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Created June 8, 2023 09:08
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regrid_cmip_to_jra(1).ipynb
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"id": "30f85c09-63da-4030-8c91-9ec69763ad3f",
"metadata": {
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"source": [
"import xarray\n",
"import xesmf"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1d72f175-567f-4c92-8631-346c50051742",
"metadata": {
"tags": []
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"outputs": [],
"source": [
"import intake"
]
},
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"cell_type": "markdown",
"id": "edd97f29-5242-4813-ab2f-51a3fb072fff",
"metadata": {
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"source": [
"# Input data"
]
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"execution_count": 3,
"id": "d610f1e4-0535-4dfa-9bf4-a1e5d2d3bbbb",
"metadata": {
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"outputs": [],
"source": [
"cat = intake.cat.nci.esgf.cmip6.search(source_id='ACCESS-ESM1-5', experiment_id='ssp585', member_id='r1i1p1f1', table_id='Amon', variable_id='tas')"
]
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"id": "92f40668-3d5d-435c-8a09-52c2f23b3353",
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" project activity_id institution_id source_id experiment_id member_id \\\n",
"0 CMIP6 ScenarioMIP CSIRO ACCESS-ESM1-5 ssp585 r1i1p1f1 \n",
"1 CMIP6 ScenarioMIP CSIRO ACCESS-ESM1-5 ssp585 r1i1p1f1 \n",
"\n",
" table_id variable_id grid_label date_range \\\n",
"0 Amon tas gn 201501-210012 \n",
"1 Amon tas gn 210101-230012 \n",
"\n",
" path version \n",
"0 /g/data/fs38/publications/CMIP6/ScenarioMIP/CS... v20210318 \n",
"1 /g/data/fs38/publications/CMIP6/ScenarioMIP/CS... v20210318 "
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"execution_count": 4,
"metadata": {},
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"source": [
"cat.df"
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"\n",
"--> The keys in the returned dictionary of datasets are constructed as follows:\n",
"\t'project.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'\n"
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"tas_dict = cat.to_dataset_dict(xarray_open_kwargs={'use_cftime':True})\n",
"\n",
"tas_ds = list(tas_dict.values())[0]"
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (time: 3432, bnds: 2, lat: 145, lon: 192)\n",
"Coordinates:\n",
" * time (time) object 2015-01-16 12:00:00 ... 2300-12-16 12:00:00\n",
" time_bnds (time, bnds) object dask.array&lt;chunksize=(1032, 2), meta=np.ndarray&gt;\n",
" * lat (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n",
" lat_bnds (lat, bnds) float64 dask.array&lt;chunksize=(145, 2), meta=np.ndarray&gt;\n",
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"Attributes: (12/53)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: ScenarioMIP\n",
" branch_method: standard\n",
" branch_time_in_child: 60265.0\n",
" branch_time_in_parent: 60265.0\n",
" data_specs_version: 01.00.30\n",
" ... ...\n",
" intake_esm_attrs:table_id: Amon\n",
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" intake_esm_attrs:version: v20210318\n",
" intake_esm_attrs:_data_format_: netcdf\n",
" intake_esm_dataset_key: CMIP6.ScenarioMIP.CSIRO.ACCESS-ESM1-5.s...</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-ed17b76e-aca3-4751-b8d1-3e4f9dd355f6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ed17b76e-aca3-4751-b8d1-3e4f9dd355f6' 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>: 3432</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ba089053-1b78-4cd3-828d-ad2a0dd54fc8' class='xr-section-summary-in' type='checkbox' checked><label for='section-ba089053-1b78-4cd3-828d-ad2a0dd54fc8' class='xr-section-summary' >Coordinates: <span>(7)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2015-01-16 12:00:00 ... 2300-12-...</div><input id='attrs-8acda20a-0f41-4f99-8b38-bd3960789916' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8acda20a-0f41-4f99-8b38-bd3960789916' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcfe812f-7d5f-407a-838d-17e788c0ef13' class='xr-var-data-in' type='checkbox'><label for='data-dcfe812f-7d5f-407a-838d-17e788c0ef13' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><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([cftime.DatetimeProlepticGregorian(2015, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeProlepticGregorian(2015, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeProlepticGregorian(2015, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
" ...,\n",
" cftime.DatetimeProlepticGregorian(2300, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeProlepticGregorian(2300, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
" cftime.DatetimeProlepticGregorian(2300, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
" dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1032, 2), meta=np.ndarray&gt;</div><input id='attrs-b6b11a2b-dd04-4315-b70e-cbc3ad793409' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b6b11a2b-dd04-4315-b70e-cbc3ad793409' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c5836c1-8a9b-4e24-985c-69b0b95933e7' class='xr-var-data-in' type='checkbox'><label for='data-8c5836c1-8a9b-4e24-985c-69b0b95933e7' 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'></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> 53.62 kiB </td>\n",
" <td> 37.50 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (3432, 2) </td>\n",
" <td> (2400, 2) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> object numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\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 class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-d60cb354-b7fd-4e41-b2fe-a3912b2731be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d60cb354-b7fd-4e41-b2fe-a3912b2731be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a90b044f-d562-457b-b3c1-a859a96d0f25' class='xr-var-data-in' type='checkbox'><label for='data-a90b044f-d562-457b-b3c1-a859a96d0f25' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90. , -88.75, -87.5 , -86.25, -85. , -83.75, -82.5 , -81.25, -80. ,\n",
" -78.75, -77.5 , -76.25, -75. , -73.75, -72.5 , -71.25, -70. , -68.75,\n",
" -67.5 , -66.25, -65. , -63.75, -62.5 , -61.25, -60. , -58.75, -57.5 ,\n",
" -56.25, -55. , -53.75, -52.5 , -51.25, -50. , -48.75, -47.5 , -46.25,\n",
" -45. , -43.75, -42.5 , -41.25, -40. , -38.75, -37.5 , -36.25, -35. ,\n",
" -33.75, -32.5 , -31.25, -30. , -28.75, -27.5 , -26.25, -25. , -23.75,\n",
" -22.5 , -21.25, -20. , -18.75, -17.5 , -16.25, -15. , -13.75, -12.5 ,\n",
" -11.25, -10. , -8.75, -7.5 , -6.25, -5. , -3.75, -2.5 , -1.25,\n",
" 0. , 1.25, 2.5 , 3.75, 5. , 6.25, 7.5 , 8.75, 10. ,\n",
" 11.25, 12.5 , 13.75, 15. , 16.25, 17.5 , 18.75, 20. , 21.25,\n",
" 22.5 , 23.75, 25. , 26.25, 27.5 , 28.75, 30. , 31.25, 32.5 ,\n",
" 33.75, 35. , 36.25, 37.5 , 38.75, 40. , 41.25, 42.5 , 43.75,\n",
" 45. , 46.25, 47.5 , 48.75, 50. , 51.25, 52.5 , 53.75, 55. ,\n",
" 56.25, 57.5 , 58.75, 60. , 61.25, 62.5 , 63.75, 65. , 66.25,\n",
" 67.5 , 68.75, 70. , 71.25, 72.5 , 73.75, 75. , 76.25, 77.5 ,\n",
" 78.75, 80. , 81.25, 82.5 , 83.75, 85. , 86.25, 87.5 , 88.75,\n",
" 90. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(145, 2), meta=np.ndarray&gt;</div><input id='attrs-9f5d02d8-9c24-4bb7-a60d-ecd55aa1994c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f5d02d8-9c24-4bb7-a60d-ecd55aa1994c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-824e97d7-ed92-460c-b4ad-4074505d97e0' class='xr-var-data-in' type='checkbox'><label for='data-824e97d7-ed92-460c-b4ad-4074505d97e0' 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'></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> 2.27 kiB </td>\n",
" <td> 2.27 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (145, 2) </td>\n",
" <td> (145, 2) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 1 chunks in 5 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"77\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\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 class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-e7b25bfb-7570-40ec-bdbc-74370594a109' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7b25bfb-7570-40ec-bdbc-74370594a109' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e716db21-71e6-4e63-b660-4c2ceb6bae8e' class='xr-var-data-in' type='checkbox'><label for='data-e716db21-71e6-4e63-b660-4c2ceb6bae8e' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([ 0. , 1.875, 3.75 , 5.625, 7.5 , 9.375, 11.25 , 13.125,\n",
" 15. , 16.875, 18.75 , 20.625, 22.5 , 24.375, 26.25 , 28.125,\n",
" 30. , 31.875, 33.75 , 35.625, 37.5 , 39.375, 41.25 , 43.125,\n",
" 45. , 46.875, 48.75 , 50.625, 52.5 , 54.375, 56.25 , 58.125,\n",
" 60. , 61.875, 63.75 , 65.625, 67.5 , 69.375, 71.25 , 73.125,\n",
" 75. , 76.875, 78.75 , 80.625, 82.5 , 84.375, 86.25 , 88.125,\n",
" 90. , 91.875, 93.75 , 95.625, 97.5 , 99.375, 101.25 , 103.125,\n",
" 105. , 106.875, 108.75 , 110.625, 112.5 , 114.375, 116.25 , 118.125,\n",
" 120. , 121.875, 123.75 , 125.625, 127.5 , 129.375, 131.25 , 133.125,\n",
" 135. , 136.875, 138.75 , 140.625, 142.5 , 144.375, 146.25 , 148.125,\n",
" 150. , 151.875, 153.75 , 155.625, 157.5 , 159.375, 161.25 , 163.125,\n",
" 165. , 166.875, 168.75 , 170.625, 172.5 , 174.375, 176.25 , 178.125,\n",
" 180. , 181.875, 183.75 , 185.625, 187.5 , 189.375, 191.25 , 193.125,\n",
" 195. , 196.875, 198.75 , 200.625, 202.5 , 204.375, 206.25 , 208.125,\n",
" 210. , 211.875, 213.75 , 215.625, 217.5 , 219.375, 221.25 , 223.125,\n",
" 225. , 226.875, 228.75 , 230.625, 232.5 , 234.375, 236.25 , 238.125,\n",
" 240. , 241.875, 243.75 , 245.625, 247.5 , 249.375, 251.25 , 253.125,\n",
" 255. , 256.875, 258.75 , 260.625, 262.5 , 264.375, 266.25 , 268.125,\n",
" 270. , 271.875, 273.75 , 275.625, 277.5 , 279.375, 281.25 , 283.125,\n",
" 285. , 286.875, 288.75 , 290.625, 292.5 , 294.375, 296.25 , 298.125,\n",
" 300. , 301.875, 303.75 , 305.625, 307.5 , 309.375, 311.25 , 313.125,\n",
" 315. , 316.875, 318.75 , 320.625, 322.5 , 324.375, 326.25 , 328.125,\n",
" 330. , 331.875, 333.75 , 335.625, 337.5 , 339.375, 341.25 , 343.125,\n",
" 345. , 346.875, 348.75 , 350.625, 352.5 , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192, 2), meta=np.ndarray&gt;</div><input id='attrs-ad0735e5-e64e-48b5-a394-07684c79a96e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad0735e5-e64e-48b5-a394-07684c79a96e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a84044e1-2f02-4cb1-885f-e0f62b393904' class='xr-var-data-in' type='checkbox'><label for='data-a84044e1-2f02-4cb1-885f-e0f62b393904' 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'></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> 3.00 kiB </td>\n",
" <td> 3.00 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (192, 2) </td>\n",
" <td> (192, 2) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 1 chunks in 5 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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" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-ad6d1014-3914-408a-a74f-919bd195097a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad6d1014-3914-408a-a74f-919bd195097a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d359477-80a6-47bb-9e17-7c0b39252b8f' class='xr-var-data-in' type='checkbox'><label for='data-8d359477-80a6-47bb-9e17-7c0b39252b8f' 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>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3c4056ff-4f78-45fa-a46e-95562f6604c0' class='xr-section-summary-in' type='checkbox' checked><label for='section-3c4056ff-4f78-45fa-a46e-95562f6604c0' 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>tas</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=(1032, 145, 192), meta=np.ndarray&gt;</div><input id='attrs-0e5612df-499d-4543-a473-34d1d97e2cef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e5612df-499d-4543-a473-34d1d97e2cef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f6fe354-c236-4ca5-89b3-d937311f4d03' class='xr-var-data-in' type='checkbox'><label for='data-7f6fe354-c236-4ca5-89b3-d937311f4d03' 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>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</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> 364.48 MiB </td>\n",
" <td> 254.88 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (3432, 145, 192) </td>\n",
" <td> (2400, 145, 192) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 2 chunks in 5 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"165\" height=\"154\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
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"\n",
" <!-- Vertical lines -->\n",
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"\n",
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"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"10\" y1=\"0\" x2=\"45\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"31\" y1=\"21\" x2=\"66\" y2=\"21\" />\n",
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"\n",
" <!-- Vertical lines -->\n",
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" <line x1=\"45\" y1=\"0\" x2=\"115\" y2=\"70\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
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"\n",
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"\n",
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"\n",
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"\n",
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" <text x=\"98.181880\" y=\"124.056998\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >192</text>\n",
" <text x=\"135.775525\" y=\"87.322617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,135.775525,87.322617)\">145</text>\n",
" <text x=\"35.294118\" y=\"88.762880\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,88.762880)\">3432</text>\n",
"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b45eb340-5d96-40c1-8010-850d2b205202' class='xr-section-summary-in' type='checkbox' ><label for='section-b45eb340-5d96-40c1-8010-850d2b205202' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3c82a28c-5270-497f-9ef1-b113f2586dd3' class='xr-index-data-in' type='checkbox'/><label for='index-3c82a28c-5270-497f-9ef1-b113f2586dd3' 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([2015-01-16 12:00:00, 2015-02-15 00:00:00, 2015-03-16 12:00:00,\n",
" 2015-04-16 00:00:00, 2015-05-16 12:00:00, 2015-06-16 00:00:00,\n",
" 2015-07-16 12:00:00, 2015-08-16 12:00:00, 2015-09-16 00:00:00,\n",
" 2015-10-16 12:00:00,\n",
" ...\n",
" 2300-03-16 12:00:00, 2300-04-16 00:00:00, 2300-05-16 12:00:00,\n",
" 2300-06-16 00:00:00, 2300-07-16 12:00:00, 2300-08-16 12:00:00,\n",
" 2300-09-16 00:00:00, 2300-10-16 12:00:00, 2300-11-16 00:00:00,\n",
" 2300-12-16 12:00:00],\n",
" dtype=&#x27;object&#x27;,\n",
" length=3432,\n",
" calendar=&#x27;proleptic_gregorian&#x27;,\n",
" freq=&#x27;None&#x27;))</pre></div></li><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-10022de4-feb0-47c1-8b58-5c500259fc65' class='xr-index-data-in' type='checkbox'/><label for='index-10022de4-feb0-47c1-8b58-5c500259fc65' 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(Float64Index([ -90.0, -88.75, -87.5, -86.25, -85.0, -83.75, -82.5, -81.25,\n",
" -80.0, -78.75,\n",
" ...\n",
" 78.75, 80.0, 81.25, 82.5, 83.75, 85.0, 86.25, 87.5,\n",
" 88.75, 90.0],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</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-63e2897b-f7b7-4886-a682-95f492d228c4' class='xr-index-data-in' type='checkbox'/><label for='index-63e2897b-f7b7-4886-a682-95f492d228c4' 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(Float64Index([ 0.0, 1.875, 3.75, 5.625, 7.5, 9.375, 11.25,\n",
" 13.125, 15.0, 16.875,\n",
" ...\n",
" 341.25, 343.125, 345.0, 346.875, 348.75, 350.625, 352.5,\n",
" 354.375, 356.25, 358.125],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b730e0d5-f1e3-4aaf-b07c-1b5937807332' class='xr-section-summary-in' type='checkbox' ><label for='section-b730e0d5-f1e3-4aaf-b07c-1b5937807332' class='xr-section-summary' >Attributes: <span>(53)</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 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>ScenarioMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>60265.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>60265.0</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>update of RCP8.5 based on SSP5</dd><dt><span>experiment_id :</span></dt><dd>ssp585</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.ssp585.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>native atmosphere N96 grid (145x192 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>historical</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-1-1</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>source :</span></dt><dd>ACCESS-ESM1.5 (2019): \n",
"aerosol: CLASSIC (v1.0)\n",
"atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m)\n",
"atmosChem: none\n",
"land: CABLE2.4\n",
"landIce: none\n",
"ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m)\n",
"ocnBgchem: WOMBAT (same grid as ocean)\n",
"seaIce: CICE4.1 (same grid as ocean)</dd><dt><span>source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>title :</span></dt><dd>ACCESS-ESM1-5 output prepared for CMIP6</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;tas&#x27;]</dd><dt><span>intake_esm_attrs:project :</span></dt><dd>CMIP6</dd><dt><span>intake_esm_attrs:activity_id :</span></dt><dd>ScenarioMIP</dd><dt><span>intake_esm_attrs:institution_id :</span></dt><dd>CSIRO</dd><dt><span>intake_esm_attrs:source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>intake_esm_attrs:experiment_id :</span></dt><dd>ssp585</dd><dt><span>intake_esm_attrs:member_id :</span></dt><dd>r1i1p1f1</dd><dt><span>intake_esm_attrs:table_id :</span></dt><dd>Amon</dd><dt><span>intake_esm_attrs:variable_id :</span></dt><dd>tas</dd><dt><span>intake_esm_attrs:grid_label :</span></dt><dd>gn</dd><dt><span>intake_esm_attrs:version :</span></dt><dd>v20210318</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>netcdf</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP6.ScenarioMIP.CSIRO.ACCESS-ESM1-5.ssp585.r1i1p1f1.Amon.tas.gn.v20210318</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (time: 3432, bnds: 2, lat: 145, lon: 192)\n",
"Coordinates:\n",
" * time (time) object 2015-01-16 12:00:00 ... 2300-12-16 12:00:00\n",
" time_bnds (time, bnds) object dask.array<chunksize=(1032, 2), meta=np.ndarray>\n",
" * lat (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n",
" lat_bnds (lat, bnds) float64 dask.array<chunksize=(145, 2), meta=np.ndarray>\n",
" * lon (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
" lon_bnds (lon, bnds) float64 dask.array<chunksize=(192, 2), meta=np.ndarray>\n",
" height float64 2.0\n",
"Dimensions without coordinates: bnds\n",
"Data variables:\n",
" tas (time, lat, lon) float32 dask.array<chunksize=(1032, 145, 192), meta=np.ndarray>\n",
"Attributes: (12/53)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: ScenarioMIP\n",
" branch_method: standard\n",
" branch_time_in_child: 60265.0\n",
" branch_time_in_parent: 60265.0\n",
" data_specs_version: 01.00.30\n",
" ... ...\n",
" intake_esm_attrs:table_id: Amon\n",
" intake_esm_attrs:variable_id: tas\n",
" intake_esm_attrs:grid_label: gn\n",
" intake_esm_attrs:version: v20210318\n",
" intake_esm_attrs:_data_format_: netcdf\n",
" intake_esm_dataset_key: CMIP6.ScenarioMIP.CSIRO.ACCESS-ESM1-5.s..."
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# tas_ds has both the field and the bounds\n",
"tas_ds"
]
},
{
"cell_type": "markdown",
"id": "7eb00a62-151c-4822-a7e7-a07577b8dd7c",
"metadata": {},
"source": [
"# Target grid"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a5f1142e-58b6-48eb-a259-12e671f35a44",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"target = xarray.open_dataset('/g/data/ik11/inputs/JRA-55/RYF/v1-4/RYF.uas.1990_1991.nc')"
]
},
{
"cell_type": "markdown",
"id": "c60c2129-2244-4347-ae13-df37a6eef7b7",
"metadata": {},
"source": [
"# Regrid"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2ebb7118-efdc-476a-a61e-a297b61b653b",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x14fe7cf070d0>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Give tas_ds with the field and bounds to xesmf.Regridder\n",
"regrid = xesmf.Regridder(tas_ds, target, 'conservative', periodic=True)\n",
"\n",
"# You can then give just the variable (tas_ds.tas) to regrid()\n",
"tas_on_jra = regrid(tas_ds.tas[0,:,:], keep_attrs = True)\n",
"tas_on_jra.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff5b3cbc-ea74-48b9-a6e9-b654bd6fd9aa",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:analysis3]",
"language": "python",
"name": "conda-env-analysis3-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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