005859375E-5',\n",
      " 'id': 'G2113241213-POCLOUD',\n",
      " 'links': [{'href': 's3://podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      "            'hreflang': 'en-US',\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/s3#',\n",
      "            'title': 'This link provides direct download access via S3 to the '\n",
      "                     'granule.'},\n",
      "           {'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      "            'hreflang': 'en-US',\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n",
      "            'title': 'Download '\n",
      "                     '20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'},\n",
      "           {'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MUR-JPL-L4-GLOB-v4.1/20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.md5',\n",
      "            'hreflang': 'en-US',\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n",
      "            'title': 'Download '\n",
      "                     '20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.md5'},\n",
      "           {'href': 'https://archive.podaac.earthdata.nasa.gov/s3credentials',\n",
      "            'hreflang': 'en-US',\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n",
      "            'title': 'api endpoint to retrieve temporary credentials valid for '\n",
      "                     'same-region direct s3 access'},\n",
      "           {'href': 'https://opendap.earthdata.nasa.gov/collections/C1996881146-POCLOUD/granules/20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n",
      "            'hreflang': 'en-US',\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/service#',\n",
      "            'title': 'OPeNDAP request URL'},\n",
      "           {'href': 'https://github.com/nasa/podaac_tools_and_services/tree/master/subset_opendap',\n",
      "            'hreflang': 'en-US',\n",
      "            'inherited': True,\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n",
      "           {'href': 'https://ghrsst.jpl.nasa.gov',\n",
      "            'hreflang': 'en-US',\n",
      "            'inherited': True,\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n",
      "           {'href': 'https://earthdata.nasa.gov/esds/competitive-programs/measures/mur-sst',\n",
      "            'hreflang': 'en-US',\n",
      "            'inherited': True,\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n",
      "           {'href': 'http://journals.ametsoc.org/doi/abs/10.1175/1520-0426%281998%29015%3C0741:BSHWSS%3E2.0.CO;2',\n",
      "            'hreflang': 'en-US',\n",
      "            'inherited': True,\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n",
      "           {'href': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n",
      "            'hreflang': 'en-US',\n",
      "            'inherited': True,\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n",
      "           {'href': 'https://github.com/podaac/data-readers',\n",
      "            'hreflang': 'en-US',\n",
      "            'inherited': True,\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n",
      "           {'href': 'https://doi.org/10.1016/j.rse.2017.07.029',\n",
      "            'hreflang': 'en-US',\n",
      "            'inherited': True,\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n",
      "           {'href': 'https://registry.opendata.aws/mur/#usageexa',\n",
      "            'hreflang': 'en-US',\n",
      "            'inherited': True,\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n",
      "           {'href': 'http://www.ghrsst.org',\n",
      "            'hreflang': 'en-US',\n",
      "            'inherited': True,\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n",
      "           {'href': 'https://podaac.jpl.nasa.gov/CitingPODAAC',\n",
      "            'hreflang': 'en-US',\n",
      "            'inherited': True,\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n",
      "           {'href': 'https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1996881146-POCLOUD ',\n",
      "            'hreflang': 'en-US',\n",
      "            'inherited': True,\n",
      "            'length': '300.0MB',\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n",
      "           {'href': ' '\n",
      "                    'https://search.earthdata.nasa.gov/search/granules?p=C1996881146-POCLOUD ',\n",
      "            'hreflang': 'en-US',\n",
      "            'inherited': True,\n",
      "            'length': '700.0MB',\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n",
      "           {'href': 'https://podaac.jpl.nasa.gov/MEaSUREs-MUR',\n",
      "            'hreflang': 'en-US',\n",
      "            'inherited': True,\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n",
      "           {'href': 'https://github.com/podaac/tutorials/blob/master/notebooks/SWOT-EA-2021/Colocate_satellite_insitu_ocean.ipynb',\n",
      "            'hreflang': 'en-US',\n",
      "            'inherited': True,\n",
      "            'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'}],\n",
      " 'online_access_flag': True,\n",
      " 'original_format': 'UMM_JSON',\n",
      " 'time_end': '2021-09-01T21:00:00.000Z',\n",
      " 'time_start': '2021-08-31T21:00:00.000Z',\n",
      " 'title': '20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n",
      " 'updated': '2021-09-10T07:29:40.511Z'}\n"
     ]
    }
   ],
   "source": [
    "pprint(granules[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "532571dc-ce64-4e4a-9668-61607f15a9bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210902090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210903090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210904090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210905090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210906090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210907090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210908090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210909090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210910090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210911090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210912090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210913090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210914090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210915090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210916090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210917090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
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      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210920090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210921090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210922090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210923090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210924090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210925090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210926090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210927090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210928090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210929090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n",
      " 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210930090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc']\n"
     ]
    }
   ],
   "source": [
    "urls = []\n",
    "for granule in granules:\n",
    "    for link in granule['links']:\n",
    "        if link['rel'].endswith('/data#'):\n",
    "            urls.append(link['href'])\n",
    "            break\n",
    "pprint(urls)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23aaff9f-aa7c-47e4-b230-d734f316481f",
   "metadata": {},
   "source": [
    "### Open and view our AOI without downloading a whole file\n",
    "\n",
    "#### Check to see if we can use an efficient partial-access technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d0ed36a6-cbb7-47ae-a8fc-302b3a6e2c89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Can we use EosdisZarrStore and XArray to access these files more efficiently?\n",
      "Yes\n"
     ]
    }
   ],
   "source": [
    "response = requests.head(f'{urls[0]}.dmrpp')\n",
    "\n",
    "print('Can we use EosdisZarrStore and XArray to access these files more efficiently?')\n",
    "print('Yes' if response.ok else 'No')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d90dec8-c631-463f-be77-6f74066139f0",
   "metadata": {},
   "source": [
    "Open our first URL using the Zarr library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "315e249e-c92b-44e1-ad3d-507647bb9126",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = urls[0]\n",
    "\n",
    "ds = xr.open_zarr(EosdisStore(url), consolidated=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd164108-0e43-4a20-bc94-6946f5ea8f2d",
   "metadata": {},
   "source": [
    "That's it!  No downloads, temporary credentials, or S3 filesystems.  Hereafter, we interact with the `ds` variable as with any XArray dataset.  We need not worry about the EosdisStore anymore."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d1d5866-c132-490a-bca7-b0ac5457ff4f",
   "metadata": {},
   "source": [
    "View the file's variable structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "939cc4fc-ae03-47b1-afc2-56672114462f",
   "metadata": {},
   "outputs": [
    {
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       "\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",
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       "  vertical-align: middle;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:           (time: 1, lat: 17999, lon: 36000)\n",
       "Coordinates:\n",
       "  * lat               (lat) float32 -89.99 -89.98 -89.97 ... 89.97 89.98 89.99\n",
       "  * lon               (lon) float32 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n",
       "  * time              (time) datetime64[ns] 2021-09-01T09:00:00\n",
       "Data variables:\n",
       "    analysed_sst      (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1023, 2047), meta=np.ndarray&gt;\n",
       "    analysis_error    (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1023, 2047), meta=np.ndarray&gt;\n",
       "    dt_1km_data       (time, lat, lon) timedelta64[ns] dask.array&lt;chunksize=(1, 1447, 2895), meta=np.ndarray&gt;\n",
       "    mask              (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1447, 2895), meta=np.ndarray&gt;\n",
       "    sea_ice_fraction  (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1447, 2895), meta=np.ndarray&gt;\n",
       "    sst_anomaly       (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1023, 2047), meta=np.ndarray&gt;\n",
       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.7\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",
       "    project:                    NASA Making Earth Science Data Records for Us...\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</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-545db239-24ab-402f-b69a-1995c491cb32' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-545db239-24ab-402f-b69a-1995c491cb32' 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>: 17999</li><li><span class='xr-has-index'>lon</span>: 36000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-73815837-fa19-4ab9-badb-1afd35a70cbd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-73815837-fa19-4ab9-badb-1afd35a70cbd' 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 ... 89.98 89.99</div><input id='attrs-8dc86c0a-b7e5-4338-8547-84a0d857b71e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8dc86c0a-b7e5-4338-8547-84a0d857b71e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa00bb80-c177-4495-9743-034afa9bfafd' class='xr-var-data-in' type='checkbox'><label for='data-aa00bb80-c177-4495-9743-034afa9bfafd' 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>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div><div class='xr-var-data'><pre>array([-89.99, -89.98, -89.97, ...,  89.97,  89.98,  89.99], 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 ... 180.0 180.0</div><input id='attrs-3e9938f7-5118-42e3-bf8f-fd6f0bec4bc1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e9938f7-5118-42e3-bf8f-fd6f0bec4bc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-660129fe-0314-4062-bcdd-54e5b9ea1d44' class='xr-var-data-in' type='checkbox'><label for='data-660129fe-0314-4062-bcdd-54e5b9ea1d44' 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>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div><div class='xr-var-data'><pre>array([-179.99, -179.98, -179.97, ...,  179.98,  179.99,  180.  ],\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'>2021-09-01T09:00:00</div><input id='attrs-e396c743-6e2e-45c7-91c6-6375b0013229' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e396c743-6e2e-45c7-91c6-6375b0013229' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8e99d76-b067-4427-a3d5-faa99d79da45' class='xr-var-data-in' type='checkbox'><label for='data-b8e99d76-b067-4427-a3d5-faa99d79da45' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-09-01T09:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9923303e-8ba7-443c-bf57-6f8acbf4eaec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9923303e-8ba7-443c-bf57-6f8acbf4eaec' class='xr-section-summary' >Data variables: <span>(6)</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, 1023, 2047), meta=np.ndarray&gt;</div><input id='attrs-26fff6c9-a59b-42e4-9467-dd8544a389e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26fff6c9-a59b-42e4-9467-dd8544a389e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78b785c1-d470-43be-acd2-2810f7d1b67f' class='xr-var-data-in' type='checkbox'><label for='data-78b785c1-d470-43be-acd2-2810f7d1b67f' 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, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.41 GiB </td>\n",
       "                        <td> 7.99 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1, 17999, 36000) </td>\n",
       "                        <td> (1, 1023, 2047) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 325 Tasks </td>\n",
       "                        <td> 324 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\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, 1023, 2047), meta=np.ndarray&gt;</div><input id='attrs-c9fca5df-181c-45eb-a679-3bd2894c9980' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9fca5df-181c-45eb-a679-3bd2894c9980' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4320eeaa-4fa7-4347-9d2f-a016d2da7cc5' class='xr-var-data-in' type='checkbox'><label for='data-4320eeaa-4fa7-4347-9d2f-a016d2da7cc5' 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>uncertainty in \\&quot;analysed_sst\\&quot;</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
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       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.41 GiB </td>\n",
       "                        <td> 7.99 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (1, 1023, 2047) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 325 Tasks </td>\n",
       "                        <td> 324 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dt_1km_data</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'>dask.array&lt;chunksize=(1, 1447, 2895), meta=np.ndarray&gt;</div><input id='attrs-39909e02-f412-42cf-87c0-8c065cdb6450' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39909e02-f412-42cf-87c0-8c065cdb6450' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23c8af46-075d-4da4-a8a9-3de18822556b' class='xr-var-data-in' type='checkbox'><label for='data-23c8af46-075d-4da4-a8a9-3de18822556b' 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 to most recent 1km data</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>source :</span></dt><dd>MODIS and VIIRS pixels ingested by MUR</dd><dt><span>comment :</span></dt><dd>The grid value is hours between the analysis time and the most recent MODIS or VIIRS 1km L2P datum within 0.01 degrees from the grid point.  \\&quot;Fill value\\&quot; indicates absence of such 1km data at the grid point.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.83 GiB </td>\n",
       "                        <td> 31.96 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1, 17999, 36000) </td>\n",
       "                        <td> (1, 1447, 2895) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 170 Tasks </td>\n",
       "                        <td> 169 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> timedelta64[ns] </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</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, 1447, 2895), meta=np.ndarray&gt;</div><input id='attrs-941ebd9e-5a98-4ab0-8bb5-e38e5c2c8f72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-941ebd9e-5a98-4ab0-8bb5-e38e5c2c8f72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2b430d3-c31a-45f1-b73c-8a96018af3ff' class='xr-var-data-in' type='checkbox'><label for='data-c2b430d3-c31a-45f1-b73c-8a96018af3ff' 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/land field composite mask</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>31</dd><dt><span>flag_masks :</span></dt><dd>[1, 2, 4, 8, 16]</dd><dt><span>flag_meanings :</span></dt><dd>open_sea land open_lake open_sea_with_ice_in_the_grid open_lake_with_ice_in_the_grid</dd><dt><span>comment :</span></dt><dd>mask can be used to further filter the data.</dd><dt><span>source :</span></dt><dd>GMT \\&quot;grdlandmask\\&quot;, ice flag from sea_ice_fraction data</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
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       "                        <td> </td>\n",
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       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.41 GiB </td>\n",
       "                        <td> 15.98 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1, 17999, 36000) </td>\n",
       "                        <td> (1, 1447, 2895) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 170 Tasks </td>\n",
       "                        <td> 169 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "</table></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'>dask.array&lt;chunksize=(1, 1447, 2895), meta=np.ndarray&gt;</div><input id='attrs-4dd94284-7af7-4570-bdf8-3e2241243573' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4dd94284-7af7-4570-bdf8-3e2241243573' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2c665bb-738e-4b0e-bb85-9256464c7259' class='xr-var-data-in' type='checkbox'><label for='data-c2c665bb-738e-4b0e-bb85-9256464c7259' 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 area fraction</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>source :</span></dt><dd>EUMETSAT OSI-SAF, copyright EUMETSAT</dd><dt><span>comment :</span></dt><dd>ice fraction is a dimensionless quantity between 0 and 1; it has been interpolated by a nearest neighbor approach.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "<xarray.Dataset>\n",
       "Dimensions:           (time: 1, lat: 17999, lon: 36000)\n",
       "Coordinates:\n",
       "  * lat               (lat) float32 -89.99 -89.98 -89.97 ... 89.97 89.98 89.99\n",
       "  * lon               (lon) float32 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n",
       "  * time              (time) datetime64[ns] 2021-09-01T09:00:00\n",
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       "    analysed_sst      (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n",
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       "    mask              (time, lat, lon) float32 dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n",
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       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.7\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",
       "    project:                    NASA Making Earth Science Data Records for Us...\n",
       "    publisher_name:             GHRSST Project Office\n",
       "    publisher_url:              http://www.ghrsst.org\n",
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       "    processing_level:           L4\n",
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     "execution_count": 12,
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;analysed_sst&#x27; (time: 1, lat: 17999, lon: 36000)&gt;\n",
       "dask.array&lt;open_dataset-4d5a9a1e1fda090e80524b67b2e413c6analysed_sst, shape=(1, 17999, 36000), dtype=float32, chunksize=(1, 1023, 2047), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -89.99 -89.98 -89.97 -89.96 ... 89.97 89.98 89.99\n",
       "  * lon      (lon) float32 -180.0 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n",
       "  * time     (time) datetime64[ns] 2021-09-01T09:00:00\n",
       "Attributes:\n",
       "    long_name:      analysed sea surface temperature\n",
       "    standard_name:  sea_surface_foundation_temperature\n",
       "    units:          kelvin\n",
       "    valid_min:      -32767\n",
       "    valid_max:      32767\n",
       "    comment:        \\&quot;Final\\&quot; version using Multi-Resolution Variational Anal...\n",
       "    source:         MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'analysed_sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>lat</span>: 17999</li><li><span class='xr-has-index'>lon</span>: 36000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ec7fdd62-eb58-4787-8dcf-e4e9a1bd40a0' class='xr-array-in' type='checkbox' checked><label for='section-ec7fdd62-eb58-4787-8dcf-e4e9a1bd40a0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1023, 2047), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.41 GiB </td>\n",
       "                        <td> 7.99 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1, 17999, 36000) </td>\n",
       "                        <td> (1, 1023, 2047) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 325 Tasks </td>\n",
       "                        <td> 324 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-10d859d1-2738-447e-a443-0c4fddbc8ab0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-10d859d1-2738-447e-a443-0c4fddbc8ab0' 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 ... 89.98 89.99</div><input id='attrs-e72cd62c-a84c-414a-88ba-eced60f8fdf1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e72cd62c-a84c-414a-88ba-eced60f8fdf1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49a8774e-1478-4c23-8e2d-8b2c6c14bf5e' class='xr-var-data-in' type='checkbox'><label for='data-49a8774e-1478-4c23-8e2d-8b2c6c14bf5e' 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>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div><div class='xr-var-data'><pre>array([-89.99, -89.98, -89.97, ...,  89.97,  89.98,  89.99], 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 ... 180.0 180.0</div><input id='attrs-7320d3d5-cb32-4ee7-abed-16629c2c557e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7320d3d5-cb32-4ee7-abed-16629c2c557e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4a4421e-9743-4111-af76-3052269a950b' class='xr-var-data-in' type='checkbox'><label for='data-c4a4421e-9743-4111-af76-3052269a950b' 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>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div><div class='xr-var-data'><pre>array([-179.99, -179.98, -179.97, ...,  179.98,  179.99,  180.  ],\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'>2021-09-01T09:00:00</div><input id='attrs-c6e42458-9db2-4f44-80ff-d1663a9470ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6e42458-9db2-4f44-80ff-d1663a9470ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9be12ff1-7434-464a-a0bd-865258835bb7' class='xr-var-data-in' type='checkbox'><label for='data-9be12ff1-7434-464a-a0bd-865258835bb7' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-09-01T09:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aad375da-dc41-4a39-8afc-3024ee60308a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aad375da-dc41-4a39-8afc-3024ee60308a' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>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, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'analysed_sst' (time: 1, lat: 17999, lon: 36000)>\n",
       "dask.array<open_dataset-4d5a9a1e1fda090e80524b67b2e413c6analysed_sst, shape=(1, 17999, 36000), dtype=float32, chunksize=(1, 1023, 2047), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
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       "  * time     (time) datetime64[ns] 2021-09-01T09:00:00\n",
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       "    units:          kelvin\n",
       "    valid_min:      -32767\n",
       "    valid_max:      32767\n",
       "    comment:        \\\"Final\\\" version using Multi-Resolution Variational Anal...\n",
       "    source:         MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A..."
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     },
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     "metadata": {},
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;analysed_sst&#x27; (time: 1, lat: 801, lon: 1701)&gt;\n",
       "dask.array&lt;getitem, shape=(1, 801, 1701), dtype=float32, chunksize=(1, 601, 1536), chunktype=numpy.ndarray&gt;\n",
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       "    standard_name:  sea_surface_foundation_temperature\n",
       "    units:          kelvin\n",
       "    valid_min:      -32767\n",
       "    valid_max:      32767\n",
       "    comment:        \\&quot;Final\\&quot; version using Multi-Resolution Variational Anal...\n",
       "    source:         MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'analysed_sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>lat</span>: 801</li><li><span class='xr-has-index'>lon</span>: 1701</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-878bbc25-4acf-4a19-80e6-991096c2f4a0' class='xr-array-in' type='checkbox' checked><label for='section-878bbc25-4acf-4a19-80e6-991096c2f4a0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 200, 1536), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 5.20 MiB </td>\n",
       "                        <td> 3.52 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1, 801, 1701) </td>\n",
       "                        <td> (1, 601, 1536) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 329 Tasks </td>\n",
       "                        <td> 4 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-6cde2ae4-6a0c-49a7-955d-73cc441d2cf4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6cde2ae4-6a0c-49a7-955d-73cc441d2cf4' 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'>41.0 41.01 41.02 ... 48.99 49.0</div><input id='attrs-924fc360-dbd4-4e5e-bae0-c6b7370dbc31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-924fc360-dbd4-4e5e-bae0-c6b7370dbc31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7881cbb4-c10e-415f-94b6-5713f2135c2f' class='xr-var-data-in' type='checkbox'><label for='data-7881cbb4-c10e-415f-94b6-5713f2135c2f' 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>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div><div class='xr-var-data'><pre>array([41.  , 41.01, 41.02, ..., 48.98, 48.99, 49.  ], 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'>-93.0 -92.99 ... -76.01 -76.0</div><input id='attrs-44916ef4-bc0e-4079-90f6-de41ca2c39d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44916ef4-bc0e-4079-90f6-de41ca2c39d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd6bd194-9ae5-407e-8d6a-2eba1082fea4' class='xr-var-data-in' type='checkbox'><label for='data-fd6bd194-9ae5-407e-8d6a-2eba1082fea4' 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>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div><div class='xr-var-data'><pre>array([-93.  , -92.99, -92.98, ..., -76.02, -76.01, -76.  ], 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'>2021-09-01T09:00:00</div><input id='attrs-53f18138-9ba1-4355-9d37-d387c911c897' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53f18138-9ba1-4355-9d37-d387c911c897' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88497d8b-9688-4070-8fc9-b9cca7131643' class='xr-var-data-in' type='checkbox'><label for='data-88497d8b-9688-4070-8fc9-b9cca7131643' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-09-01T09:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-60a8b1a6-7ec6-4a07-8cb4-867b7b7bdb81' class='xr-section-summary-in' type='checkbox'  checked><label for='section-60a8b1a6-7ec6-4a07-8cb4-867b7b7bdb81' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>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, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'analysed_sst' (time: 1, lat: 801, lon: 1701)>\n",
       "dask.array<getitem, shape=(1, 801, 1701), dtype=float32, chunksize=(1, 601, 1536), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 41.0 41.01 41.02 41.03 ... 48.97 48.98 48.99 49.0\n",
       "  * lon      (lon) float32 -93.0 -92.99 -92.98 -92.97 ... -76.02 -76.01 -76.0\n",
       "  * time     (time) datetime64[ns] 2021-09-01T09:00:00\n",
       "Attributes:\n",
       "    long_name:      analysed sea surface temperature\n",
       "    standard_name:  sea_surface_foundation_temperature\n",
       "    units:          kelvin\n",
       "    valid_min:      -32767\n",
       "    valid_max:      32767\n",
       "    comment:        \\\"Final\\\" version using Multi-Resolution Variational Anal...\n",
       "    source:         MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst = ds.analysed_sst.sel(lat=lats, lon=lons)\n",
    "sst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cddd1498-74fd-401b-a6bd-a569d29166b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f2d9848d4c0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sst.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81954fb1-e01f-4fb4-9fe6-3fa07c46646c",
   "metadata": {},
   "source": [
    "### Aggregate and analyze 30 files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b10c27c6-dd62-4598-8f37-4dc0dda20a0f",
   "metadata": {},
   "source": [
    "Set up a function to open all of our URLs as XArrays in parallel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4a219beb-5565-49f1-a6a6-46186c0b342a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b7c4b58f73646f0b73be3acfdb13fb1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "SUBMITTING | :   0%|          | 0/30 [00:00<?, ?it/s]"
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   "source": [
    "def open_as_zarr_xarray(url):\n",
    "    return xr.open_zarr(EosdisStore(url), consolidated=False)\n",
    "\n",
    "datasets = pqdm(urls, open_as_zarr_xarray, n_jobs=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe6754a8-f882-4331-a662-e27857057ecf",
   "metadata": {},
   "source": [
    "Combine the individual file-based datasets into a single xarray dataset with a time axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "45c2d668-b118-4c09-9845-6169534a2769",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:           (time: 30, lat: 17999, lon: 36000)\n",
       "Coordinates:\n",
       "  * lat               (lat) float32 -89.99 -89.98 -89.97 ... 89.97 89.98 89.99\n",
       "  * lon               (lon) float32 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n",
       "  * time              (time) datetime64[ns] 2021-09-01T09:00:00 ... 2021-09-3...\n",
       "Data variables:\n",
       "    analysed_sst      (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1023, 2047), meta=np.ndarray&gt;\n",
       "    analysis_error    (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1023, 2047), meta=np.ndarray&gt;\n",
       "    dt_1km_data       (time, lat, lon) timedelta64[ns] dask.array&lt;chunksize=(1, 1447, 2895), meta=np.ndarray&gt;\n",
       "    mask              (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1447, 2895), meta=np.ndarray&gt;\n",
       "    sea_ice_fraction  (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1447, 2895), meta=np.ndarray&gt;\n",
       "    sst_anomaly       (time, lat, lon) float32 dask.array&lt;chunksize=(1, 1023, 2047), meta=np.ndarray&gt;\n",
       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.7\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",
       "    project:                    NASA Making Earth Science Data Records for Us...\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</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-f6f5a800-660e-4c41-be6b-f47879ddbd8a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f6f5a800-660e-4c41-be6b-f47879ddbd8a' 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>: 30</li><li><span class='xr-has-index'>lat</span>: 17999</li><li><span class='xr-has-index'>lon</span>: 36000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5c5e5d21-c7fc-463b-9bec-dbbe1878be25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c5e5d21-c7fc-463b-9bec-dbbe1878be25' 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 ... 89.98 89.99</div><input id='attrs-1c5edb5a-295e-4632-ba8a-f765940a8cfd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c5edb5a-295e-4632-ba8a-f765940a8cfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b37e84c0-d4a5-446c-9f0b-4ed810c34384' class='xr-var-data-in' type='checkbox'><label for='data-b37e84c0-d4a5-446c-9f0b-4ed810c34384' 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>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div><div class='xr-var-data'><pre>array([-89.99, -89.98, -89.97, ...,  89.97,  89.98,  89.99], 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 ... 180.0 180.0</div><input id='attrs-d9844632-0bbd-4560-b31c-e7aaf881eba3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9844632-0bbd-4560-b31c-e7aaf881eba3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f00bc0c-8c0d-4f61-bcb3-c57da2450e0d' class='xr-var-data-in' type='checkbox'><label for='data-6f00bc0c-8c0d-4f61-bcb3-c57da2450e0d' 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>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div><div class='xr-var-data'><pre>array([-179.99, -179.98, -179.97, ...,  179.98,  179.99,  180.  ],\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'>2021-09-01T09:00:00 ... 2021-09-...</div><input id='attrs-9f7fffe2-c82d-43e3-b508-ee7ef164f2f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f7fffe2-c82d-43e3-b508-ee7ef164f2f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-580c32bc-d2f1-495b-a1f0-34832e614588' class='xr-var-data-in' type='checkbox'><label for='data-580c32bc-d2f1-495b-a1f0-34832e614588' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-09-01T09:00:00.000000000&#x27;, &#x27;2021-09-02T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-03T09:00:00.000000000&#x27;, &#x27;2021-09-04T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-05T09:00:00.000000000&#x27;, &#x27;2021-09-06T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-07T09:00:00.000000000&#x27;, &#x27;2021-09-08T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-09T09:00:00.000000000&#x27;, &#x27;2021-09-10T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-11T09:00:00.000000000&#x27;, &#x27;2021-09-12T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-13T09:00:00.000000000&#x27;, &#x27;2021-09-14T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-15T09:00:00.000000000&#x27;, &#x27;2021-09-16T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-17T09:00:00.000000000&#x27;, &#x27;2021-09-18T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-19T09:00:00.000000000&#x27;, &#x27;2021-09-20T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-21T09:00:00.000000000&#x27;, &#x27;2021-09-22T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-23T09:00:00.000000000&#x27;, &#x27;2021-09-24T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-25T09:00:00.000000000&#x27;, &#x27;2021-09-26T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-27T09:00:00.000000000&#x27;, &#x27;2021-09-28T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-29T09:00:00.000000000&#x27;, &#x27;2021-09-30T09:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fef5000a-6ddd-4983-8063-2896dbe8615a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fef5000a-6ddd-4983-8063-2896dbe8615a' class='xr-section-summary' >Data variables: <span>(6)</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, 1023, 2047), meta=np.ndarray&gt;</div><input id='attrs-a14f3277-4948-442a-89bf-985107a1674a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a14f3277-4948-442a-89bf-985107a1674a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13e6cbbd-d222-4131-a400-1cb51cb165aa' class='xr-var-data-in' type='checkbox'><label for='data-13e6cbbd-d222-4131-a400-1cb51cb165aa' 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, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 72.42 GiB </td>\n",
       "                        <td> 7.99 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30, 17999, 36000) </td>\n",
       "                        <td> (1, 1023, 2047) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
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       "                        <td> 9720 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\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, 1023, 2047), meta=np.ndarray&gt;</div><input id='attrs-13a54577-25c0-41f7-aa40-8827f01749bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13a54577-25c0-41f7-aa40-8827f01749bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53daacaf-7ead-48ab-8f79-8b59ad0e8973' class='xr-var-data-in' type='checkbox'><label for='data-53daacaf-7ead-48ab-8f79-8b59ad0e8973' 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>uncertainty in \\&quot;analysed_sst\\&quot;</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 72.42 GiB </td>\n",
       "                        <td> 7.99 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30, 17999, 36000) </td>\n",
       "                        <td> (1, 1023, 2047) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
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       "                        <td> 9720 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dt_1km_data</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'>dask.array&lt;chunksize=(1, 1447, 2895), meta=np.ndarray&gt;</div><input id='attrs-babaf95c-dbaf-4d6c-8971-325104bc342c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-babaf95c-dbaf-4d6c-8971-325104bc342c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de7e97c8-9520-471b-a7d5-cd8e50154633' class='xr-var-data-in' type='checkbox'><label for='data-de7e97c8-9520-471b-a7d5-cd8e50154633' 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 to most recent 1km data</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd><dt><span>source :</span></dt><dd>MODIS and VIIRS pixels ingested by MUR</dd><dt><span>comment :</span></dt><dd>The grid value is hours between the analysis time and the most recent MODIS or VIIRS 1km L2P datum within 0.01 degrees from the grid point.  \\&quot;Fill value\\&quot; indicates absence of such 1km data at the grid point.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "            <table>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 144.83 GiB </td>\n",
       "                        <td> 31.96 MiB </td>\n",
       "                    </tr>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (1, 1447, 2895) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 10170 Tasks </td>\n",
       "                        <td> 5070 Chunks </td>\n",
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       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> timedelta64[ns] </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</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, 1447, 2895), meta=np.ndarray&gt;</div><input id='attrs-f8a5fd0d-c215-4ae5-aca2-ae28c4a1a275' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8a5fd0d-c215-4ae5-aca2-ae28c4a1a275' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-498b0b70-5c27-4f11-86d8-0f8033f96331' class='xr-var-data-in' type='checkbox'><label for='data-498b0b70-5c27-4f11-86d8-0f8033f96331' 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/land field composite mask</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>31</dd><dt><span>flag_masks :</span></dt><dd>[1, 2, 4, 8, 16]</dd><dt><span>flag_meanings :</span></dt><dd>open_sea land open_lake open_sea_with_ice_in_the_grid open_lake_with_ice_in_the_grid</dd><dt><span>comment :</span></dt><dd>mask can be used to further filter the data.</dd><dt><span>source :</span></dt><dd>GMT \\&quot;grdlandmask\\&quot;, ice flag from sea_ice_fraction data</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 15.98 MiB </td>\n",
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       "                        <td> (1, 1447, 2895) </td>\n",
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       "                    <tr>\n",
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       "                        <td> 5070 Chunks </td>\n",
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       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
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       "</table></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'>dask.array&lt;chunksize=(1, 1447, 2895), meta=np.ndarray&gt;</div><input id='attrs-e607a321-1aac-4d7e-9845-1b179a2293ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e607a321-1aac-4d7e-9845-1b179a2293ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-967869c7-f172-4c5e-9af5-a88a5e1a0069' class='xr-var-data-in' type='checkbox'><label for='data-967869c7-f172-4c5e-9af5-a88a5e1a0069' 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 area fraction</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>source :</span></dt><dd>EUMETSAT OSI-SAF, copyright EUMETSAT</dd><dt><span>comment :</span></dt><dd>ice fraction is a dimensionless quantity between 0 and 1; it has been interpolated by a nearest neighbor approach.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 72.42 GiB </td>\n",
       "                        <td> 15.98 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30, 17999, 36000) </td>\n",
       "                        <td> (1, 1447, 2895) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 10170 Tasks </td>\n",
       "                        <td> 5070 Chunks </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst_anomaly</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, 1023, 2047), meta=np.ndarray&gt;</div><input id='attrs-2553b53f-f5b0-40af-bdda-4c93bcae5894' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2553b53f-f5b0-40af-bdda-4c93bcae5894' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e06f6ed-c0fd-406a-9dca-06d514348256' class='xr-var-data-in' type='checkbox'><label for='data-2e06f6ed-c0fd-406a-9dca-06d514348256' 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>SST anomaly from a seasonal SST climatology based on the MUR data over 2003-2014 period</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>anomaly reference to the day-of-year average between 2003 and 2014</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                    <tr>\n",
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       "                        <td> 72.42 GiB </td>\n",
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      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:           (time: 30, lat: 17999, lon: 36000)\n",
       "Coordinates:\n",
       "  * lat               (lat) float32 -89.99 -89.98 -89.97 ... 89.97 89.98 89.99\n",
       "  * lon               (lon) float32 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n",
       "  * time              (time) datetime64[ns] 2021-09-01T09:00:00 ... 2021-09-3...\n",
       "Data variables:\n",
       "    analysed_sst      (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n",
       "    analysis_error    (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n",
       "    dt_1km_data       (time, lat, lon) timedelta64[ns] dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n",
       "    mask              (time, lat, lon) float32 dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n",
       "    sea_ice_fraction  (time, lat, lon) float32 dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n",
       "    sst_anomaly       (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n",
       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.7\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",
       "    project:                    NASA Making Earth Science Data Records for Us...\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"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.concat(datasets, 'time')\n",
    "ds"
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  },
  {
   "cell_type": "markdown",
   "id": "2325c279-0202-471e-b562-65627938911a",
   "metadata": {},
   "source": [
    "Look at the Analysed SST variable metadata"
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  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5c9d82a5-ff1b-4b9e-a10c-fd7252363124",
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   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;analysed_sst&#x27; (time: 30, lat: 17999, lon: 36000)&gt;\n",
       "dask.array&lt;concatenate, shape=(30, 17999, 36000), dtype=float32, chunksize=(1, 1023, 2047), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -89.99 -89.98 -89.97 -89.96 ... 89.97 89.98 89.99\n",
       "  * lon      (lon) float32 -180.0 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n",
       "  * time     (time) datetime64[ns] 2021-09-01T09:00:00 ... 2021-09-30T09:00:00\n",
       "Attributes:\n",
       "    long_name:      analysed sea surface temperature\n",
       "    standard_name:  sea_surface_foundation_temperature\n",
       "    units:          kelvin\n",
       "    valid_min:      -32767\n",
       "    valid_max:      32767\n",
       "    comment:        \\&quot;Final\\&quot; version using Multi-Resolution Variational Anal...\n",
       "    source:         MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'analysed_sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 30</li><li><span class='xr-has-index'>lat</span>: 17999</li><li><span class='xr-has-index'>lon</span>: 36000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d3fe712b-910a-41ee-9f0a-410c98e0e5af' class='xr-array-in' type='checkbox' checked><label for='section-d3fe712b-910a-41ee-9f0a-410c98e0e5af' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1023, 2047), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
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       "                        <td> </td>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 72.42 GiB </td>\n",
       "                        <td> 7.99 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30, 17999, 36000) </td>\n",
       "                        <td> (1, 1023, 2047) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 19470 Tasks </td>\n",
       "                        <td> 9720 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-416fd293-0d92-4442-b82e-0ce065e87073' class='xr-section-summary-in' type='checkbox'  checked><label for='section-416fd293-0d92-4442-b82e-0ce065e87073' 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 ... 89.98 89.99</div><input id='attrs-0ee8ab63-6b38-4b30-b391-69a12639fcd9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ee8ab63-6b38-4b30-b391-69a12639fcd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f976fb9-d8f9-47a3-8da5-fd78b841562f' class='xr-var-data-in' type='checkbox'><label for='data-2f976fb9-d8f9-47a3-8da5-fd78b841562f' 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>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div><div class='xr-var-data'><pre>array([-89.99, -89.98, -89.97, ...,  89.97,  89.98,  89.99], 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 ... 180.0 180.0</div><input id='attrs-f847fca7-5240-4150-860a-557196d80b91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f847fca7-5240-4150-860a-557196d80b91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a2df69e-a8a6-4b30-ac15-0c64770dc249' class='xr-var-data-in' type='checkbox'><label for='data-1a2df69e-a8a6-4b30-ac15-0c64770dc249' 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>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div><div class='xr-var-data'><pre>array([-179.99, -179.98, -179.97, ...,  179.98,  179.99,  180.  ],\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'>2021-09-01T09:00:00 ... 2021-09-...</div><input id='attrs-1ecb3b28-18ac-401f-9de3-5410f1bac825' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ecb3b28-18ac-401f-9de3-5410f1bac825' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c505171-4bde-409c-ac6a-ac52d601e24b' class='xr-var-data-in' type='checkbox'><label for='data-0c505171-4bde-409c-ac6a-ac52d601e24b' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-09-01T09:00:00.000000000&#x27;, &#x27;2021-09-02T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-03T09:00:00.000000000&#x27;, &#x27;2021-09-04T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-05T09:00:00.000000000&#x27;, &#x27;2021-09-06T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-07T09:00:00.000000000&#x27;, &#x27;2021-09-08T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-09T09:00:00.000000000&#x27;, &#x27;2021-09-10T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-11T09:00:00.000000000&#x27;, &#x27;2021-09-12T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-13T09:00:00.000000000&#x27;, &#x27;2021-09-14T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-15T09:00:00.000000000&#x27;, &#x27;2021-09-16T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-17T09:00:00.000000000&#x27;, &#x27;2021-09-18T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-19T09:00:00.000000000&#x27;, &#x27;2021-09-20T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-21T09:00:00.000000000&#x27;, &#x27;2021-09-22T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-23T09:00:00.000000000&#x27;, &#x27;2021-09-24T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-25T09:00:00.000000000&#x27;, &#x27;2021-09-26T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-27T09:00:00.000000000&#x27;, &#x27;2021-09-28T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-29T09:00:00.000000000&#x27;, &#x27;2021-09-30T09:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-af795b99-86d6-462b-bdce-225d7026bc24' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af795b99-86d6-462b-bdce-225d7026bc24' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>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, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'analysed_sst' (time: 30, lat: 17999, lon: 36000)>\n",
       "dask.array<concatenate, shape=(30, 17999, 36000), dtype=float32, chunksize=(1, 1023, 2047), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -89.99 -89.98 -89.97 -89.96 ... 89.97 89.98 89.99\n",
       "  * lon      (lon) float32 -180.0 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n",
       "  * time     (time) datetime64[ns] 2021-09-01T09:00:00 ... 2021-09-30T09:00:00\n",
       "Attributes:\n",
       "    long_name:      analysed sea surface temperature\n",
       "    standard_name:  sea_surface_foundation_temperature\n",
       "    units:          kelvin\n",
       "    valid_min:      -32767\n",
       "    valid_max:      32767\n",
       "    comment:        \\\"Final\\\" version using Multi-Resolution Variational Anal...\n",
       "    source:         MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_sst = ds.analysed_sst\n",
    "all_sst"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9c777a6-5460-4b65-ae82-d352e592038e",
   "metadata": {},
   "source": [
    "Create a dataset / variable that is only our area of interest and view its metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "265c1818-6cc3-417f-8c7c-76526fa5f504",
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       "dask.array&lt;getitem, shape=(30, 801, 1701), dtype=float32, chunksize=(1, 601, 1536), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
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       "  * time     (time) datetime64[ns] 2021-09-01T09:00:00 ... 2021-09-30T09:00:00\n",
       "Attributes:\n",
       "    long_name:      analysed sea surface temperature\n",
       "    standard_name:  sea_surface_foundation_temperature\n",
       "    units:          kelvin\n",
       "    valid_min:      -32767\n",
       "    valid_max:      32767\n",
       "    comment:        \\&quot;Final\\&quot; version using Multi-Resolution Variational Anal...\n",
       "    source:         MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'analysed_sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 30</li><li><span class='xr-has-index'>lat</span>: 801</li><li><span class='xr-has-index'>lon</span>: 1701</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-de5074cb-8b2b-40a9-b559-05e509a76648' class='xr-array-in' type='checkbox' checked><label for='section-de5074cb-8b2b-40a9-b559-05e509a76648' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 200, 1536), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "       &#x27;2021-09-17T09:00:00.000000000&#x27;, &#x27;2021-09-18T09:00:00.000000000&#x27;,\n",
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       "       &#x27;2021-09-21T09:00:00.000000000&#x27;, &#x27;2021-09-22T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-23T09:00:00.000000000&#x27;, &#x27;2021-09-24T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-25T09:00:00.000000000&#x27;, &#x27;2021-09-26T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-27T09:00:00.000000000&#x27;, &#x27;2021-09-28T09:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-29T09:00:00.000000000&#x27;, &#x27;2021-09-30T09:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f8ca3e0b-4904-48d3-9b57-094dd1baacea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f8ca3e0b-4904-48d3-9b57-094dd1baacea' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>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, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'analysed_sst' (time: 30, lat: 801, lon: 1701)>\n",
       "dask.array<getitem, shape=(30, 801, 1701), dtype=float32, chunksize=(1, 601, 1536), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 41.0 41.01 41.02 41.03 ... 48.97 48.98 48.99 49.0\n",
       "  * lon      (lon) float32 -93.0 -92.99 -92.98 -92.97 ... -76.02 -76.01 -76.0\n",
       "  * time     (time) datetime64[ns] 2021-09-01T09:00:00 ... 2021-09-30T09:00:00\n",
       "Attributes:\n",
       "    long_name:      analysed sea surface temperature\n",
       "    standard_name:  sea_surface_foundation_temperature\n",
       "    units:          kelvin\n",
       "    valid_min:      -32767\n",
       "    valid_max:      32767\n",
       "    comment:        \\\"Final\\\" version using Multi-Resolution Variational Anal...\n",
       "    source:         MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A..."
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst = ds.analysed_sst.sel(lat=lats, lon=lons)\n",
    "sst"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe72d226-3b5d-48d9-b277-252a93cf4e00",
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   "source": [
    "XArray reads data lazily, i.e. only when our code actually needs it.  Up to this point, we haven't read any data values, only metadata.  The next line will force XArray to read the portions of the source files containing our area of interest.  Behind the scenes, the eosdis-zarr-store library is ensuring data is fetched as efficiently as possible.\n",
    "\n",
    "Note: This line isn't strictly necessary, since XArray will automatically read the data we need the first time our code tries to use it, but calling this will make sure that we can read the data multiple times later on without re-fetching anything from the source files.\n",
    "\n",
    "This line will take several seconds to complete, but since it is retrieving only about 50 MB of data from 22 GB of source files, several seconds constitutes a significant time, bandwidth, and disk space savings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3d2a7f69-b648-42a5-a60e-556d3b7d003d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sst.load();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fb83570-30d3-4670-8291-d9c281a53ede",
   "metadata": {},
   "source": [
    "Now we can start looking at aggregations across the time dimension.  In this case, plot the standard deviation of the temperature at each point to get a visual sense of how much temperatures fluctuate over the course of the month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bc07917d-aae5-4f1b-8cb3-0632dfc00a20",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/numpy/lib/nanfunctions.py:1670: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
      "  var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We expect a warning here, from finding the standard deviation of arrays that contain all N/A values.\n",
    "# numpy produces N/A for these points, though, which is exactly what we want.\n",
    "stdev_sst = sst.std('time')\n",
    "stdev_sst.name = 'stdev of analysed_sst [Kelvin]'\n",
    "stdev_sst.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f31b96b5-ce99-4179-bc48-75520310c1c8",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Interactive animation of a month of data\n",
    "\n",
    "This section isn't as important to fully understand.  It shows us a way to get an interactive animation to see what we have retrieved so far"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c6cd229-9a17-476c-89e4-74bcc2ee47d2",
   "metadata": {},
   "source": [
    "Define an animation function to plot the `i`th time step.  We need to make sure each plot is using the same color scale, set by `vmin` and `vmax` so the animation is consistent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5005ab2c-6ae7-4284-bccb-ee50558b00c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "sst_min = sst.min()\n",
    "sst_max = sst.max()\n",
    "\n",
    "def show_time_step(i):\n",
    "    plt.clf()\n",
    "    res = sst[i].plot.imshow(vmin=sst_min, vmax=sst_max)\n",
    "    return (res,)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e14aa74-90f7-4092-bd2b-2aeadfacf33b",
   "metadata": {},
   "source": [
    "Render each time slice once and show it as an HTML animation with interactive controls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ed7a0427-cc7d-4be5-b4d4-84708f97244c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#anim = animation.FuncAnimation(plt.gcf(), func=show_time_step, frames=len(sst))\n",
    "#display(HTML(anim.to_jshtml()))\n",
    "#plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25403454-1853-4892-8446-2ef6398a69ef",
   "metadata": {},
   "source": [
    "### Supplemental: What's happening here?\n",
    "\n",
    "For EOSDIS data in the cloud, we have begun producing a metadata sidecar file in a format called DMR++ that extracts all of the information about arrays, variables, and dimensions from data files, as well as the byte offsets in the NetCDF4 file where data can be found.  This information is sufficient to let the Zarr library read data from our NetCDF4 files, but it's in the wrong format.  zarr-eosdis-store knows how to fetch the sidecar file and transform it into something the Zarr library understands.  Passing it when reading Zarr using XArray or the Zarr library lets these libraries interact with EOSDIS data exactly as if they were Zarr stores in a way that's more optimal for reading data in the cloud.  Beyond this, the zarr-eosdis-store library makes some optimizations in the way it reads data to help make up for situations where the NetCDF4 file is not internally arranged well for cloud-based access patterns."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
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 },
 "nbformat": 4,
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}