3_generate_member_anomalies.ipynb

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    "# Generate Member Anomalies S2S\n",
    "\n",
    "---\n",
    "\n",
    "Uses the climatology generated in `2_generate_model_climatology.ipynb` to create anomalies from the S2S forecasting system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "xr.set_options(keep_attrs=True)\n",
    "import glob\n",
    "import cftime\n",
    "\n",
    "from dask.distributed import Client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = Client(\"tcp://10.12.206.54:41456\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = \"ECCC\" #ECMWF, NCEP, or ECCC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw = xr.open_zarr(\"/glade/campaign/mmm/c3we/jaye/S2S_zarr/\"+model+\".raw.daily.geospatial.zarr/\",consolidated=True)\n",
    "clim = xr.open_zarr(\"/glade/campaign/mmm/c3we/jaye/S2S_zarr/\"+model+\".climatology.daily.geospatial.zarr/\",consolidated=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we want to compute the climatology before creating the anomalies so the calculation is more efficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.26 s, sys: 31.7 s, total: 36 s\n",
      "Wall time: 2min 20s\n"
     ]
    }
   ],
   "source": [
    "%time clim = clim.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate anomalies!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw = raw.chunk({\"member\": -1, \"init\": 1, \"lead\": -1, \"lat\": 121, \"lon\": 240}).persist()\n",
    "ds_anom = raw.groupby(\"init.dayofyear\") - clim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "Dimensions:    (init: 1060, lat: 121, lead: 32, lon: 240, member: 4)\n",
       "Coordinates:\n",
       "  * init       (init) datetime64[ns] 1998-01-02 1998-01-09 ... 2017-12-31\n",
       "  * lat        (lat) float64 90.0 88.5 87.0 85.5 ... -85.5 -87.0 -88.5 -90.0\n",
       "  * lead       (lead) timedelta64[ns] 1 days 2 days 3 days ... 31 days 32 days\n",
       "  * lon        (lon) float64 0.0 1.5 3.0 4.5 6.0 ... 354.0 355.5 357.0 358.5\n",
       "  * member     (member) int64 0 1 2 3\n",
       "    dayofyear  (init) int64 2 9 16 23 30 37 44 ... 323 330 337 344 351 358 365\n",
       "Data variables:\n",
       "    gh_500     (member, init, lead, lat, lon) float64 dask.array&lt;chunksize=(4, 1, 32, 121, 240), meta=np.ndarray&gt;\n",
       "    t2m        (member, init, lead, lat, lon) float64 dask.array&lt;chunksize=(4, 1, 32, 121, 240), meta=np.ndarray&gt;\n",
       "    tp         (member, init, lead, lat, lon) float64 dask.array&lt;chunksize=(4, 1, 32, 121, 240), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><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-e886b4e8-9759-40c6-9422-18af937ec690' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e886b4e8-9759-40c6-9422-18af937ec690' 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'>init</span>: 1060</li><li><span class='xr-has-index'>lat</span>: 121</li><li><span class='xr-has-index'>lead</span>: 32</li><li><span class='xr-has-index'>lon</span>: 240</li><li><span class='xr-has-index'>member</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9fc6a9f1-d49e-43c5-808d-c615a1147bb2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9fc6a9f1-d49e-43c5-808d-c615a1147bb2' 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 class='xr-has-index'>init</span></div><div class='xr-var-dims'>(init)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1998-01-02 ... 2017-12-31</div><input id='attrs-b0c550b6-084f-4ba5-be82-b3f1833571d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0c550b6-084f-4ba5-be82-b3f1833571d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2313699-b594-4e4f-82f3-90baf69025d2' class='xr-var-data-in' type='checkbox'><label for='data-d2313699-b594-4e4f-82f3-90baf69025d2' 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;1998-01-02T00:00:00.000000000&#x27;, &#x27;1998-01-09T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-01-16T00:00:00.000000000&#x27;, ..., &#x27;2017-12-17T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-12-24T00:00:00.000000000&#x27;, &#x27;2017-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></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 88.5 87.0 ... -88.5 -90.0</div><input id='attrs-656481d6-8481-4fc8-bbbb-0c66cb7f843b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-656481d6-8481-4fc8-bbbb-0c66cb7f843b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00b310dd-fade-44e4-9d03-e1798f764aa9' class='xr-var-data-in' type='checkbox'><label for='data-00b310dd-fade-44e4-9d03-e1798f764aa9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90. ,  88.5,  87. ,  85.5,  84. ,  82.5,  81. ,  79.5,  78. ,  76.5,\n",
       "        75. ,  73.5,  72. ,  70.5,  69. ,  67.5,  66. ,  64.5,  63. ,  61.5,\n",
       "        60. ,  58.5,  57. ,  55.5,  54. ,  52.5,  51. ,  49.5,  48. ,  46.5,\n",
       "        45. ,  43.5,  42. ,  40.5,  39. ,  37.5,  36. ,  34.5,  33. ,  31.5,\n",
       "        30. ,  28.5,  27. ,  25.5,  24. ,  22.5,  21. ,  19.5,  18. ,  16.5,\n",
       "        15. ,  13.5,  12. ,  10.5,   9. ,   7.5,   6. ,   4.5,   3. ,   1.5,\n",
       "         0. ,  -1.5,  -3. ,  -4.5,  -6. ,  -7.5,  -9. , -10.5, -12. , -13.5,\n",
       "       -15. , -16.5, -18. , -19.5, -21. , -22.5, -24. , -25.5, -27. , -28.5,\n",
       "       -30. , -31.5, -33. , -34.5, -36. , -37.5, -39. , -40.5, -42. , -43.5,\n",
       "       -45. , -46.5, -48. , -49.5, -51. , -52.5, -54. , -55.5, -57. , -58.5,\n",
       "       -60. , -61.5, -63. , -64.5, -66. , -67.5, -69. , -70.5, -72. , -73.5,\n",
       "       -75. , -76.5, -78. , -79.5, -81. , -82.5, -84. , -85.5, -87. , -88.5,\n",
       "       -90. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lead</span></div><div class='xr-var-dims'>(lead)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>1 days 2 days ... 31 days 32 days</div><input id='attrs-3522df3a-6012-4dda-90ca-be3d10efc9f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3522df3a-6012-4dda-90ca-be3d10efc9f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7aa7567b-7dba-4353-a541-b1f3ee4f9216' class='xr-var-data-in' type='checkbox'><label for='data-7aa7567b-7dba-4353-a541-b1f3ee4f9216' 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([  86400000000000,  172800000000000,  259200000000000,  345600000000000,\n",
       "        432000000000000,  518400000000000,  604800000000000,  691200000000000,\n",
       "        777600000000000,  864000000000000,  950400000000000, 1036800000000000,\n",
       "       1123200000000000, 1209600000000000, 1296000000000000, 1382400000000000,\n",
       "       1468800000000000, 1555200000000000, 1641600000000000, 1728000000000000,\n",
       "       1814400000000000, 1900800000000000, 1987200000000000, 2073600000000000,\n",
       "       2160000000000000, 2246400000000000, 2332800000000000, 2419200000000000,\n",
       "       2505600000000000, 2592000000000000, 2678400000000000, 2764800000000000],\n",
       "      dtype=&#x27;timedelta64[ns]&#x27;)</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'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.5 3.0 ... 355.5 357.0 358.5</div><input id='attrs-8f047c08-3193-485c-8a01-f8480d7859dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f047c08-3193-485c-8a01-f8480d7859dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d65c54d-3b01-419a-8528-7664da416f35' class='xr-var-data-in' type='checkbox'><label for='data-2d65c54d-3b01-419a-8528-7664da416f35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0. ,   1.5,   3. , ..., 355.5, 357. , 358.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member</span></div><div class='xr-var-dims'>(member)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-e016429f-456d-4d70-9c16-78f96158dbc7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e016429f-456d-4d70-9c16-78f96158dbc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2062b2e1-2415-4a19-9407-b6916ed4b728' class='xr-var-data-in' type='checkbox'><label for='data-2062b2e1-2415-4a19-9407-b6916ed4b728' 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>ensemble member numerical id</dd><dt><span>standard_name :</span></dt><dd>realization</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dayofyear</span></div><div class='xr-var-dims'>(init)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 9 16 23 30 ... 344 351 358 365</div><input id='attrs-d5f41b80-9fea-4ea7-9961-d6841447a294' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5f41b80-9fea-4ea7-9961-d6841447a294' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4c1d4f6-30c7-401b-81c3-cce1aee32b47' class='xr-var-data-in' type='checkbox'><label for='data-a4c1d4f6-30c7-401b-81c3-cce1aee32b47' 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'><pre>array([  2,   9,  16, ..., 351, 358, 365])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1ec36ab9-8d53-4b91-af34-943ed7c3a57d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1ec36ab9-8d53-4b91-af34-943ed7c3a57d' class='xr-section-summary' >Data variables: <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>gh_500</span></div><div class='xr-var-dims'>(member, init, lead, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 1, 32, 121, 240), meta=np.ndarray&gt;</div><input id='attrs-267a8bce-20de-4bcb-aa26-9031af18c139' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-267a8bce-20de-4bcb-aa26-9031af18c139' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8b02eea-67f3-4749-aa0a-c2105abb5874' class='xr-var-data-in' type='checkbox'><label for='data-e8b02eea-67f3-4749-aa0a-c2105abb5874' 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_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>240</dd><dt><span>GRIB_Ny :</span></dt><dd>121</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential_height</dd><dt><span>GRIB_cfVarName :</span></dt><dd>gh</dd><dt><span>GRIB_dataType :</span></dt><dd>pf</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/longitude </dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>1.5</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>1.5</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>358.5</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential Height</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>29040</dd><dt><span>GRIB_paramId :</span></dt><dd>156</dd><dt><span>GRIB_shortName :</span></dt><dd>gh</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>4</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_units :</span></dt><dd>gpm</dd><dt><span>cell_method :</span></dt><dd>lead_time: point</dd><dt><span>coordinates :</span></dt><dd>valid_time</dd><dt><span>long_name :</span></dt><dd>Geopotential Height</dd><dt><span>standard_name :</span></dt><dd>geopotential_height</dd><dt><span>units :</span></dt><dd>gpm</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
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       "    <tr><th> Shape </th><td> (4, 1060, 32, 121, 240) </td> <td> (4, 1, 32, 121, 240) </td></tr>\n",
       "    <tr><th> Count </th><td> 5397 Tasks </td><td> 1060 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>(member, init, lead, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 1, 32, 121, 240), meta=np.ndarray&gt;</div><input id='attrs-f1d375b2-4563-47e0-912d-cc24a4788eed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1d375b2-4563-47e0-912d-cc24a4788eed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cef2ec7-46d2-43e2-8a2d-322cc9c95f41' class='xr-var-data-in' type='checkbox'><label for='data-0cef2ec7-46d2-43e2-8a2d-322cc9c95f41' 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_NV :</span></dt><dd>0</dd><dt><span>GRIB_Nx :</span></dt><dd>240</dd><dt><span>GRIB_Ny :</span></dt><dd>121</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_dataType :</span></dt><dd>pf</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/longitude </dd><dt><span>GRIB_gridType :</span></dt><dd>regular_ll</dd><dt><span>GRIB_iDirectionIncrementInDegrees :</span></dt><dd>1.5</dd><dt><span>GRIB_iScansNegatively :</span></dt><dd>0</dd><dt><span>GRIB_jDirectionIncrementInDegrees :</span></dt><dd>1.5</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>358.5</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>29040</dd><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_stepType :</span></dt><dd>avg</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>4</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>cell_method :</span></dt><dd>lead_time: average</dd><dt><span>coordinates :</span></dt><dd>valid_time</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-1bc1abf8-8e79-4509-b4d3-74bee42d7d2d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1bc1abf8-8e79-4509-b4d3-74bee42d7d2d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (init: 1060, lat: 121, lead: 32, lon: 240, member: 4)\n",
       "Coordinates:\n",
       "  * init       (init) datetime64[ns] 1998-01-02 1998-01-09 ... 2017-12-31\n",
       "  * lat        (lat) float64 90.0 88.5 87.0 85.5 ... -85.5 -87.0 -88.5 -90.0\n",
       "  * lead       (lead) timedelta64[ns] 1 days 2 days 3 days ... 31 days 32 days\n",
       "  * lon        (lon) float64 0.0 1.5 3.0 4.5 6.0 ... 354.0 355.5 357.0 358.5\n",
       "  * member     (member) int64 0 1 2 3\n",
       "    dayofyear  (init) int64 2 9 16 23 30 37 44 ... 323 330 337 344 351 358 365\n",
       "Data variables:\n",
       "    gh_500     (member, init, lead, lat, lon) float64 dask.array<chunksize=(4, 1, 32, 121, 240), meta=np.ndarray>\n",
       "    t2m        (member, init, lead, lat, lon) float64 dask.array<chunksize=(4, 1, 32, 121, 240), meta=np.ndarray>\n",
       "    tp         (member, init, lead, lat, lon) float64 dask.array<chunksize=(4, 1, 32, 121, 240), meta=np.ndarray>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_anom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the `init` dimension to a proleptic gregorian calendar using cftime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_anom[\"init\"] = [cftime.DatetimeProlepticGregorian(int(str(d)[0:4]), int(str(d)[5:7]), int(str(d)[8:10])) for d in ds_anom[\"init\"].values]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Write the anomalies out to a zarr file!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 s, sys: 11.9 s, total: 14.9 s\n",
      "Wall time: 1min 39s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.backends.zarr.ZarrStore at 0x2ae2316a6d60>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time ds_anom.to_zarr(\"/glade/campaign/mmm/c3we/jaye/S2S_zarr/\"+model+\".anom.daily.geospatial.zarr/\",mode=\"w\",consolidated=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just made a quick plot to check that they are anomalies. Looks good!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2ae2400106d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_anom.t2m.isel(init=0, lead=10, member=3).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:miniconda3-s2s-asp-test]",
   "language": "python",
   "name": "conda-env-miniconda3-s2s-asp-test-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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}