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{
"cells": [
{
"cell_type": "markdown",
"id": "e9dfe0df-9f1a-4e5d-99d3-81f01a76d3c8",
"metadata": {
"tags": []
},
"source": [
"# Awesome HRRR \n",
"\n",
"**Rich Signell (USGS) with Martin Durant (Anaconda), Eskild Eriksen (Quansight)**\n",
"\n",
"NOAA's High-Resolution Rapid Refresh (HRRR) model is a 3km model of CONUS updated every hour, assimilating Radar data every 15 minutes. \n",
"\n",
"![](https://rapidrefresh.noaa.gov/hrrr/hrrrcrefimage)\n",
"\n",
"**Data**: \n",
"* [AWS Public Data](https://registry.opendata.aws/noaa-hrrr-pds/): Available on the Cloud, YAY! Thousands of GRIB2 files each day -- not awesome yet!\n",
"\n",
"**Goal**:\n",
"Make it easy and cloud-performant to access this collection of data. We use Kerchunk/Fsspec to extract all the metadata, create a virtual Zarrset which just pokes into the files to only extract the compressed data chunks from the pile of files. \n",
"\n",
"**Tech Stack**:\n",
"* [Nebari](https://www.nebari.dev/) (formerly Qhub) for deploying JupyterHub on Kubernetes with [Dask Gateway](https://gateway.dask.org/)\n",
"* [fsspec](https://filesystem-spec.readthedocs.io/en/latest/) for representing scientific data format files as a Zarr dataset (ReferenceFileSystem)\n",
"* [Kerchunk](https://github.com/fsspec/kerchunk) for creating the ReferenceFileSystem JSON\n",
"* [kbatch](https://github.com/kbatch-dev/kbatch) for running notebooks as batch jobs on Kubernetes (includes new `cronjob` feature for running on a schedule)\n",
"* Jupyter, Dask, Xarray, Zarr, Holoviz\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9ac4c885-3e20-473b-aca5-63e611c181f9",
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"import hvplot.xarray\n",
"import panel as pn\n",
"import fsspec\n",
"import datetime as dt"
]
},
{
"cell_type": "markdown",
"id": "eac4889f-9979-4016-b492-ac54b3b1289d",
"metadata": {},
"source": [
"#### Create Dask Cluster \n",
"Here we use Dask Gateway, but could use any Dask Cluster"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f0d7312e-ccbf-4962-8320-12a31fbabe16",
"metadata": {},
"outputs": [],
"source": [
"from dask_gateway import Gateway\n",
"# instantiate dask gateway\n",
"gateway = Gateway()\n",
"\n",
"# setup a cluster\n",
"options = gateway.cluster_options(use_local_defaults=False)\n",
"\n",
"options.conda_environment='users/pangeo'\n",
"options.profile = 'Medium Worker'\n",
"\n",
"cluster = gateway.new_cluster(options)\n",
"\n",
"cluster.scale(30)\n",
"\n",
"# get the client for the cluster\n",
"client = cluster.get_client()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "55e2b4d0-2753-4a6e-b14e-43aa3c932a0f",
"metadata": {},
"outputs": [
{
"data": {
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"model_id": "8d537adc78c14d4db0a9b3a88389a1a3",
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"VBox(children=(HTML(value='<h2>GatewayCluster</h2>'), HBox(children=(HTML(value='\\n<div>\\n<style scoped>\\n …"
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"source": [
"cluster"
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{
"cell_type": "code",
"execution_count": 22,
"id": "a6e1055e-bb20-49e1-8008-157e62827fa1",
"metadata": {},
"outputs": [],
"source": [
"rpath = 's3://esip-qhub-public/noaa/hrrr/hrrr_best.json'\n",
"\n",
"r_opts = {'anon':True}\n",
"s_opts = {'anon':True, 'skip_instance_cache':True}\n",
"\n",
"fs = fsspec.filesystem(\"reference\", fo=rpath, ref_storage_args=s_opts,\n",
" remote_protocol='s3', remote_options=r_opts)\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "d677f2dc-f4f5-466c-9ac3-e983fa32baad",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" {'name': 'valid_time', 'type': 'directory', 'size': 0},\n",
" {'name': '.zattrs', 'type': 'file', 'size': 422},\n",
" {'name': 'heightAboveGround', 'type': 'directory', 'size': 0},\n",
" {'name': 'time', 'type': 'directory', 'size': 0},\n",
" {'name': 'step', 'type': 'directory', 'size': 0},\n",
" {'name': 'latitude', 'type': 'directory', 'size': 0},\n",
" {'name': 'longitude', 'type': 'directory', 'size': 0},\n",
" {'name': 't2m', 'type': 'directory', 'size': 0},\n",
" {'name': 'pt', 'type': 'directory', 'size': 0},\n",
" {'name': 'sh2', 'type': 'directory', 'size': 0},\n",
" {'name': 'd2m', 'type': 'directory', 'size': 0},\n",
" {'name': 'r2', 'type': 'directory', 'size': 0},\n",
" {'name': 'u10', 'type': 'directory', 'size': 0},\n",
" {'name': 'v10', 'type': 'directory', 'size': 0},\n",
" {'name': 'si10', 'type': 'directory', 'size': 0},\n",
" {'name': 'unknown', 'type': 'directory', 'size': 0}]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fs.ls('')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "91b29a55-545f-40be-9d98-a611dca5b47c",
"metadata": {},
"outputs": [],
"source": [
"m = fs.get_mapper(\"\")\n",
"\n",
"ds = xr.open_dataset(m, engine=\"zarr\", backend_kwargs=dict(consolidated=False), \n",
" chunks={'valid_time':1})"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "921439f7-1963-4ce1-a66d-06fa729937a5",
"metadata": {},
"outputs": [
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"Dimensions: (valid_time: 90, y: 1059, x: 1799)\n",
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" * valid_time (valid_time) datetime64[ns] 2022-07-10T17:00:00 ... 20...\n",
"Dimensions without coordinates: y, x\n",
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" GRIB_subCentre: 0\n",
" history: 2022-07-13T18:55 GRIB to CDM+CF via cfgrib-0.9.1...\n",
" institution: US National Weather Service - NCEP</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-073e1504-bff5-4213-8921-e58c5aafc278' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-073e1504-bff5-4213-8921-e58c5aafc278' 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'>valid_time</span>: 90</li><li><span>y</span>: 1059</li><li><span>x</span>: 1799</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d446f914-b8d6-47a7-bcd2-0d67e551485c' class='xr-section-summary-in' type='checkbox' checked><label for='section-d446f914-b8d6-47a7-bcd2-0d67e551485c' class='xr-section-summary' >Coordinates: <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>heightAboveGround</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1c6c34bf-a586-435d-94aa-9a7341b2c33d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c6c34bf-a586-435d-94aa-9a7341b2c33d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5979ded5-dad4-4696-bcd5-d5b040295f9c' class='xr-var-data-in' type='checkbox'><label for='data-5979ded5-dad4-4696-bcd5-d5b040295f9c' 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>height above the surface</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1059, 1799), meta=np.ndarray&gt;</div><input id='attrs-b9e3fc11-b37a-4d76-9bfb-525b58b2e019' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9e3fc11-b37a-4d76-9bfb-525b58b2e019' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f54a6194-706c-4404-8d77-6d00a6467a06' class='xr-var-data-in' type='checkbox'><label for='data-f54a6194-706c-4404-8d77-6d00a6467a06' 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>units :</span></dt><dd>degrees_north</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> 14.54 MiB </td>\n",
" <td> 14.54 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (1059, 1799) </td>\n",
" <td> (1059, 1799) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 2 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1059, 1799), meta=np.ndarray&gt;</div><input id='attrs-fff24478-fe04-4040-bff0-9778e6864b41' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fff24478-fe04-4040-bff0-9778e6864b41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-576215a8-afb1-4403-bcac-6305e6693c73' class='xr-var-data-in' type='checkbox'><label for='data-576215a8-afb1-4403-bcac-6305e6693c73' 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>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
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" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 14.54 MiB </td>\n",
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" \n",
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" <tr>\n",
" <th> Count </th>\n",
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" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
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" </tbody>\n",
" </table>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-97f34f31-9fd3-41bc-b92b-a6f20af643f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97f34f31-9fd3-41bc-b92b-a6f20af643f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff70ba5c-683c-45a6-a160-380049605925' class='xr-var-data-in' type='checkbox'><label for='data-ff70ba5c-683c-45a6-a160-380049605925' 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 since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(3600000000000, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(valid_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-d2c381ad-e444-4cf7-a0da-754b40a51a30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2c381ad-e444-4cf7-a0da-754b40a51a30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34aa94e7-1aef-4bb4-8d2c-b4767d8b9d94' class='xr-var-data-in' type='checkbox'><label for='data-34aa94e7-1aef-4bb4-8d2c-b4767d8b9d94' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
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" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
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" <td> (1,) </td>\n",
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" <tr>\n",
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" <tr>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>valid_time</span></div><div class='xr-var-dims'>(valid_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-07-10T17:00:00 ... 2022-07-...</div><input id='attrs-966e72ef-6e3b-42be-9992-c7ca9c771d72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-966e72ef-6e3b-42be-9992-c7ca9c771d72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f50f5fe-2f00-4e88-9077-8b9ccf17a3dd' class='xr-var-data-in' type='checkbox'><label for='data-9f50f5fe-2f00-4e88-9077-8b9ccf17a3dd' 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</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-07-10T17:00:00.000000000&#x27;, &#x27;2022-07-10T18:00:00.000000000&#x27;,\n",
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" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7575c412-f663-47ea-bdd7-23bbaba4e472' class='xr-section-summary-in' type='checkbox' checked><label for='section-7575c412-f663-47ea-bdd7-23bbaba4e472' class='xr-section-summary' >Data variables: <span>(9)</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>d2m</span></div><div class='xr-var-dims'>(valid_time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1059, 1799), meta=np.ndarray&gt;</div><input id='attrs-7b43855e-bbed-4973-b86a-fee515d12af7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b43855e-bbed-4973-b86a-fee515d12af7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d37bf373-2b14-4c3d-af05-1ca0bd6e37a0' class='xr-var-data-in' type='checkbox'><label for='data-d37bf373-2b14-4c3d-af05-1ca0bd6e37a0' 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>GRIB_DxInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_DyInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_LaDInDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin1InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin2InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_LoVInDegrees :</span></dt><dd>262.5</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1799</dd><dt><span>GRIB_Ny :</span></dt><dd>1059</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d2m</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Lambert Conformal can be secant or tangent, conical or bipolar</dd><dt><span>GRIB_gridType :</span></dt><dd>lambert</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>21.138123</dd><dt><span>GRIB_latitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>237.280472</dd><dt><span>GRIB_longitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1905141</dd><dt><span>GRIB_paramId :</span></dt><dd>168</dd><dt><span>GRIB_shortName :</span></dt><dd>2d</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v10</span></div><div class='xr-var-dims'>(valid_time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1059, 1799), meta=np.ndarray&gt;</div><input id='attrs-28ac60c0-9366-44c3-90a5-d05ce76df5eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28ac60c0-9366-44c3-90a5-d05ce76df5eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62a4807a-2801-4919-b5b5-98f41733800f' class='xr-var-data-in' type='checkbox'><label for='data-62a4807a-2801-4919-b5b5-98f41733800f' 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>GRIB_DxInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_DyInMetres :</span></dt><dd>3000.0</dd><dt><span>GRIB_LaDInDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin1InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_Latin2InDegrees :</span></dt><dd>38.5</dd><dt><span>GRIB_LoVInDegrees :</span></dt><dd>262.5</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>1799</dd><dt><span>GRIB_Ny :</span></dt><dd>1059</dd><dt><span>GRIB_cfName :</span></dt><dd>northward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v10</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Lambert Conformal can be secant or tangent, conical or bipolar</dd><dt><span>GRIB_gridType :</span></dt><dd>lambert</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>1</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>21.138123</dd><dt><span>GRIB_latitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>237.280472</dd><dt><span>GRIB_longitudeOfSouthernPoleInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>10 metre V wind component</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>1905141</dd><dt><span>GRIB_paramId :</span></dt><dd>166</dd><dt><span>GRIB_shortName :</span></dt><dd>10v</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</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> 654.08 MiB </td>\n",
" <td> 7.27 MiB </td>\n",
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" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (90, 1059, 1799) </td>\n",
" <td> (1, 1059, 1799) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 91 Tasks </td>\n",
" <td> 90 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float32 </td>\n",
" <td> numpy.ndarray </td>\n",
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" </tbody>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-2875f789-cb3e-4825-8062-d4882f2ec23f' class='xr-section-summary-in' type='checkbox' checked><label for='section-2875f789-cb3e-4825-8062-d4882f2ec23f' 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>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>GRIB_centre :</span></dt><dd>kwbc</dd><dt><span>GRIB_centreDescription :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>2022-07-13T18:55 GRIB to CDM+CF via cfgrib-0.9.10.1/ecCodes-2.26.0 with {&quot;source&quot;: &quot;../../tmp/tmpm5rzdj87grib2&quot;, &quot;filter_by_keys&quot;: {}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd><dt><span>institution :</span></dt><dd>US National Weather Service - NCEP</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (valid_time: 90, y: 1059, x: 1799)\n",
"Coordinates:\n",
" heightAboveGround float64 ...\n",
" latitude (y, x) float64 dask.array<chunksize=(1059, 1799), meta=np.ndarray>\n",
" longitude (y, x) float64 dask.array<chunksize=(1059, 1799), meta=np.ndarray>\n",
" step timedelta64[ns] ...\n",
" time (valid_time) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>\n",
" * valid_time (valid_time) datetime64[ns] 2022-07-10T17:00:00 ... 20...\n",
"Dimensions without coordinates: y, x\n",
"Data variables:\n",
" d2m (valid_time, y, x) float32 dask.array<chunksize=(1, 1059, 1799), meta=np.ndarray>\n",
" pt (valid_time, y, x) float32 dask.array<chunksize=(1, 1059, 1799), meta=np.ndarray>\n",
" r2 (valid_time, y, x) float32 dask.array<chunksize=(1, 1059, 1799), meta=np.ndarray>\n",
" sh2 (valid_time, y, x) float32 dask.array<chunksize=(1, 1059, 1799), meta=np.ndarray>\n",
" si10 (valid_time, y, x) float32 dask.array<chunksize=(1, 1059, 1799), meta=np.ndarray>\n",
" t2m (valid_time, y, x) float32 dask.array<chunksize=(1, 1059, 1799), meta=np.ndarray>\n",
" u10 (valid_time, y, x) float32 dask.array<chunksize=(1, 1059, 1799), meta=np.ndarray>\n",
" unknown (valid_time, y, x) float32 dask.array<chunksize=(1, 1059, 1799), meta=np.ndarray>\n",
" v10 (valid_time, y, x) float32 dask.array<chunksize=(1, 1059, 1799), meta=np.ndarray>\n",
"Attributes:\n",
" Conventions: CF-1.7\n",
" GRIB_centre: kwbc\n",
" GRIB_centreDescription: US National Weather Service - NCEP\n",
" GRIB_edition: 2\n",
" GRIB_subCentre: 0\n",
" history: 2022-07-13T18:55 GRIB to CDM+CF via cfgrib-0.9.1...\n",
" institution: US National Weather Service - NCEP"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds"
]
},
{
"cell_type": "markdown",
"id": "2199fcb9-3b85-47ea-80de-e80232c1e2ec",
"metadata": {},
"source": [
"Hvplot wants lon [-180,180], not [0,360]:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "ad699973-8780-49ac-a7c0-7ce3f74a7147",
"metadata": {},
"outputs": [],
"source": [
"ds = ds.assign_coords(longitude=(((ds.longitude + 180) % 360) - 180))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "baf91e48-4b4d-49ab-ae8a-e52c7458232c",
"metadata": {},
"outputs": [],
"source": [
"var = 't2m' # Temperature at 2m height"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "a408c1b6-06a9-43d3-b69a-1b73dc0d9a48",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2022-07-13 22:00:00\n"
]
}
],
"source": [
"now = dt.datetime.utcnow().strftime('%Y-%m-%d %H:00:00')\n",
"print(now)"
]
},
{
"cell_type": "markdown",
"id": "95869e81-657b-4fb1-9553-b9197c92bbd1",
"metadata": {},
"source": [
"With 30 worker cluster, takes 50 seconds to display, and 15 seconds to change the time step\n",
"after closing the dask client, it takes 30 seconds to display, 8 seconds to display a time step"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "171c7153-efb7-4aa0-acac-ec308a691f11",
"metadata": {},
"outputs": [
{
"data": {},
"metadata": {},
"output_type": "display_data"
},
{
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