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swnesbitt/grib_test.ipynb

Created August 2, 2022 18:48
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grib_test.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import cfgrib\n",
"import xarray as xr\n",
"import matplotlib.style as style\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load only the relative humidity and vertical velocity fields separately, since they have fewer numbers of vertical levels than other variables in the 'isobaricInhPa' 'typeOfLevel'"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"skipping variable: paramId==260131 shortName='o3mr'\n",
"Traceback (most recent call last):\n",
" File \"/Users/snesbitt/miniforge3/envs/py3/lib/python3.9/site-packages/cfgrib/dataset.py\", line 660, in build_dataset_components\n",
" dict_merge(variables, coord_vars)\n",
" File \"/Users/snesbitt/miniforge3/envs/py3/lib/python3.9/site-packages/cfgrib/dataset.py\", line 591, in dict_merge\n",
" raise DatasetBuildError(\n",
"cfgrib.dataset.DatasetBuildError: key present and new value is different: key='isobaricInhPa' value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,\n",
" 650., 600., 550., 500., 450., 400., 350., 300., 250.,\n",
" 200., 150., 100., 70., 50., 30., 20., 10.])) new_value=Variable(dimensions=('isobaricInhPa',), data=array([100., 70., 50., 30., 20., 10.]))\n",
"skipping variable: paramId==157 shortName='r'\n",
"Traceback (most recent call last):\n",
" File \"/Users/snesbitt/miniforge3/envs/py3/lib/python3.9/site-packages/cfgrib/dataset.py\", line 660, in build_dataset_components\n",
" dict_merge(variables, coord_vars)\n",
" File \"/Users/snesbitt/miniforge3/envs/py3/lib/python3.9/site-packages/cfgrib/dataset.py\", line 591, in dict_merge\n",
" raise DatasetBuildError(\n",
"cfgrib.dataset.DatasetBuildError: key present and new value is different: key='isobaricInhPa' value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,\n",
" 650., 600., 550., 500., 450., 400., 350., 300., 250.,\n",
" 200., 150., 100., 70., 50., 30., 20., 10.])) new_value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,\n",
" 650., 600., 550., 500., 450., 400., 350., 300., 250.,\n",
" 200., 150., 100.]))\n",
"skipping variable: paramId==135 shortName='w'\n",
"Traceback (most recent call last):\n",
" File \"/Users/snesbitt/miniforge3/envs/py3/lib/python3.9/site-packages/cfgrib/dataset.py\", line 660, in build_dataset_components\n",
" dict_merge(variables, coord_vars)\n",
" File \"/Users/snesbitt/miniforge3/envs/py3/lib/python3.9/site-packages/cfgrib/dataset.py\", line 591, in dict_merge\n",
" raise DatasetBuildError(\n",
"cfgrib.dataset.DatasetBuildError: key present and new value is different: key='isobaricInhPa' value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,\n",
" 650., 600., 550., 500., 450., 400., 350., 300., 250.,\n",
" 200., 150., 100., 70., 50., 30., 20., 10.])) new_value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,\n",
" 650., 600., 550., 500., 450., 400., 350., 300., 250.,\n",
" 200., 150., 100.]))\n",
"skipping variable: paramId==260018 shortName='clwmr'\n",
"Traceback (most recent call last):\n",
" File \"/Users/snesbitt/miniforge3/envs/py3/lib/python3.9/site-packages/cfgrib/dataset.py\", line 660, in build_dataset_components\n",
" dict_merge(variables, coord_vars)\n",
" File \"/Users/snesbitt/miniforge3/envs/py3/lib/python3.9/site-packages/cfgrib/dataset.py\", line 591, in dict_merge\n",
" raise DatasetBuildError(\n",
"cfgrib.dataset.DatasetBuildError: key present and new value is different: key='isobaricInhPa' value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,\n",
" 650., 600., 550., 500., 450., 400., 350., 300., 250.,\n",
" 200., 150., 100., 70., 50., 30., 20., 10.])) new_value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,\n",
" 650., 600., 550., 500., 450., 400., 350., 300., 250.,\n",
" 200., 150., 100.]))\n",
"skipping variable: paramId==260080 shortName='5wavh'\n",
"Traceback (most recent call last):\n",
" File \"/Users/snesbitt/miniforge3/envs/py3/lib/python3.9/site-packages/cfgrib/dataset.py\", line 660, in build_dataset_components\n",
" dict_merge(variables, coord_vars)\n",
" File \"/Users/snesbitt/miniforge3/envs/py3/lib/python3.9/site-packages/cfgrib/dataset.py\", line 591, in dict_merge\n",
" raise DatasetBuildError(\n",
"cfgrib.dataset.DatasetBuildError: key present and new value is different: key='isobaricInhPa' value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,\n",
" 650., 600., 550., 500., 450., 400., 350., 300., 250.,\n",
" 200., 150., 100., 70., 50., 30., 20., 10.])) new_value=Variable(dimensions=(), data=500.0)\n",
"skipping variable: paramId==3027 shortName='gpa'\n",
"Traceback (most recent call last):\n",
" File \"/Users/snesbitt/miniforge3/envs/py3/lib/python3.9/site-packages/cfgrib/dataset.py\", line 660, in build_dataset_components\n",
" dict_merge(variables, coord_vars)\n",
" File \"/Users/snesbitt/miniforge3/envs/py3/lib/python3.9/site-packages/cfgrib/dataset.py\", line 591, in dict_merge\n",
" raise DatasetBuildError(\n",
"cfgrib.dataset.DatasetBuildError: key present and new value is different: key='isobaricInhPa' value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,\n",
" 650., 600., 550., 500., 450., 400., 350., 300., 250.,\n",
" 200., 150., 100., 70., 50., 30., 20., 10.])) new_value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 500.]))\n",
"skipping variable: paramId==260084 shortName='5wava'\n",
"Traceback (most recent call last):\n",
" File \"/Users/snesbitt/miniforge3/envs/py3/lib/python3.9/site-packages/cfgrib/dataset.py\", line 660, in build_dataset_components\n",
" dict_merge(variables, coord_vars)\n",
" File \"/Users/snesbitt/miniforge3/envs/py3/lib/python3.9/site-packages/cfgrib/dataset.py\", line 591, in dict_merge\n",
" raise DatasetBuildError(\n",
"cfgrib.dataset.DatasetBuildError: key present and new value is different: key='isobaricInhPa' value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,\n",
" 650., 600., 550., 500., 450., 400., 350., 300., 250.,\n",
" 200., 150., 100., 70., 50., 30., 20., 10.])) new_value=Variable(dimensions=(), data=500.0)\n"
]
}
],
"source": [
"ds_r = xr.open_dataset('/Users/snesbitt/Downloads/fnl_20130324_06_00.grib2', \n",
" filter_by_keys={'typeOfLevel': 'isobaricInhPa', 'shortName':'r'}, engine='cfgrib')\n",
"ds_w = xr.open_dataset('/Users/snesbitt/Downloads/fnl_20130324_06_00.grib2', \n",
" filter_by_keys={'typeOfLevel': 'isobaricInhPa', 'shortName':'w'}, engine='cfgrib')\n",
"ds = xr.open_dataset('/Users/snesbitt/Downloads/fnl_20130324_06_00.grib2', \n",
" filter_by_keys={'typeOfLevel': 'isobaricInhPa'}, engine='cfgrib')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (isobaricInhPa: 21, latitude: 181, longitude: 360)\n",
"Coordinates:\n",
" time datetime64[ns] 2013-03-24T06:00:00\n",
" step timedelta64[ns] 00:00:00\n",
" * isobaricInhPa (isobaricInhPa) float64 1e+03 975.0 950.0 ... 150.0 100.0\n",
" * latitude (latitude) float64 90.0 89.0 88.0 87.0 ... -88.0 -89.0 -90.0\n",
" * longitude (longitude) float64 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
" valid_time datetime64[ns] 2013-03-24T06:00:00\n",
"Data variables:\n",
" w (isobaricInhPa, latitude, longitude) float32 ...\n",
"Attributes:\n",
" GRIB_edition: 2\n",
" GRIB_centre: kwbc\n",
" GRIB_centreDescription: US National Weather Service - NCEP\n",
" GRIB_subCentre: 0\n",
" Conventions: CF-1.7\n",
" institution: US National Weather Service - NCEP\n",
" history: 2022-08-02T13:43 GRIB to CDM+CF via cfgrib-0.9.1...</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-503a4a6a-4790-4a75-a47b-ea491b0cf688' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-503a4a6a-4790-4a75-a47b-ea491b0cf688' 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'>isobaricInhPa</span>: 21</li><li><span class='xr-has-index'>latitude</span>: 181</li><li><span class='xr-has-index'>longitude</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-73628998-1724-4bf8-a198-182a43d655aa' class='xr-section-summary-in' type='checkbox' checked><label for='section-73628998-1724-4bf8-a198-182a43d655aa' 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>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1f8521dc-0b34-4187-9254-5ad736842b9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f8521dc-0b34-4187-9254-5ad736842b9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bc504ee-bdff-441b-97da-1fcea8bd4f8f' class='xr-var-data-in' type='checkbox'><label for='data-8bc504ee-bdff-441b-97da-1fcea8bd4f8f' 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'><pre>array(&#x27;2013-03-24T06:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></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-a52effc9-7f0b-4c12-8b71-c6f0512eaa6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a52effc9-7f0b-4c12-8b71-c6f0512eaa6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52914f80-2418-4d6b-b376-54ffe556bdf7' class='xr-var-data-in' type='checkbox'><label for='data-52914f80-2418-4d6b-b376-54ffe556bdf7' 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(0, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>isobaricInhPa</span></div><div class='xr-var-dims'>(isobaricInhPa)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 975.0 950.0 ... 150.0 100.0</div><input id='attrs-de99b8f7-9757-4919-97b7-3d97de4e9fad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de99b8f7-9757-4919-97b7-3d97de4e9fad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97d0fde4-42c3-45e5-8b1b-dcc1b9eb0cbe' class='xr-var-data-in' type='checkbox'><label for='data-97d0fde4-42c3-45e5-8b1b-dcc1b9eb0cbe' 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>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([1000., 975., 950., 925., 900., 850., 800., 750., 700., 650.,\n",
" 600., 550., 500., 450., 400., 350., 300., 250., 200., 150.,\n",
" 100.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.0 88.0 ... -89.0 -90.0</div><input id='attrs-c4587ec9-6a06-46c1-9b6b-157ab359913c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4587ec9-6a06-46c1-9b6b-157ab359913c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a4c7a72-4fdc-450e-8117-a575b1106eae' class='xr-var-data-in' type='checkbox'><label for='data-9a4c7a72-4fdc-450e-8117-a575b1106eae' 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>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd></dl></div><div class='xr-var-data'><pre>array([ 90., 89., 88., 87., 86., 85., 84., 83., 82., 81., 80., 79.,\n",
" 78., 77., 76., 75., 74., 73., 72., 71., 70., 69., 68., 67.,\n",
" 66., 65., 64., 63., 62., 61., 60., 59., 58., 57., 56., 55.,\n",
" 54., 53., 52., 51., 50., 49., 48., 47., 46., 45., 44., 43.,\n",
" 42., 41., 40., 39., 38., 37., 36., 35., 34., 33., 32., 31.,\n",
" 30., 29., 28., 27., 26., 25., 24., 23., 22., 21., 20., 19.,\n",
" 18., 17., 16., 15., 14., 13., 12., 11., 10., 9., 8., 7.,\n",
" 6., 5., 4., 3., 2., 1., 0., -1., -2., -3., -4., -5.,\n",
" -6., -7., -8., -9., -10., -11., -12., -13., -14., -15., -16., -17.,\n",
" -18., -19., -20., -21., -22., -23., -24., -25., -26., -27., -28., -29.,\n",
" -30., -31., -32., -33., -34., -35., -36., -37., -38., -39., -40., -41.,\n",
" -42., -43., -44., -45., -46., -47., -48., -49., -50., -51., -52., -53.,\n",
" -54., -55., -56., -57., -58., -59., -60., -61., -62., -63., -64., -65.,\n",
" -66., -67., -68., -69., -70., -71., -72., -73., -74., -75., -76., -77.,\n",
" -78., -79., -80., -81., -82., -83., -84., -85., -86., -87., -88., -89.,\n",
" -90.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 357.0 358.0 359.0</div><input id='attrs-c517a185-2523-46f1-85f1-534b23c272f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c517a185-2523-46f1-85f1-534b23c272f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4de7147-edc3-4662-88aa-164391cede34' class='xr-var-data-in' type='checkbox'><label for='data-d4de7147-edc3-4662-88aa-164391cede34' 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>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([ 0., 1., 2., ..., 357., 358., 359.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-695801ee-aedf-4f0f-b941-c591d2de54c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-695801ee-aedf-4f0f-b941-c591d2de54c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7976880-6ea8-43e6-a818-0202925a3f86' class='xr-var-data-in' type='checkbox'><label for='data-b7976880-6ea8-43e6-a818-0202925a3f86' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2013-03-24T06:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-00ab08ac-c918-4f23-b833-caefec71b44e' class='xr-section-summary-in' type='checkbox' checked><label for='section-00ab08ac-c918-4f23-b833-caefec71b44e' class='xr-section-summary' >Data variables: <span>(1)</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>w</span></div><div class='xr-var-dims'>(isobaricInhPa, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7f05dd60-bfc5-4bc3-b6b9-994ea690dcb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f05dd60-bfc5-4bc3-b6b9-994ea690dcb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dd0a49d-373d-49b1-a132-11649c25d598' class='xr-var-data-in' type='checkbox'><label for='data-8dd0a49d-373d-49b1-a132-11649c25d598' 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_paramId :</span></dt><dd>135</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>65160</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>360</dd><dt><span>GRIB_Ny :</span></dt><dd>181</dd><dt><span>GRIB_cfName :</span></dt><dd>lagrangian_tendency_of_air_pressure</dd><dt><span>GRIB_cfVarName :</span></dt><dd>w</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/longitude. Also called equidistant cylindrical, or Plate Carree</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>1.0</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>1.0</dd><dt><span>GRIB_jPointsAreConsecutive :</span></dt><dd>0</dd><dt><span>GRIB_jScansPositively :</span></dt><dd>0</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>90.0</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-90.0</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.0</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Vertical velocity</dd><dt><span>GRIB_shortName :</span></dt><dd>w</dd><dt><span>GRIB_units :</span></dt><dd>Pa s**-1</dd><dt><span>long_name :</span></dt><dd>Vertical velocity</dd><dt><span>units :</span></dt><dd>Pa s**-1</dd><dt><span>standard_name :</span></dt><dd>lagrangian_tendency_of_air_pressure</dd></dl></div><div class='xr-var-data'><pre>[1368360 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-96768a9b-9ad5-4e42-bbe8-e1128bfb88e6' class='xr-section-summary-in' type='checkbox' checked><label for='section-96768a9b-9ad5-4e42-bbe8-e1128bfb88e6' 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>GRIB_edition :</span></dt><dd>2</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_subCentre :</span></dt><dd>0</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>institution :</span></dt><dd>US National Weather Service - NCEP</dd><dt><span>history :</span></dt><dd>2022-08-02T13:43 GRIB to CDM+CF via cfgrib-0.9.10.1/ecCodes-2.26.0 with {&quot;source&quot;: &quot;../../../Downloads/fnl_20130324_06_00.grib2&quot;, &quot;filter_by_keys&quot;: {&quot;typeOfLevel&quot;: &quot;isobaricInhPa&quot;, &quot;shortName&quot;: &quot;w&quot;}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (isobaricInhPa: 21, latitude: 181, longitude: 360)\n",
"Coordinates:\n",
" time datetime64[ns] ...\n",
" step timedelta64[ns] ...\n",
" * isobaricInhPa (isobaricInhPa) float64 1e+03 975.0 950.0 ... 150.0 100.0\n",
" * latitude (latitude) float64 90.0 89.0 88.0 87.0 ... -88.0 -89.0 -90.0\n",
" * longitude (longitude) float64 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
" valid_time datetime64[ns] ...\n",
"Data variables:\n",
" w (isobaricInhPa, latitude, longitude) float32 ...\n",
"Attributes:\n",
" GRIB_edition: 2\n",
" GRIB_centre: kwbc\n",
" GRIB_centreDescription: US National Weather Service - NCEP\n",
" GRIB_subCentre: 0\n",
" Conventions: CF-1.7\n",
" institution: US National Weather Service - NCEP\n",
" history: 2022-08-02T13:43 GRIB to CDM+CF via cfgrib-0.9.1..."
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_w"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ds_r['r'].sel(latitude=20).plot(cmap=sns.color_palette(\"icefire\", as_cmap=True))\n",
"plt.gca().invert_yaxis()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.7 ('py3')",
"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": "3.9.7"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "7a6b41cc1ffbe7f6292ade58ee9ab0c89bd7fa770a77f101cc95d5710b4e5fa9"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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