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lbesnard/srs_mfdataset_issue_showcase.ipynb

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Gist showcasing issue with mfdataset with a bad SRS GHRSST file
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srs_mfdataset_issue_showcase.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "7f2ba9d4-e4c3-4c9b-8703-41bfc8c6821a",
"metadata": {},
"outputs": [],
"source": [
"import s3fs\n",
"import xarray as xr\n",
"\n",
"s3 = s3fs.S3FileSystem(anon=True)\n",
"\n",
"s3path = 's3://imos-data/IMOS/SRS/SST/ghrsst/L3S-1d/dn/2023/*'\n",
"remote_files = s3.glob(s3path)\n",
"\n",
"# create a fileset\n",
"fileset = [s3.open(file) for file in remote_files]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7e263d9c-294e-41d3-ab6e-3eb1fb09a98e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<File-like object S3FileSystem, imos-data/IMOS/SRS/SST/ghrsst/L3S-1d/dn/2023/20230628092000-ABOM-L3S_GHRSST-SSTfnd-AVHRR_D-1d_dn.nc>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# located issue with one NetCDF file:\n",
"subset = fileset[170:180]\n",
"\n",
"# ISSUE is with file 0, when added to another file\n",
"subset[0]\n",
"\n",
"# similar to file https://thredds.aodn.org.au/thredds/dodsC/IMOS/SRS/SST/ghrsst/L3S-1d/dn/2023/20230628092000-ABOM-L3S_GHRSST-SSTfnd-AVHRR_D-1d_dn.nc.html"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7ef72026-35d0-4ff9-89a7-545b03f325c9",
"metadata": {},
"outputs": [
{
"ename": "MergeError",
"evalue": "conflicting values for variable 'sea_surface_temperature' on objects to be combined. You can skip this check by specifying compat='override'.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mMergeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_1282380/1347201667.py\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Showcasing issue when loading this \"bad\" file with another one, \"good\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m ds = xr.open_mfdataset(\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'h5netcdf'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/AodnCloudOptimised/lib/python3.10/site-packages/xarray/backends/api.py\u001b[0m in \u001b[0;36mopen_mfdataset\u001b[0;34m(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, **kwargs)\u001b[0m\n\u001b[1;32m 1080\u001b[0m \u001b[0;31m# Redo ordering from coordinates, ignoring how they were ordered\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1081\u001b[0m \u001b[0;31m# previously\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1082\u001b[0;31m combined = combine_by_coords(\n\u001b[0m\u001b[1;32m 1083\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1084\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/AodnCloudOptimised/lib/python3.10/site-packages/xarray/core/combine.py\u001b[0m in \u001b[0;36mcombine_by_coords\u001b[0;34m(data_objects, compat, data_vars, coords, fill_value, join, combine_attrs)\u001b[0m\n\u001b[1;32m 969\u001b[0m )\n\u001b[1;32m 970\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 971\u001b[0;31m return merge(\n\u001b[0m\u001b[1;32m 972\u001b[0m \u001b[0mconcatenated_grouped_by_data_vars\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 973\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/AodnCloudOptimised/lib/python3.10/site-packages/xarray/core/merge.py\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(objects, compat, join, fill_value, combine_attrs)\u001b[0m\n\u001b[1;32m 974\u001b[0m \u001b[0mdict_like_objects\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 976\u001b[0;31m merge_result = merge_core(\n\u001b[0m\u001b[1;32m 977\u001b[0m \u001b[0mdict_like_objects\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 978\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/AodnCloudOptimised/lib/python3.10/site-packages/xarray/core/merge.py\u001b[0m in \u001b[0;36mmerge_core\u001b[0;34m(objects, compat, join, combine_attrs, priority_arg, explicit_coords, indexes, fill_value, skip_align_args)\u001b[0m\n\u001b[1;32m 699\u001b[0m \u001b[0mcollected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcollect_variables_and_indexes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maligned\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindexes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0mprioritized\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_priority_vars_and_indexes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maligned\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpriority_arg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m variables, out_indexes = merge_collected(\n\u001b[0m\u001b[1;32m 702\u001b[0m \u001b[0mcollected\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprioritized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcombine_attrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcombine_attrs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 703\u001b[0m )\n",
"\u001b[0;32m~/miniforge3/envs/AodnCloudOptimised/lib/python3.10/site-packages/xarray/core/merge.py\u001b[0m in \u001b[0;36mmerge_collected\u001b[0;34m(grouped, prioritized, compat, combine_attrs, equals)\u001b[0m\n\u001b[1;32m 288\u001b[0m \u001b[0mvariables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mvariable\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvariable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0melements_list\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m merged_vars[name] = unique_variable(\n\u001b[0m\u001b[1;32m 291\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mequals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 292\u001b[0m )\n",
"\u001b[0;32m~/miniforge3/envs/AodnCloudOptimised/lib/python3.10/site-packages/xarray/core/merge.py\u001b[0m in \u001b[0;36munique_variable\u001b[0;34m(name, variables, compat, equals)\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mequals\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 144\u001b[0;31m raise MergeError(\n\u001b[0m\u001b[1;32m 145\u001b[0m \u001b[0;34mf\"conflicting values for variable {name!r} on objects to be combined. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[0;34m\"You can skip this check by specifying compat='override'.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mMergeError\u001b[0m: conflicting values for variable 'sea_surface_temperature' on objects to be combined. You can skip this check by specifying compat='override'."
]
}
],
"source": [
"# Showcasing issue when loading this \"bad\" file with another one, \"good\"\n",
"\n",
"ds = xr.open_mfdataset(\n",
" subset[0:2],\n",
" engine='h5netcdf',\n",
" concat_characters=True,\n",
" mask_and_scale=True,\n",
" decode_cf=True,\n",
" decode_times=True,\n",
" use_cftime=True,\n",
" parallel=True,\n",
" decode_coords=True,\n",
" compat=\"equals\",\n",
" lock=False\n",
")"
]
},
{
"cell_type": "markdown",
"id": "1ca6c547-aa62-421e-8cc6-c501ce00ac47",
"metadata": {},
"source": [
"As it can be seen there is a conflict error. If i remove the compat=\"equals\" option, my local machines consume all the available memory.\n",
"\n",
"I also tried on a cluster with dask; the dask graph is absolutely horrible and ends up failing. Even with 64Gb of mem"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7a8871c8-8781-404d-a804-07aaf9d24d0a",
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 8GB\n",
"Dimensions: (time: 3, lat: 4500, lon: 6000)\n",
"Coordinates:\n",
" * lat (lat) float32 18kB 19.99 19.97 ... -69.97 -69.99\n",
" * lon (lon) float32 24kB 70.01 70.03 ... 190.0 190.0\n",
" * time (time) object 24B 2023-06-29 09:20:00 ... 2023...\n",
"Data variables: (12/14)\n",
" sea_surface_temperature (time, lat, lon) float64 648MB dask.array&lt;chunksize=(1, 1500, 2000), meta=np.ndarray&gt;\n",
" sst_dtime (time, lat, lon) float64 648MB dask.array&lt;chunksize=(1, 1500, 2000), meta=np.ndarray&gt;\n",
" dt_analysis (time, lat, lon) float64 648MB dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;\n",
" wind_speed (time, lat, lon) float64 648MB dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;\n",
" wind_speed_dtime_from_sst (time, lat, lon) float64 648MB dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;\n",
" satellite_zenith_angle (time, lat, lon) float64 648MB dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;\n",
" ... ...\n",
" sses_bias (time, lat, lon) float64 648MB dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;\n",
" sses_standard_deviation (time, lat, lon) float64 648MB dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;\n",
" sses_count (time, lat, lon) float64 648MB dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;\n",
" sst_count (time, lat, lon) float64 648MB dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;\n",
" sst_mean (time, lat, lon) float64 648MB dask.array&lt;chunksize=(1, 1500, 2000), meta=np.ndarray&gt;\n",
" sst_standard_deviation (time, lat, lon) float64 648MB dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;\n",
"Attributes: (12/47)\n",
" title: IMOS L3S Day and Night gridded multiple-senso...\n",
" summary: Skin SST retrievals produced from stitching t...\n",
" references: http://imos.org.au/sstproducts.html and Griff...\n",
" institution: ABOM\n",
" comment: HRPT AVHRR experimental L3 retrieval produced...\n",
" license: GHRSST protocol describes data use as free an...\n",
" ... ...\n",
" processing_level: L3S\n",
" cdm_data_type: grid\n",
" history: platform_counts=NOAA-18=2,quality_counts=arch...\n",
" Conventions: CF-1.6\n",
" creator_url: http://www.imos.org.au/srs.html\n",
" acknowledgment: Any use of these data requires the following ...</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-6d1e9d89-df38-4e85-aa79-448ff081e8d6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6d1e9d89-df38-4e85-aa79-448ff081e8d6' 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>: 3</li><li><span class='xr-has-index'>lat</span>: 4500</li><li><span class='xr-has-index'>lon</span>: 6000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-52ae3118-fd6a-42e6-abe1-69a4ed723a33' class='xr-section-summary-in' type='checkbox' checked><label for='section-52ae3118-fd6a-42e6-abe1-69a4ed723a33' 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'>19.99 19.97 19.95 ... -69.97 -69.99</div><input id='attrs-dd93a1df-1651-495b-9a37-f3af866d0d07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd93a1df-1651-495b-9a37-f3af866d0d07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cd9a310-62ab-4bdb-9f09-3385a8b079d9' class='xr-var-data-in' type='checkbox'><label for='data-6cd9a310-62ab-4bdb-9f09-3385a8b079d9' 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>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>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>Latitudes for locating data</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 19.99, 19.97, 19.95, ..., -69.95, -69.97, -69.99], 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'>70.01 70.03 70.05 ... 190.0 190.0</div><input id='attrs-29078001-cf21-4891-b9e2-2324b07a1b74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29078001-cf21-4891-b9e2-2324b07a1b74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3aca5e2b-87a0-4358-bb30-0fa962ff23f9' class='xr-var-data-in' type='checkbox'><label for='data-3aca5e2b-87a0-4358-bb30-0fa962ff23f9' 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>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitudes for locating data</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([ 70.01, 70.03, 70.05, ..., 189.95, 189.97, 189.99], 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'>object</div><div class='xr-var-preview xr-preview'>2023-06-29 09:20:00 ... 2023-07-...</div><input id='attrs-25536c15-c6f4-4fa1-a128-8bf83ece9889' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25536c15-c6f4-4fa1-a128-8bf83ece9889' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93837eb1-42e8-49d6-a7b8-f829eb28831e' class='xr-var-data-in' type='checkbox'><label for='data-93837eb1-42e8-49d6-a7b8-f829eb28831e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>reference time of sst file</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>comment :</span></dt><dd>A typical reference time for data</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2023, 6, 29, 9, 20, 0, 0, has_year_zero=False),\n",
" cftime.DatetimeGregorian(2023, 6, 30, 9, 20, 0, 0, has_year_zero=False),\n",
" cftime.DatetimeGregorian(2023, 7, 1, 9, 20, 0, 0, has_year_zero=False)],\n",
" dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9c7d16df-181d-4078-b076-cc36fc9c9dd2' class='xr-section-summary-in' type='checkbox' checked><label for='section-9c7d16df-181d-4078-b076-cc36fc9c9dd2' class='xr-section-summary' >Data variables: <span>(14)</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>sea_surface_temperature</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1500, 2000), meta=np.ndarray&gt;</div><input id='attrs-08a5821a-0e42-4259-9278-41245aeaf702' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08a5821a-0e42-4259-9278-41245aeaf702' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-420609b7-f3d2-4b08-a3dd-29e77bd58a7a' class='xr-var-data-in' type='checkbox'><label for='data-420609b7-f3d2-4b08-a3dd-29e77bd58a7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface foundation temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>comment :</span></dt><dd>An estimate of the foundation temperature of the upper few meters of the ocean in the absence of diurnal variation</dd><dt><span>standard_name :</span></dt><dd>sea_surface_foundation_temperature</dd><dt><span>valid_min :</span></dt><dd>-32767</dd><dt><span>valid_max :</span></dt><dd>32767</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>sst_dtime</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1500, 2000), meta=np.ndarray&gt;</div><input id='attrs-af205474-b626-4d95-8fc6-310777b242fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af205474-b626-4d95-8fc6-310777b242fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7c56b4a-c740-44d9-a923-b37a7c74e5b5' class='xr-var-data-in' type='checkbox'><label for='data-b7c56b4a-c740-44d9-a923-b37a7c74e5b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time difference from reference time</dd><dt><span>units :</span></dt><dd>second</dd><dt><span>comment :</span></dt><dd>time plus sst_dtime gives seconds after 00:00:00 UTC January 1, 1981</dd><dt><span>valid_min :</span></dt><dd>-2147483645</dd><dt><span>valid_max :</span></dt><dd>2147483645</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>wind_speed</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;</div><input id='attrs-1edaeae4-2ec1-4c86-a81b-0e34eb6583e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1edaeae4-2ec1-4c86-a81b-0e34eb6583e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d9e8a80-002c-4b7e-bcbf-5f1f886dadc4' class='xr-var-data-in' type='checkbox'><label for='data-3d9e8a80-002c-4b7e-bcbf-5f1f886dadc4' 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>wind speed</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comment :</span></dt><dd>Typically represent surface winds (10 meters above the sea surface)</dd><dt><span>standard_name :</span></dt><dd>wind_speed</dd><dt><span>source :</span></dt><dd>ACCESSG-ABOM-Forecast-WSP</dd><dt><span>height :</span></dt><dd>10m</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd></dl></div><div class='xr-var-data'><table>\n",
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" <td> (3, 4500, 6000) </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed_dtime_from_sst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;</div><input id='attrs-96b21e69-d51b-4db7-a657-b5a61b0bb742' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96b21e69-d51b-4db7-a657-b5a61b0bb742' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39438e30-c346-4081-bc25-31e75980ccce' class='xr-var-data-in' type='checkbox'><label for='data-39438e30-c346-4081-bc25-31e75980ccce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time difference of wind speed measurement from sst measurement</dd><dt><span>units :</span></dt><dd>hour</dd><dt><span>comment :</span></dt><dd>The hours between the wind speed measurement and the SST observation</dd><dt><span>source :</span></dt><dd>ACCESSG-ABOM-Forecast-WSP</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</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>satellite_zenith_angle</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;</div><input id='attrs-a2eb4dab-45d3-482e-b884-74d687bc8496' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2eb4dab-45d3-482e-b884-74d687bc8496' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-833af17e-a146-4cdf-b11f-38fd0faa4d31' class='xr-var-data-in' type='checkbox'><label for='data-833af17e-a146-4cdf-b11f-38fd0faa4d31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>satellite zenith angle</dd><dt><span>units :</span></dt><dd>angular_degree</dd><dt><span>comment :</span></dt><dd>The satellite zenith angle at the time of the SST observations</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd></dl></div><div class='xr-var-data'><table>\n",
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" <td colspan=\"2\"> 12 chunks in 7 graph layers </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>l2p_flags</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1500, 2000), meta=np.ndarray&gt;</div><input id='attrs-d5b64fb2-2534-4fc3-b0b8-e19d5a95256f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5b64fb2-2534-4fc3-b0b8-e19d5a95256f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70a9eea6-84cf-4c61-993d-30c01233be56' class='xr-var-data-in' type='checkbox'><label for='data-70a9eea6-84cf-4c61-993d-30c01233be56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>L2P flags</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>32767</dd><dt><span>comment :</span></dt><dd>These flags are important to properly use the data. Data not flagged as microwave are sourced from an infrared sensor. The lake and river flags are currently not set, but defined in GDS2.0r4. The aerosol flag indicates high aerosol concentration. The analysis flag indicates high difference from analysis temperatures (differences greater than Analysis Limit). The lowwind flag indicates regions of low wind speed (typically less than the low Wind Limit) per NWP model. The highwind flag indicates regions of high wind speed (typically greater than the high Wind Limit) per NWP model. See wind limits in the comment field for the actual values. The edge flag indicates pixel sizes that are larger than Pixel Spread times the size of the pixel in the center of the field of view in either lat or lon direction. The terminator flag indicates that the sun is near the horizon. The reflector flag indicates that the satellite would receive direct reflected sunlight if the earth was a perfect mirror. The swath flag is used in gridded files to indicate if the pixel could have been seen by the satellite. delta_dn indicates that the day.night sst algorithm was different from the standard algorithm. Other flags may be populated and are for internal use and the definitions may change, so should not be relied on. Flags greater than 64 only apply to non-land pixels</dd><dt><span>flag_masks :</span></dt><dd>[ 1 2 4 8 16 32 64 128 256 512 1024 2048\n",
" 4096 8192 16384]</dd><dt><span>flag_meanings :</span></dt><dd>microwave land ice lake river reserved aerosol analysis lowwind highwind edge terminator reflector swath delta_dn</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>sses_bias</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;</div><input id='attrs-314d64d2-c302-4fb4-bc21-13db38e04055' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-314d64d2-c302-4fb4-bc21-13db38e04055' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b52dd403-a202-4a26-b87f-e1b16b475bcd' class='xr-var-data-in' type='checkbox'><label for='data-b52dd403-a202-4a26-b87f-e1b16b475bcd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>SSES bias estimate</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>comment :</span></dt><dd>Bias estimate derived from L2P bias per https://www.ghrsst.org/ghrsst/tags-and-wgs/stval-wg/sses-description-of-schemes/</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd></dl></div><div class='xr-var-data'><table>\n",
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" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
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" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 617.98 MiB </td>\n",
" <td> 51.50 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (3, 4500, 6000) </td>\n",
" <td> (1, 2250, 3000) </td>\n",
" </tr>\n",
" <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>sses_standard_deviation</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;</div><input id='attrs-c4ac8ab4-bfac-40a8-b8da-8cf5d12befcc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4ac8ab4-bfac-40a8-b8da-8cf5d12befcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c209c92a-4c7f-4dee-b38f-4e63f23dae6c' class='xr-var-data-in' type='checkbox'><label for='data-c209c92a-4c7f-4dee-b38f-4e63f23dae6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>SSES standard deviation estimate</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>comment :</span></dt><dd>Standard deviation estimate derived from L2P standard deviation per https://www.ghrsst.org/ghrsst/tags-and-wgs/stval-wg/sses-description-of-schemes/</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 617.98 MiB </td>\n",
" <td> 51.50 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (3, 4500, 6000) </td>\n",
" <td> (1, 2250, 3000) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 12 chunks in 7 graph layers </td>\n",
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" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sses_count</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;</div><input id='attrs-8760349a-deee-49e0-83d8-d39c36afe4e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8760349a-deee-49e0-83d8-d39c36afe4e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f860b71-c15a-4ecb-b902-79652b40bd23' class='xr-var-data-in' type='checkbox'><label for='data-2f860b71-c15a-4ecb-b902-79652b40bd23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>SSES count</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>Weighted representative number of swath pixels. EXPERIMENTAL_FIELD</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd></dl></div><div class='xr-var-data'><table>\n",
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" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
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" <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> 617.98 MiB </td>\n",
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" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (3, 4500, 6000) </td>\n",
" <td> (1, 2250, 3000) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 12 chunks in 7 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst_count</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;</div><input id='attrs-862d6454-a4a3-4b2b-a925-6b4dfa785f67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-862d6454-a4a3-4b2b-a925-6b4dfa785f67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a18b350b-2f61-4e3a-963d-bb26c820cbd2' class='xr-var-data-in' type='checkbox'><label for='data-a18b350b-2f61-4e3a-963d-bb26c820cbd2' 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>Number of SST measurements</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>Unweighted count of number of contributory SST measurements. EXPERIMENTAL_FIELD</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\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> 617.98 MiB </td>\n",
" <td> 51.50 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (3, 4500, 6000) </td>\n",
" <td> (1, 2250, 3000) </td>\n",
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" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 12 chunks in 7 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst_mean</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1500, 2000), meta=np.ndarray&gt;</div><input id='attrs-8a5ccc0f-a9f2-450e-a681-25061ca7e36a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a5ccc0f-a9f2-450e-a681-25061ca7e36a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4640719f-be80-403c-8ec5-454768f0c21e' class='xr-var-data-in' type='checkbox'><label for='data-4640719f-be80-403c-8ec5-454768f0c21e' 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>Unweighted SST mean</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>comment :</span></dt><dd>Unweighted mean of contributory SST measurements. EXPERIMENTAL_FIELD</dd><dt><span>valid_min :</span></dt><dd>-32767</dd><dt><span>valid_max :</span></dt><dd>32767</dd></dl></div><div class='xr-var-data'><table>\n",
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" <td> (3, 4500, 6000) </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst_standard_deviation</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2250, 3000), meta=np.ndarray&gt;</div><input id='attrs-69903573-184e-47e7-ab9c-8eb4772b2d03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69903573-184e-47e7-ab9c-8eb4772b2d03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e20df31-85f9-4506-9892-8aac11e0262b' class='xr-var-data-in' type='checkbox'><label for='data-9e20df31-85f9-4506-9892-8aac11e0262b' 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>Unweighted SST standard deviation</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-127</dd><dt><span>valid_max :</span></dt><dd>127</dd></dl></div><div class='xr-var-data'><table>\n",
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" <td> 617.98 MiB </td>\n",
" <td> 51.50 MiB </td>\n",
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" <th> Shape </th>\n",
" <td> (3, 4500, 6000) </td>\n",
" <td> (1, 2250, 3000) </td>\n",
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" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 12 chunks in 7 graph layers </td>\n",
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" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9ac7dc0a-9213-493e-bbb1-bf0db648b1e1' class='xr-section-summary-in' type='checkbox' ><label for='section-9ac7dc0a-9213-493e-bbb1-bf0db648b1e1' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-621f00f6-7e68-4316-a21e-13c1da95f6a5' class='xr-index-data-in' type='checkbox'/><label for='index-621f00f6-7e68-4316-a21e-13c1da95f6a5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([19.989999771118164, 19.969999313354492, 19.950000762939453,\n",
" 19.93000030517578, 19.90999984741211, 19.889999389648438,\n",
" 19.8700008392334, 19.850000381469727, 19.829999923706055,\n",
" 19.809999465942383,\n",
" ...\n",
" -69.80999755859375, -69.83000183105469, -69.8499984741211,\n",
" -69.87000274658203, -69.88999938964844, -69.91000366210938,\n",
" -69.93000030517578, -69.94999694824219, -69.97000122070312,\n",
" -69.98999786376953],\n",
" dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=4500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-36829f54-5325-4f5e-9c62-c397d5ba44fa' class='xr-index-data-in' type='checkbox'/><label for='index-36829f54-5325-4f5e-9c62-c397d5ba44fa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 70.01000213623047, 70.02999877929688, 70.05000305175781,\n",
" 70.06999969482422, 70.08999633789062, 70.11000061035156,\n",
" 70.12999725341797, 70.1500015258789, 70.16999816894531,\n",
" 70.19000244140625,\n",
" ...\n",
" 189.80999755859375, 189.8300018310547, 189.85000610351562,\n",
" 189.8699951171875, 189.88999938964844, 189.91000366210938,\n",
" 189.92999267578125, 189.9499969482422, 189.97000122070312,\n",
" 189.99000549316406],\n",
" dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=6000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9031c1a0-617d-43ad-ba8c-43f145afd247' class='xr-index-data-in' type='checkbox'/><label for='index-9031c1a0-617d-43ad-ba8c-43f145afd247' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([2023-06-29 09:20:00, 2023-06-30 09:20:00, 2023-07-01 09:20:00],\n",
" dtype=&#x27;object&#x27;, length=3, calendar=&#x27;standard&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a1267c52-4c1a-4a1a-b0e6-0e6f67febdd6' class='xr-section-summary-in' type='checkbox' ><label for='section-a1267c52-4c1a-4a1a-b0e6-0e6f67febdd6' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>IMOS L3S Day and Night gridded multiple-sensor multiple-swath Australian region foundation SST</dd><dt><span>summary :</span></dt><dd>Skin SST retrievals produced from stitching together High Resolution Picture Transmission direct broadcast data from a NOAA polar-orbiting satellite received at Australian receiving stations.</dd><dt><span>references :</span></dt><dd>http://imos.org.au/sstproducts.html and Griffin et al. (2017) at http://imos.org.au/facilities/srs/sstproducts/sstdata0/sstdata-references</dd><dt><span>institution :</span></dt><dd>ABOM</dd><dt><span>comment :</span></dt><dd>HRPT AVHRR experimental L3 retrieval produced by the Australian Bureau of Meteorology as a contribution to the Integrated Marine Observing System. SSTs were calibrated to drifting buoy depths (~20-30cm) followed by a cool skin correction of -0.17K to convert to a skin (~10 micron) SST. SSTs are a weighted average of the SSTs of contributing pixels (weighted by sses_standard_deviation^-2).\\nWARNING: some applications are unable to properly handle signed byte values. If byte values &gt;127 are encountered, subtract 256 from this reported value. GRID:CONTINENTAL, SYSCODE:PRODUCTION</dd><dt><span>license :</span></dt><dd>GHRSST protocol describes data use as free and open</dd><dt><span>id :</span></dt><dd>AVHRR_D-ABOM-L3S-v01.0</dd><dt><span>naming_authority :</span></dt><dd>org.ghrsst</dd><dt><span>product_version :</span></dt><dd>01.0</dd><dt><span>uuid :</span></dt><dd>572220fa-f804-42ca-ab2c-0e528724ab5e</dd><dt><span>gds_version_id :</span></dt><dd>2.0r4</dd><dt><span>netcdf_version_id :</span></dt><dd>4.3.3.1</dd><dt><span>date_created :</span></dt><dd>20230702T023344Z</dd><dt><span>file_quality_level :</span></dt><dd>3</dd><dt><span>spatial_resolution :</span></dt><dd>0.02 deg</dd><dt><span>start_time :</span></dt><dd>20230628T222528Z</dd><dt><span>time_coverage_start :</span></dt><dd>20230628T222528Z</dd><dt><span>stop_time :</span></dt><dd>20230630T000253Z</dd><dt><span>time_coverage_end :</span></dt><dd>20230630T000253Z</dd><dt><span>northernmost_latitude :</span></dt><dd>19.99</dd><dt><span>southernmost_latitude :</span></dt><dd>-69.99</dd><dt><span>easternmost_longitude :</span></dt><dd>-170.01</dd><dt><span>westernmost_longitude :</span></dt><dd>70.01</dd><dt><span>source :</span></dt><dd>wind_source=ACCESSG-ABOM-Forecast-WSP,analysis_source=ABOM-L4LRfnd-GLOB-GAMSSA_28km,adi_source=unknown,ice_source=SSMI-NCEP-Analysis-ICE-5min,l3_source=AVHRR18_D-ABOM-L3C-v01.0</dd><dt><span>platform :</span></dt><dd>NOAA-18</dd><dt><span>sensor :</span></dt><dd>VIIRS,AVHRR</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>Metadata_Link :</span></dt><dd>TBA</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.02</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.02</dd><dt><span>creator_name :</span></dt><dd>Australian Bureau of Meteorology</dd><dt><span>creator_email :</span></dt><dd>ghrsst@bom.gov.au</dd><dt><span>project :</span></dt><dd>Group for High Resolution Sea Surface Temperature</dd><dt><span>publisher_name :</span></dt><dd>The GHRSST Project Office</dd><dt><span>publisher_email :</span></dt><dd>gpa@ghrsst.org</dd><dt><span>publisher_url :</span></dt><dd>http://www.ghrsst.org</dd><dt><span>processing_level :</span></dt><dd>L3S</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>history :</span></dt><dd>platform_counts=NOAA-18=2,quality_counts=archive=2,platform=NOAA-18,quality_source=archive,ice_source=SSMI-NCEP-Analysis-ICE-5min,adi_source=unknown,wind_source=ACCESSG-ABOM-Forecast-WSP,analysis_source=ABOM-L4LRfnd-GLOB-GAMSSA_28km,source_file=20230629152000-ABOM-L3C_GHRSST-SSTskin-AVHRR18_D-1d_night-v02.0-fv01.0.nc;20230629032000-ABOM-L3C_GHRSST-SSTskin-AVHRR18_D-1d_day-v02.0-fv01.0.nc,l3_file=20230629152000-ABOM-L3C_GHRSST-SSTskin-AVHRR18_D-1d_night-v02.0-fv01.0.nc;20230629032000-ABOM-L3C_GHRSST-SSTskin-AVHRR18_D-1d_day-v02.0-fv01.0.nc,l3_source=AVHRR18_D-ABOM-L3C-v01.0,global_source=wind_source=ACCESSG-ABOM-Forecast-WSP,analysis_source=ABOM-L4LRfnd-GLOB-GAMSSA_28km,adi_source=unknown,ice_source=SSMI-NCEP-Analysis-ICE-5min,l3_source=AVHRR18_D-ABOM-L3C-v01.0,landmask_file=lsmask.dist5.5.nc,landmask_reference=Naval Oceanographic Office (NAVOCEANO),landmask_URL=https://www.ghrsst.org/data/ghrsst-data-tools/navo-ghrsst-pp-land-sea-mask/,landmask_source=NAVOCEANO 1km Version 5.5,ice_reference=US National Weather Service - NCEP,ice_URL=http://polar.ncep.noaa.gov/seaice/Analyses.html,ice_file=20230628.ice_data.5min.nc,ice_jdate=2460124,merge_tool=mergeL3U,mergeL3U_version=9042,quality=archive,mergeL3U_quality=archive</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>creator_url :</span></dt><dd>http://www.imos.org.au/srs.html</dd><dt><span>acknowledgment :</span></dt><dd>Any use of these data requires the following acknowledgment:HRPT AVHRR SSTskin retrievals were produced by the Australian Bureau of Meteorology as a contribution to the Integrated Marine Observing System - an initiative of the Australian Government being conducted as part of the National Collaborative Research Infrastructure Strategy and the Super Science Initiative. The imagery data were acquired from NOAA spacecraft by the Bureau, Australian Institute of Marine Science, Australian Commonwealth Scientific and Industrial Research Organization, Geoscience Australia, and Western Australian Satellite Technology and Applications Consortium.</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset> Size: 8GB\n",
"Dimensions: (time: 3, lat: 4500, lon: 6000)\n",
"Coordinates:\n",
" * lat (lat) float32 18kB 19.99 19.97 ... -69.97 -69.99\n",
" * lon (lon) float32 24kB 70.01 70.03 ... 190.0 190.0\n",
" * time (time) object 24B 2023-06-29 09:20:00 ... 2023...\n",
"Data variables: (12/14)\n",
" sea_surface_temperature (time, lat, lon) float64 648MB dask.array<chunksize=(1, 1500, 2000), meta=np.ndarray>\n",
" sst_dtime (time, lat, lon) float64 648MB dask.array<chunksize=(1, 1500, 2000), meta=np.ndarray>\n",
" dt_analysis (time, lat, lon) float64 648MB dask.array<chunksize=(1, 2250, 3000), meta=np.ndarray>\n",
" wind_speed (time, lat, lon) float64 648MB dask.array<chunksize=(1, 2250, 3000), meta=np.ndarray>\n",
" wind_speed_dtime_from_sst (time, lat, lon) float64 648MB dask.array<chunksize=(1, 2250, 3000), meta=np.ndarray>\n",
" satellite_zenith_angle (time, lat, lon) float64 648MB dask.array<chunksize=(1, 2250, 3000), meta=np.ndarray>\n",
" ... ...\n",
" sses_bias (time, lat, lon) float64 648MB dask.array<chunksize=(1, 2250, 3000), meta=np.ndarray>\n",
" sses_standard_deviation (time, lat, lon) float64 648MB dask.array<chunksize=(1, 2250, 3000), meta=np.ndarray>\n",
" sses_count (time, lat, lon) float64 648MB dask.array<chunksize=(1, 2250, 3000), meta=np.ndarray>\n",
" sst_count (time, lat, lon) float64 648MB dask.array<chunksize=(1, 2250, 3000), meta=np.ndarray>\n",
" sst_mean (time, lat, lon) float64 648MB dask.array<chunksize=(1, 1500, 2000), meta=np.ndarray>\n",
" sst_standard_deviation (time, lat, lon) float64 648MB dask.array<chunksize=(1, 2250, 3000), meta=np.ndarray>\n",
"Attributes: (12/47)\n",
" title: IMOS L3S Day and Night gridded multiple-senso...\n",
" summary: Skin SST retrievals produced from stitching t...\n",
" references: http://imos.org.au/sstproducts.html and Griff...\n",
" institution: ABOM\n",
" comment: HRPT AVHRR experimental L3 retrieval produced...\n",
" license: GHRSST protocol describes data use as free an...\n",
" ... ...\n",
" processing_level: L3S\n",
" cdm_data_type: grid\n",
" history: platform_counts=NOAA-18=2,quality_counts=arch...\n",
" Conventions: CF-1.6\n",
" creator_url: http://www.imos.org.au/srs.html\n",
" acknowledgment: Any use of these data requires the following ..."
]
},
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],
"source": [
"# running the same code on multiple good files works\n",
"\n",
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" subset[1:4],\n",
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" concat_characters=True,\n",
" mask_and_scale=True,\n",
" decode_cf=True,\n",
" decode_times=True,\n",
" use_cftime=True,\n",
" parallel=True,\n",
" decode_coords=True,\n",
" compat=\"equals\",\n",
" lock=False\n",
")\n",
"ds"
]
},
{
"cell_type": "code",
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"id": "22506562-615a-4827-9635-3efd3a090b01",
"metadata": {},
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