sea-level-rise.ipynb

{
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   "source": [
    "# Analyzing Sea Level Rise in the Cloud with Coiled and Earthaccess\n",
    "\n",
    "_This notebook was adapted from [this NASA Earthdata Cloud Cookbook example](https://nasa-openscapes.github.io/earthdata-cloud-cookbook/tutorials/Sea_Level_Rise.html)_"
   ]
  },
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   "cell_type": "markdown",
   "id": "4a30aadb-41d4-4c08-a6fa-5ea60c6bd695",
   "metadata": {},
   "source": [
    "## Get data files with `earthaccess`"
   ]
  },
  {
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   "source": [
    "# Authenticate my machine with `earthaccess`\n",
    "import earthaccess\n",
    "\n",
    "earthaccess.login();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "%%time\n",
    "\n",
    "# Retrieve data files for the dataset I'm interested in\n",
    "granules = earthaccess.search_data(\n",
    "    short_name=\"SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL2205\",\n",
    "    temporal=(\"2000\", \"2019\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64df6beb-e1e9-4691-b6ec-18dfa6803f53",
   "metadata": {},
   "source": [
    "## Define processing function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5cb0793-5273-4ede-9b8d-080a79ab8228",
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   "source": [
    "import coiled\n",
    "import xarray as xr\n",
    "\n",
    "@coiled.function(region=\"us-west-2\")  # Same region as data\n",
    "def process(granule, fs):\n",
    "    results = []\n",
    "    for file in granule.data_links(\"direct\"):\n",
    "        ds = xr.open_dataset(fs.open(file))\n",
    "        ds = ds.sel(Latitude=slice(23, 50), Longitude=slice(270, 330))\n",
    "        ds = ds.SLA.where((ds.SLA >= 0) & (ds.SLA < 10))\n",
    "        results.append(ds)\n",
    "    result = xr.concat(results, dim=\"Time\")\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd853bd7-244e-4fbb-9cee-5e7921d082b1",
   "metadata": {},
   "source": [
    "## Process Granules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1d3fb8a-8f86-406c-860d-7ee9c8eb45ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = earthaccess.get_s3fs_session(results=granules)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86988643-b344-43c5-9ad7-51c17c24b4d7",
   "metadata": {},
   "source": [
    "### Process single file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b268d9e1-b056-444e-9b99-1171e2f5b075",
   "metadata": {},
   "outputs": [],
   "source": [
    "chunk = process(granules[0], fs=fs)\n",
    "chunk.plot(x=\"Longitude\", y=\"Latitude\", figsize=(14, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe32079-17f9-45a8-9601-c0fe21980183",
   "metadata": {},
   "source": [
    "### Process all files in parallel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d187d258-4caf-4430-b4f1-d34d920a83a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "chunks = process.map(granules, fs=fs)  # This runs on the cloud in parallel\n",
    "ds = xr.concat(chunks, dim=\"Time\")\n",
    "ds.std(\"Time\").plot(x=\"Longitude\", y=\"Latitude\", figsize=(14, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41e5f464-5504-41d6-a170-96c57a4c6808",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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