docs/examples/02-Run-Synsat-for-ERA5-Data.ipynb

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   "source": [
    "# Run Synsat for ERA5 Data"
   ]
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   "source": [
    "Here, we will \n",
    "- load ERA5 test data \n",
    "- run Synsat on ERA5 and\n",
    "- finally store the Synsats into a netcdf file"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup Env and Load Libraries "
   ]
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   "source": [
    "import os, sys\n",
    "os.environ['RTTOV_PYTHON_WRAPPER'] = '/work/bb1262/tools/rttov/rttov-v13.2/wrapper'"
   ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no git hash can be obtained\n"
     ]
    }
   ],
   "source": [
    "import synsatipy\n",
    "\n",
    "from synsatipy.synsat import SynSat\n",
    "\n",
    "import synsatipy.synsat_example_data as synsat_example_data"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Steps towards Synsat "
   ]
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   "source": [
    "### Step I: Initialize Synsat class"
   ]
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    "tags": []
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     "text": [
      "... [synsat] set cloud / aerosol file to  /work/bb1262/tools/rttov/rttov-v13.2/rtcoef_rttov13/cldaer_visir/sccldcoef_msg_3_seviri.dat\n",
      "... [synsat] load coefficient file /work/bb1262/tools/rttov/rttov-v13.2/rtcoef_rttov13/rttov13pred54L/rtcoef_msg_3_seviri_o3.dat\n"
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     "text": [
      " 2024/03/26  17:11:19  Load coefficients:\n",
      "Load successful >>>>> inst_id : 1, nchannels : 6.\n",
      " 2024/03/26  17:11:19  /work/bb1262/tools/rttov/rttov-v13.2/rtcoef_rttov13/rttov13pred54L/rtcoef_msg_3_seviri_o3.dat\n",
      " 2024/03/26  17:11:19  /work/bb1262/tools/rttov/rttov-v13.2/rtcoef_rttov13/cldaer_visir/sccldcoef_msg_3_seviri.dat\n"
     ]
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   "source": [
    "s = SynSat( )"
   ]
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   "source": [
    "### Step II: Load Example Data "
   ]
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   "execution_count": 4,
   "metadata": {
    "tags": []
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     "text": [
      "... [synsat] read data from file  /work/bb1262/data/era5/medi/2020/era5-3d-medi-2020-09-15.nc\n"
     ]
    },
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     "text": [
      "/home/b/b380352/proj/2022-01_RTTOV-Synsat/synsatipy-devel/synsatipy/data_handler.py:172: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  nlevels = profs.dims[\"lev\"]\n",
      "/home/b/b380352/proj/2022-01_RTTOV-Synsat/synsatipy-devel/synsatipy/data_handler.py:173: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  nprofiles = profs.dims[\"profile\"]\n",
      "/home/b/b380352/proj/2022-01_RTTOV-Synsat/synsatipy-devel/synsatipy/data_handler.py:73: RuntimeWarning: invalid value encountered in arccos\n",
      "  azi = np.arccos(np.sin(lat) / np.sin(delta))\n"
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    "eraname = synsat_example_data.get_example_data( 'era01' )\n",
    "s.load( eraname, isel = {'time':[23,]} )"
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   "source": [
    "The ERA5 data have been loaded into a data handler. You can inspect the data as follows:"
   ]
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   "execution_count": 5,
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   "source": [
    "era5 = s.synsat.data_handler.input_data"
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   "execution_count": 6,
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     "text": [
      "<xarray.Dataset> Size: 94MB\n",
      "Dimensions:  (time: 1, lon: 213, lat: 61, lev: 137, nhyi: 138, nhym: 137,\n",
      "              lev_2: 1)\n",
      "Coordinates:\n",
      "  * time     (time) datetime64[ns] 8B 2020-09-15T23:00:00\n",
      "  * lon      (lon) float64 2kB -8.0 -7.75 -7.5 -7.25 ... 44.25 44.5 44.75 45.0\n",
      "  * lat      (lat) float64 488B 30.0 30.25 30.5 30.75 ... 44.25 44.5 44.75 45.0\n",
      "  * lev      (lev) float64 1kB 1.0 2.0 3.0 4.0 5.0 ... 134.0 135.0 136.0 137.0\n",
      "  * lev_2    (lev_2) float64 8B 1.0\n",
      "Dimensions without coordinates: nhyi, nhym\n",
      "Data variables: (12/37)\n",
      "    SSTK     (time, lat, lon) float32 52kB dask.array<chunksize=(1, 61, 213), meta=np.ndarray>\n",
      "    SLT      (time, lat, lon) float32 52kB dask.array<chunksize=(1, 61, 213), meta=np.ndarray>\n",
      "    Z        (time, lat, lon) float32 52kB dask.array<chunksize=(1, 61, 213), meta=np.ndarray>\n",
      "    SP       (time, lat, lon) float32 52kB dask.array<chunksize=(1, 61, 213), meta=np.ndarray>\n",
      "    TCW      (time, lat, lon) float32 52kB dask.array<chunksize=(1, 61, 213), meta=np.ndarray>\n",
      "    TCWV     (time, lat, lon) float32 52kB dask.array<chunksize=(1, 61, 213), meta=np.ndarray>\n",
      "    ...       ...\n",
      "    lnsp     (time, lev_2, lat, lon) float32 52kB 11.39 11.39 ... 11.52 11.53\n",
      "    o3       (time, lev, lat, lon) float32 7MB 1.501e-07 1.502e-07 ... 6.895e-08\n",
      "    clwc     (time, lev, lat, lon) float32 7MB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "    ciwc     (time, lev, lat, lon) float32 7MB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "    cc       (time, lev, lat, lon) float32 7MB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "    p        (lev, time, lat, lon) float64 14MB dask.array<chunksize=(137, 1, 61, 213), meta=np.ndarray>\n",
      "Attributes:\n",
      "    CDI:                       Climate Data Interface version 2.0.5 (https://...\n",
      "    Conventions:               CF-1.6\n",
      "    institution:               European Centre for Medium-Range Weather Forec...\n",
      "    history:                   Thu Jun 23 17:15:12 2022: cdo -z zip_3 merge E...\n",
      "    cdo_openmp_thread_number:  8\n",
      "    CDO:                       Climate Data Operators version 2.0.5 (https://...\n"
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   "source": [
    "print( era5 )"
   ]
  },
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   "execution_count": 7,
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    "tags": []
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       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 1)&gt; Size: 8B\n",
       "array([&#x27;2020-09-15T23:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 8B 2020-09-15T23:00:00\n",
       "Attributes:\n",
       "    standard_name:  time\n",
       "    axis:           T</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7b8cbe96-7357-434c-b33b-d56ac2811720' class='xr-array-in' type='checkbox' checked><label for='section-7b8cbe96-7357-434c-b33b-d56ac2811720' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2020-09-15T23:00:00</span></div><div class='xr-array-data'><pre>array([&#x27;2020-09-15T23:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-74532a70-cbdf-471d-a698-ea0cf5f5591d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74532a70-cbdf-471d-a698-ea0cf5f5591d' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-09-15T23:00:00</div><input id='attrs-e666a271-5bbd-4e5d-b6ba-414458ad42ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e666a271-5bbd-4e5d-b6ba-414458ad42ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da2a51c2-6c9a-4ff4-ba6d-97111fd32865' class='xr-var-data-in' type='checkbox'><label for='data-da2a51c2-6c9a-4ff4-ba6d-97111fd32865' 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>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-09-15T23:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d072f5e7-1517-4c89-bc07-50c9196f605b' class='xr-section-summary-in' type='checkbox'  ><label for='section-d072f5e7-1517-4c89-bc07-50c9196f605b' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-35c6782f-a6ad-4ba4-b490-37453be92322' class='xr-index-data-in' type='checkbox'/><label for='index-35c6782f-a6ad-4ba4-b490-37453be92322' 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(DatetimeIndex([&#x27;2020-09-15 23:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b5325ba-2234-4eae-8cfb-e418c9e0c6c1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7b5325ba-2234-4eae-8cfb-e418c9e0c6c1' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 1)> Size: 8B\n",
       "array(['2020-09-15T23:00:00.000000000'], dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 8B 2020-09-15T23:00:00\n",
       "Attributes:\n",
       "    standard_name:  time\n",
       "    axis:           T"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "era5.time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step III: Setup Run Options and Start Execution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "s._options.Nthreads=4\n",
    "s._options.NprofsPerCall = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IR emissivity atlas loaded successfully\n",
      "Atlas deallocated.\n",
      " 2024/03/26  17:11:23  Running RTTOV using nthreads =    4 and nprofs_per_call =     1000\n"
     ]
    }
   ],
   "source": [
    "s.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step IV: Extract the Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... [synsat]: WARNING: fail to write global attributes\n"
     ]
    }
   ],
   "source": [
    "era_synsat = s.extract_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset> Size: 626kB\n",
      "Dimensions:  (time: 1, lon: 213, lat: 61)\n",
      "Coordinates:\n",
      "  * time     (time) datetime64[ns] 8B 2020-09-15T23:00:00\n",
      "  * lon      (lon) float64 2kB -8.0 -7.75 -7.5 -7.25 ... 44.25 44.5 44.75 45.0\n",
      "  * lat      (lat) float64 488B 30.0 30.25 30.5 30.75 ... 44.25 44.5 44.75 45.0\n",
      "Data variables:\n",
      "    bt062    (time, lon, lat) float64 104kB 229.8 229.4 230.6 ... 233.9 233.6\n",
      "    bt073    (time, lon, lat) float64 104kB 242.0 239.3 246.3 ... 252.5 251.9\n",
      "    bt087    (time, lon, lat) float64 104kB 260.0 251.1 269.2 ... 282.3 282.6\n",
      "    bt108    (time, lon, lat) float64 104kB 257.7 249.3 268.2 ... 285.3 285.5\n",
      "    bt120    (time, lon, lat) float64 104kB 255.1 247.6 265.6 ... 284.4 284.6\n",
      "    bt134    (time, lon, lat) float64 104kB 244.6 240.1 251.9 ... 259.3 259.1\n",
      "Attributes:\n",
      "    CDI:                       Climate Data Interface version 2.0.5 (https://...\n",
      "    Conventions:               CF-1.6\n",
      "    institution:               European Centre for Medium-Range Weather Forec...\n",
      "    history:                   Thu Jun 23 17:15:12 2022: cdo -z zip_3 merge E...\n",
      "    cdo_openmp_thread_number:  8\n",
      "    CDO:                       Climate Data Operators version 2.0.5 (https://...\n"
     ]
    }
   ],
   "source": [
    "print( era_synsat )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step V: Store Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... store synsat at ./Data/synsat_era5_example_data.nc\n"
     ]
    }
   ],
   "source": [
    "outdir = './Data'\n",
    "\n",
    "if not os.path.isdir( outdir ):\n",
    "    os.makedirs( outdir )\n",
    "outfile = f'{outdir}/synsat_era5_example_data.nc'\n",
    "\n",
    "print(f'... store synsat at {outfile}')\n",
    "era_synsat.to_netcdf(outfile)\n"
   ]
  }
 ],
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