diff --git a/examples/jupyter_notebooks/MM_trp_no2_l2_plot.ipynb b/examples/jupyter_notebooks/MM_trp_no2_l2_plot.ipynb index 7fc3315b..ef71d521 100644 --- a/examples/jupyter_notebooks/MM_trp_no2_l2_plot.ipynb +++ b/examples/jupyter_notebooks/MM_trp_no2_l2_plot.ipynb @@ -74,7 +74,7 @@ " 'tm5_tropopause_layer_index': {'group': ['PRODUCT']}}}}},\n", " 'model': {'wrfchem_v4.2': {'files': '/Users/mengli/Work/melodies-monet/modeldata/wrfchem/0715/*',\n", " 'mod_type': 'wrfchem',\n", - " 'apply_ak': False,\n", + " 'apply_ak': True,\n", " 'mod_kwargs': {'mech': 'racm_esrl_vcp'},\n", " 'mapping': {'tropomi_l2_no2': {'no2': 'nitrogendioxide_tropospheric_column'}},\n", " 'projection': None,\n", @@ -149,163 +149,212 @@ "output_type": "stream", "text": [ "Reading TROPOMI L2 NO2\n", - "/Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/*\n", "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190714T231100_20190715T005230_09074_03_020400_20221105T205731.nc\n", - "Reading tropomi l2 no2 data: qa_value\n", - "Reading tropomi l2 no2 data: nitrogendioxide_tropospheric_column\n", - "Reading tropomi l2 no2 data: averaging_kernel\n", - "Reading tropomi l2 no2 data: air_mass_factor_total\n", - "Reading tropomi l2 no2 data: air_mass_factor_troposphere\n", - "Reading tropomi l2 no2 data: latitude\n", - "Reading tropomi l2 no2 data: longitude\n", - "Reading tropomi l2 no2 data: preslev\n", - "DEBUG:root:preslev\n", - "Working on TROPOMI NO2 pressure\n", - "nitrogendioxide_tropospheric_column\n", - "DEBUG:root:nitrogendioxide_tropospheric_column\n", + "- qa_value\n", + "- nitrogendioxide_tropospheric_column\n", + "INFO:root:nitrogendioxide_tropospheric_column already masked\n", + "- averaging_kernel\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/driver.py:300: FutureWarning: read_trpdataset is an alias for open_dataset and may be removed in the future\n", + " self.obj = mio.sat._tropomi_l2_no2_mm.read_trpdataset(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:root:averaging_kernel already masked\n", + "- air_mass_factor_total\n", + "INFO:root:air_mass_factor_total already masked\n", + "- air_mass_factor_troposphere\n", + "INFO:root:air_mass_factor_troposphere already masked\n", + "- latitude\n", + "- longitude\n", + "- preslev\n", + "INFO:root:tm5_tropopause_layer_index already masked\n", + "INFO:root:surface_pressure already masked\n", + "DEBUG:root:applying quality flag to nitrogendioxide_tropospheric_column\n", "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T005230_20190715T023400_09075_03_020400_20221105T210613.nc\n", - "Reading tropomi l2 no2 data: qa_value\n", - "Reading tropomi l2 no2 data: nitrogendioxide_tropospheric_column\n", - "Reading tropomi l2 no2 data: averaging_kernel\n", - "Reading tropomi l2 no2 data: air_mass_factor_total\n", - "Reading tropomi l2 no2 data: air_mass_factor_troposphere\n", - "Reading tropomi l2 no2 data: latitude\n", - "Reading tropomi l2 no2 data: longitude\n", - "Reading tropomi l2 no2 data: preslev\n", - "DEBUG:root:preslev\n", - "Working on TROPOMI NO2 pressure\n", - "nitrogendioxide_tropospheric_column\n", - "DEBUG:root:nitrogendioxide_tropospheric_column\n", + "- qa_value\n", + "- nitrogendioxide_tropospheric_column\n", + "INFO:root:nitrogendioxide_tropospheric_column already masked\n", + "- averaging_kernel\n", + "INFO:root:averaging_kernel already masked\n", + "- air_mass_factor_total\n", + "INFO:root:air_mass_factor_total already masked\n", + "- air_mass_factor_troposphere\n", + "INFO:root:air_mass_factor_troposphere already masked\n", + "- latitude\n", + "- longitude\n", + "- preslev\n", + "INFO:root:tm5_tropopause_layer_index already masked\n", + "INFO:root:surface_pressure already masked\n", + "DEBUG:root:applying quality flag to nitrogendioxide_tropospheric_column\n", "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T023400_20190715T041529_09076_03_020400_20221105T210615.nc\n", - "Reading tropomi l2 no2 data: qa_value\n", - "Reading tropomi l2 no2 data: nitrogendioxide_tropospheric_column\n", - "Reading tropomi l2 no2 data: averaging_kernel\n", - "Reading tropomi l2 no2 data: air_mass_factor_total\n", - "Reading tropomi l2 no2 data: air_mass_factor_troposphere\n", - "Reading tropomi l2 no2 data: latitude\n", - "Reading tropomi l2 no2 data: longitude\n", - "Reading tropomi l2 no2 data: preslev\n", - "DEBUG:root:preslev\n", - "Working on TROPOMI NO2 pressure\n", - "nitrogendioxide_tropospheric_column\n", - "DEBUG:root:nitrogendioxide_tropospheric_column\n", + "- qa_value\n", + "- nitrogendioxide_tropospheric_column\n", + "INFO:root:nitrogendioxide_tropospheric_column already masked\n", + "- averaging_kernel\n", + "INFO:root:averaging_kernel already masked\n", + "- air_mass_factor_total\n", + "INFO:root:air_mass_factor_total already masked\n", + "- air_mass_factor_troposphere\n", + "INFO:root:air_mass_factor_troposphere already masked\n", + "- latitude\n", + "- longitude\n", + "- preslev\n", + "INFO:root:tm5_tropopause_layer_index already masked\n", + "INFO:root:surface_pressure already masked\n", + "DEBUG:root:applying quality flag to nitrogendioxide_tropospheric_column\n", "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T041529_20190715T055659_09077_03_020400_20221105T210617.nc\n", - "Reading tropomi l2 no2 data: qa_value\n", - "Reading tropomi l2 no2 data: nitrogendioxide_tropospheric_column\n", - "Reading tropomi l2 no2 data: averaging_kernel\n", - "Reading tropomi l2 no2 data: air_mass_factor_total\n", - "Reading tropomi l2 no2 data: air_mass_factor_troposphere\n", - "Reading tropomi l2 no2 data: latitude\n", - "Reading tropomi l2 no2 data: longitude\n", - "Reading tropomi l2 no2 data: preslev\n", - "DEBUG:root:preslev\n", - "Working on TROPOMI NO2 pressure\n", - "nitrogendioxide_tropospheric_column\n", - "DEBUG:root:nitrogendioxide_tropospheric_column\n", + "- qa_value\n", + "- nitrogendioxide_tropospheric_column\n", + "INFO:root:nitrogendioxide_tropospheric_column already masked\n", + "- averaging_kernel\n", + "INFO:root:averaging_kernel already masked\n", + "- air_mass_factor_total\n", + "INFO:root:air_mass_factor_total already masked\n", + "- air_mass_factor_troposphere\n", + "INFO:root:air_mass_factor_troposphere already masked\n", + "- latitude\n", + "- longitude\n", + "- preslev\n", + "INFO:root:tm5_tropopause_layer_index already masked\n", + "INFO:root:surface_pressure already masked\n", + "DEBUG:root:applying quality flag to nitrogendioxide_tropospheric_column\n", "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T055659_20190715T073829_09078_03_020400_20221105T210619.nc\n", - "Reading tropomi l2 no2 data: qa_value\n", - "Reading tropomi l2 no2 data: nitrogendioxide_tropospheric_column\n", - "Reading tropomi l2 no2 data: averaging_kernel\n", - "Reading tropomi l2 no2 data: air_mass_factor_total\n", - "Reading tropomi l2 no2 data: air_mass_factor_troposphere\n", - "Reading tropomi l2 no2 data: latitude\n", - "Reading tropomi l2 no2 data: longitude\n", - "Reading tropomi l2 no2 data: preslev\n", - "DEBUG:root:preslev\n", - "Working on TROPOMI NO2 pressure\n", - "nitrogendioxide_tropospheric_column\n", - "DEBUG:root:nitrogendioxide_tropospheric_column\n", + "- qa_value\n", + "- nitrogendioxide_tropospheric_column\n", + "INFO:root:nitrogendioxide_tropospheric_column already masked\n", + "- averaging_kernel\n", + "INFO:root:averaging_kernel already masked\n", + "- air_mass_factor_total\n", + "INFO:root:air_mass_factor_total already masked\n", + "- air_mass_factor_troposphere\n", + "INFO:root:air_mass_factor_troposphere already masked\n", + "- latitude\n", + "- longitude\n", + "- preslev\n", + "INFO:root:tm5_tropopause_layer_index already masked\n", + "INFO:root:surface_pressure already masked\n", + "DEBUG:root:applying quality flag to nitrogendioxide_tropospheric_column\n", "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T124258_20190715T142428_09082_03_020400_20221105T210621.nc\n", - "Reading tropomi l2 no2 data: qa_value\n", - "Reading tropomi l2 no2 data: nitrogendioxide_tropospheric_column\n", - "Reading tropomi l2 no2 data: averaging_kernel\n", - "Reading tropomi l2 no2 data: air_mass_factor_total\n", - "Reading tropomi l2 no2 data: air_mass_factor_troposphere\n", - "Reading tropomi l2 no2 data: latitude\n", - "Reading tropomi l2 no2 data: longitude\n", - "Reading tropomi l2 no2 data: preslev\n", - "DEBUG:root:preslev\n", - "Working on TROPOMI NO2 pressure\n", - "nitrogendioxide_tropospheric_column\n", - "DEBUG:root:nitrogendioxide_tropospheric_column\n", + "- qa_value\n", + "- nitrogendioxide_tropospheric_column\n", + "INFO:root:nitrogendioxide_tropospheric_column already masked\n", + "- averaging_kernel\n", + "INFO:root:averaging_kernel already masked\n", + "- air_mass_factor_total\n", + "INFO:root:air_mass_factor_total already masked\n", + "- air_mass_factor_troposphere\n", + "INFO:root:air_mass_factor_troposphere already masked\n", + "- latitude\n", + "- longitude\n", + "- preslev\n", + "INFO:root:tm5_tropopause_layer_index already masked\n", + "INFO:root:surface_pressure already masked\n", + "DEBUG:root:applying quality flag to nitrogendioxide_tropospheric_column\n", "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T142428_20190715T160557_09083_03_020400_20221105T210623.nc\n", - "Reading tropomi l2 no2 data: qa_value\n", - "Reading tropomi l2 no2 data: nitrogendioxide_tropospheric_column\n", - "Reading tropomi l2 no2 data: averaging_kernel\n", - "Reading tropomi l2 no2 data: air_mass_factor_total\n", - "Reading tropomi l2 no2 data: air_mass_factor_troposphere\n", - "Reading tropomi l2 no2 data: latitude\n", - "Reading tropomi l2 no2 data: longitude\n", - "Reading tropomi l2 no2 data: preslev\n", - "DEBUG:root:preslev\n", - "Working on TROPOMI NO2 pressure\n", - "nitrogendioxide_tropospheric_column\n", - "DEBUG:root:nitrogendioxide_tropospheric_column\n", + "- qa_value\n", + "- nitrogendioxide_tropospheric_column\n", + "INFO:root:nitrogendioxide_tropospheric_column already masked\n", + "- averaging_kernel\n", + "INFO:root:averaging_kernel already masked\n", + "- air_mass_factor_total\n", + "INFO:root:air_mass_factor_total already masked\n", + "- air_mass_factor_troposphere\n", + "INFO:root:air_mass_factor_troposphere already masked\n", + "- latitude\n", + "- longitude\n", + "- preslev\n", + "INFO:root:tm5_tropopause_layer_index already masked\n", + "INFO:root:surface_pressure already masked\n", + "DEBUG:root:applying quality flag to nitrogendioxide_tropospheric_column\n", "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T160557_20190715T174727_09084_03_020400_20221105T210624.nc\n", - "Reading tropomi l2 no2 data: qa_value\n", - "Reading tropomi l2 no2 data: nitrogendioxide_tropospheric_column\n", - "Reading tropomi l2 no2 data: averaging_kernel\n", - "Reading tropomi l2 no2 data: air_mass_factor_total\n", - "Reading tropomi l2 no2 data: air_mass_factor_troposphere\n", - "Reading tropomi l2 no2 data: latitude\n", - "Reading tropomi l2 no2 data: longitude\n", - "Reading tropomi l2 no2 data: preslev\n", - "DEBUG:root:preslev\n", - "Working on TROPOMI NO2 pressure\n", - "nitrogendioxide_tropospheric_column\n", - "DEBUG:root:nitrogendioxide_tropospheric_column\n", + "- qa_value\n", + "- nitrogendioxide_tropospheric_column\n", + "INFO:root:nitrogendioxide_tropospheric_column already masked\n", + "- averaging_kernel\n", + "INFO:root:averaging_kernel already masked\n", + "- air_mass_factor_total\n", + "INFO:root:air_mass_factor_total already masked\n", + "- air_mass_factor_troposphere\n", + "INFO:root:air_mass_factor_troposphere already masked\n", + "- latitude\n", + "- longitude\n", + "- preslev\n", + "INFO:root:tm5_tropopause_layer_index already masked\n", + "INFO:root:surface_pressure already masked\n", + "DEBUG:root:applying quality flag to nitrogendioxide_tropospheric_column\n", "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T174727_20190715T192857_09085_03_020400_20221105T210627.nc\n", - "Reading tropomi l2 no2 data: qa_value\n", - "Reading tropomi l2 no2 data: nitrogendioxide_tropospheric_column\n", - "Reading tropomi l2 no2 data: averaging_kernel\n", - "Reading tropomi l2 no2 data: air_mass_factor_total\n", - "Reading tropomi l2 no2 data: air_mass_factor_troposphere\n", - "Reading tropomi l2 no2 data: latitude\n", - "Reading tropomi l2 no2 data: longitude\n", - "Reading tropomi l2 no2 data: preslev\n", - "DEBUG:root:preslev\n", - "Working on TROPOMI NO2 pressure\n", - "nitrogendioxide_tropospheric_column\n", - "DEBUG:root:nitrogendioxide_tropospheric_column\n", + "- qa_value\n", + "- nitrogendioxide_tropospheric_column\n", + "INFO:root:nitrogendioxide_tropospheric_column already masked\n", + "- averaging_kernel\n", + "INFO:root:averaging_kernel already masked\n", + "- air_mass_factor_total\n", + "INFO:root:air_mass_factor_total already masked\n", + "- air_mass_factor_troposphere\n", + "INFO:root:air_mass_factor_troposphere already masked\n", + "- latitude\n", + "- longitude\n", + "- preslev\n", + "INFO:root:tm5_tropopause_layer_index already masked\n", + "INFO:root:surface_pressure already masked\n", + "DEBUG:root:applying quality flag to nitrogendioxide_tropospheric_column\n", "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T192857_20190715T211026_09086_03_020400_20221105T210630.nc\n", - "Reading tropomi l2 no2 data: qa_value\n", - "Reading tropomi l2 no2 data: nitrogendioxide_tropospheric_column\n", - "Reading tropomi l2 no2 data: averaging_kernel\n", - "Reading tropomi l2 no2 data: air_mass_factor_total\n", - "Reading tropomi l2 no2 data: air_mass_factor_troposphere\n", - "Reading tropomi l2 no2 data: latitude\n", - "Reading tropomi l2 no2 data: longitude\n", - "Reading tropomi l2 no2 data: preslev\n", - "DEBUG:root:preslev\n", - "Working on TROPOMI NO2 pressure\n", - "nitrogendioxide_tropospheric_column\n", - "DEBUG:root:nitrogendioxide_tropospheric_column\n", + "- qa_value\n", + "- nitrogendioxide_tropospheric_column\n", + "INFO:root:nitrogendioxide_tropospheric_column already masked\n", + "- averaging_kernel\n", + "INFO:root:averaging_kernel already masked\n", + "- air_mass_factor_total\n", + "INFO:root:air_mass_factor_total already masked\n", + "- air_mass_factor_troposphere\n", + "INFO:root:air_mass_factor_troposphere already masked\n", + "- latitude\n", + "- longitude\n", + "- preslev\n", + "INFO:root:tm5_tropopause_layer_index already masked\n", + "INFO:root:surface_pressure already masked\n", + "DEBUG:root:applying quality flag to nitrogendioxide_tropospheric_column\n", "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T211026_20190715T225156_09087_03_020400_20221105T210634.nc\n", - "Reading tropomi l2 no2 data: qa_value\n", - "Reading tropomi l2 no2 data: nitrogendioxide_tropospheric_column\n", - "Reading tropomi l2 no2 data: averaging_kernel\n", - "Reading tropomi l2 no2 data: air_mass_factor_total\n", - "Reading tropomi l2 no2 data: air_mass_factor_troposphere\n", - "Reading tropomi l2 no2 data: latitude\n", - "Reading tropomi l2 no2 data: longitude\n", - "Reading tropomi l2 no2 data: preslev\n", - "DEBUG:root:preslev\n", - "Working on TROPOMI NO2 pressure\n", - "nitrogendioxide_tropospheric_column\n", - "DEBUG:root:nitrogendioxide_tropospheric_column\n", + "- qa_value\n", + "- nitrogendioxide_tropospheric_column\n", + "INFO:root:nitrogendioxide_tropospheric_column already masked\n", + "- averaging_kernel\n", + "INFO:root:averaging_kernel already masked\n", + "- air_mass_factor_total\n", + "INFO:root:air_mass_factor_total already masked\n", + "- air_mass_factor_troposphere\n", + "INFO:root:air_mass_factor_troposphere already masked\n", + "- latitude\n", + "- longitude\n", + "- preslev\n", + "INFO:root:tm5_tropopause_layer_index already masked\n", + "INFO:root:surface_pressure already masked\n", + "DEBUG:root:applying quality flag to nitrogendioxide_tropospheric_column\n", "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T225156_20190716T003326_09088_03_020400_20221105T210637.nc\n", - "Reading tropomi l2 no2 data: qa_value\n", - "Reading tropomi l2 no2 data: nitrogendioxide_tropospheric_column\n", - "Reading tropomi l2 no2 data: averaging_kernel\n", - "Reading tropomi l2 no2 data: air_mass_factor_total\n", - "Reading tropomi l2 no2 data: air_mass_factor_troposphere\n", - "Reading tropomi l2 no2 data: latitude\n", - "Reading tropomi l2 no2 data: longitude\n", - "Reading tropomi l2 no2 data: preslev\n", - "DEBUG:root:preslev\n", - "Working on TROPOMI NO2 pressure\n", - "nitrogendioxide_tropospheric_column\n", - "DEBUG:root:nitrogendioxide_tropospheric_column\n" + "- qa_value\n", + "- nitrogendioxide_tropospheric_column\n", + "INFO:root:nitrogendioxide_tropospheric_column already masked\n", + "- averaging_kernel\n", + "INFO:root:averaging_kernel already masked\n", + "- air_mass_factor_total\n", + "INFO:root:air_mass_factor_total already masked\n", + "- air_mass_factor_troposphere\n", + "INFO:root:air_mass_factor_troposphere already masked\n", + "- latitude\n", + "- longitude\n", + "- preslev\n", + "INFO:root:tm5_tropopause_layer_index already masked\n", + "INFO:root:surface_pressure already masked\n", + "DEBUG:root:applying quality flag to nitrogendioxide_tropospheric_column\n" ] } ], @@ -331,124 +380,1364 @@ } ], "source": [ - "# --- model\n", - "an.open_models()\n", - "lat = an.models['wrfchem_v4.2'].obj.coords['latitude']\n", - "lon = an.models['wrfchem_v4.2'].obj.coords['longitude']" + "# --- model\n", + "an.open_models()\n", + "lat = an.models['wrfchem_v4.2'].obj.coords['latitude']\n", + "lon = an.models['wrfchem_v4.2'].obj.coords['longitude']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b1e6bcd3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "model(\n", + " model='wrfchem',\n", + " radius_of_influence=1000000.0,\n", + " mod_kwargs={'mech': 'racm_esrl_vcp', 'var_list': ['no2', 'pres', 'height', 'tk', 'height_agl', 'PSFC', 'zstag']},\n", + " file_str='/Users/mengli/Work/melodies-monet/modeldata/wrfchem/0715/*',\n", + " label='wrfchem_v4.2',\n", + " obj=...,\n", + " mapping={'tropomi_l2_no2': {'no2': 'nitrogendioxide_tropospheric_column'}},\n", + " label='wrfchem_v4.2',\n", + " ...\n", + ")" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "an.models['wrfchem_v4.2']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1e1b261f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:        (y: 284, x: 440, time: 24, z: 50, bottom_top_stag: 51)\n",
+       "Coordinates:\n",
+       "    longitude      (y, x) float32 -122.3 -122.2 -122.1 ... -60.68 -60.52 -60.37\n",
+       "    latitude       (y, x) float32 21.19 21.22 21.24 21.27 ... 50.28 50.24 50.2\n",
+       "  * time           (time) datetime64[ns] 2019-07-15T12:00:00 ... 2019-07-15T2...\n",
+       "Dimensions without coordinates: y, x, z, bottom_top_stag\n",
+       "Data variables:\n",
+       "    no2            (time, z, y, x) float32 0.03217 0.0324 ... 0.2095 0.2095\n",
+       "    pres_pa_mid    (time, z, y, x) float32 1.012e+05 1.012e+05 ... 5.584e+03\n",
+       "    alt_msl_m_mid  (time, z, y, x) float32 11.31 11.31 ... 2.032e+04 2.032e+04\n",
+       "    temperature_k  (time, z, y, x) float32 295.2 295.2 295.2 ... 221.9 221.9\n",
+       "    alt_agl_m_mid  (time, z, y, x) float32 11.32 11.32 ... 2.031e+04 2.032e+04\n",
+       "    surfpres_pa    (time, y, x) float32 1.013e+05 1.013e+05 ... 1.006e+05\n",
+       "    zstag          (time, bottom_top_stag, y, x) float32 -0.009976 ... 2.1e+04\n",
+       "    dz_m           (time, z, y, x) float32 22.65 22.64 ... 1.364e+03 1.364e+03\n",
+       "Attributes: (12/15)\n",
+       "    FieldType:                 104\n",
+       "    MemoryOrder:               XYZ\n",
+       "    description:               NO2 mixing ratio\n",
+       "    units:                     ppmv\n",
+       "    stagger:                   \n",
+       "    coordinates:               XLONG XLAT XTIME\n",
+       "    ...                        ...\n",
+       "    MOAD_CEN_LAT:              39.617638\n",
+       "    STAND_LON:                 -97.0\n",
+       "    MAP_PROJ:                  1\n",
+       "    CEN_LAT:                   39.617638\n",
+       "    CEN_LON:                   -97.77487\n",
+       "    mapping_tables_to_airnow:  {'OZONE': 'o3', 'PM2.5': 'PM2_5_DRY', 'PM10': ...
" + ], + "text/plain": [ + "\n", + "Dimensions: (y: 284, x: 440, time: 24, z: 50, bottom_top_stag: 51)\n", + "Coordinates:\n", + " longitude (y, x) float32 -122.3 -122.2 -122.1 ... -60.68 -60.52 -60.37\n", + " latitude (y, x) float32 21.19 21.22 21.24 21.27 ... 50.28 50.24 50.2\n", + " * time (time) datetime64[ns] 2019-07-15T12:00:00 ... 2019-07-15T2...\n", + "Dimensions without coordinates: y, x, z, bottom_top_stag\n", + "Data variables:\n", + " no2 (time, z, y, x) float32 0.03217 0.0324 ... 0.2095 0.2095\n", + " pres_pa_mid (time, z, y, x) float32 1.012e+05 1.012e+05 ... 5.584e+03\n", + " alt_msl_m_mid (time, z, y, x) float32 11.31 11.31 ... 2.032e+04 2.032e+04\n", + " temperature_k (time, z, y, x) float32 295.2 295.2 295.2 ... 221.9 221.9\n", + " alt_agl_m_mid (time, z, y, x) float32 11.32 11.32 ... 2.031e+04 2.032e+04\n", + " surfpres_pa (time, y, x) float32 1.013e+05 1.013e+05 ... 1.006e+05\n", + " zstag (time, bottom_top_stag, y, x) float32 -0.009976 ... 2.1e+04\n", + " dz_m (time, z, y, x) float32 22.65 22.64 ... 1.364e+03 1.364e+03\n", + "Attributes: (12/15)\n", + " FieldType: 104\n", + " MemoryOrder: XYZ\n", + " description: NO2 mixing ratio\n", + " units: ppmv\n", + " stagger: \n", + " coordinates: XLONG XLAT XTIME\n", + " ... ...\n", + " MOAD_CEN_LAT: 39.617638\n", + " STAND_LON: -97.0\n", + " MAP_PROJ: 1\n", + " CEN_LAT: 39.617638\n", + " CEN_LON: -97.77487\n", + " mapping_tables_to_airnow: {'OZONE': 'o3', 'PM2.5': 'PM2_5_DRY', 'PM10': ..." + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "an.models['wrfchem_v4.2'].obj" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "b1e6bcd3", + "execution_count": 7, + "id": "6fe07174", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "model(\n", - " model='wrfchem',\n", - " radius_of_influence=1000000.0,\n", - " mod_kwargs={'mech': 'racm_esrl_vcp', 'var_list': ['no2', 'pres', 'height', 'tk', 'height_agl', 'PSFC', 'PH', 'PHB', 'PB', 'P', 'T']},\n", - " file_str='/Users/mengli/Work/melodies-monet/modeldata/wrfchem/0715/*',\n", - " label='wrfchem_v4.2',\n", - " obj=...,\n", - " mapping={'tropomi_l2_no2': {'no2': 'nitrogendioxide_tropospheric_column'}},\n", - " label='wrfchem_v4.2',\n", - " ...\n", - ")" + "OrderedDict([('2019-07-14',\n", + " [\n", + " Dimensions: (y: 3245, x: 450, z: 34)\n", + " Coordinates:\n", + " lon (y, x) float32 -172.1 -171.7 ... 6.39\n", + " lat (y, x) float32 -78.98 -78.99 ... 53.15\n", + " time datetime64[ns] 2019-07-14\n", + " scan_time (y) datetime64[ns] 2019-07-14T23:32:...\n", + " Dimensions without coordinates: y, x, z\n", + " Data variables:\n", + " qa_value (y, x) float32 0.0 0.0 0.0 ... 0.0 0.0\n", + " nitrogendioxide_tropospheric_column (y, x) float32 nan nan nan ... nan nan\n", + " averaging_kernel (y, x, z) float32 nan nan ... nan nan\n", + " air_mass_factor_total (y, x) float32 nan nan nan ... nan nan\n", + " air_mass_factor_troposphere (y, x) float32 nan nan nan ... nan nan\n", + " latitude (y, x) float32 -78.98 -78.99 ... 53.15\n", + " longitude (y, x) float32 -172.1 -171.7 ... 6.39\n", + " preslev (z, y, x) float32 nan nan ... nan nan\n", + " troppres (y, x) float32 nan nan nan ... nan nan\n", + " Attributes:\n", + " reference_time_string: 2019-07-14\n", + " quality_flag: qa_value\n", + " quality_thresh_min: 0.7\n", + " var_applied: ['nitrogendioxide_tropospheric_column']]),\n", + " ('2019-07-15',\n", + " [\n", + " Dimensions: (y: 3245, x: 450, z: 34)\n", + " Coordinates:\n", + " lon (y, x) float32 162.5 163.0 ... -18.98\n", + " lat (y, x) float32 -78.97 -78.99 ... 53.14\n", + " time datetime64[ns] 2019-07-15\n", + " scan_time (y) datetime64[ns] 2019-07-15T01:14:...\n", + " Dimensions without coordinates: y, x, z\n", + " Data variables:\n", + " qa_value (y, x) float32 0.0 0.0 0.0 ... 0.0 0.0\n", + " nitrogendioxide_tropospheric_column (y, x) float32 nan nan nan ... nan nan\n", + " averaging_kernel (y, x, z) float32 nan nan ... nan nan\n", + " air_mass_factor_total (y, x) float32 nan nan nan ... nan nan\n", + " air_mass_factor_troposphere (y, x) float32 nan nan nan ... nan nan\n", + " latitude (y, x) float32 -78.97 -78.99 ... 53.14\n", + " longitude (y, x) float32 162.5 163.0 ... -18.98\n", + " preslev (z, y, x) float32 nan nan ... nan nan\n", + " troppres (y, x) float32 nan nan nan ... nan nan\n", + " Attributes:\n", + " reference_time_string: 2019-07-15\n", + " quality_flag: qa_value\n", + " quality_thresh_min: 0.7\n", + " var_applied: ['nitrogendioxide_tropospheric_column'],\n", + " \n", + " Dimensions: (y: 3245, x: 450, z: 34)\n", + " Coordinates:\n", + " lon (y, x) float32 137.1 137.6 ... -44.36\n", + " lat (y, x) float32 -79.0 -79.02 ... 53.18\n", + " time datetime64[ns] 2019-07-15\n", + " scan_time (y) datetime64[ns] 2019-07-15T02:55:...\n", + " Dimensions without coordinates: y, x, z\n", + " Data variables:\n", + " qa_value (y, x) float32 0.0 0.0 0.0 ... 0.0 0.0\n", + " nitrogendioxide_tropospheric_column (y, x) float32 nan nan nan ... nan nan\n", + " averaging_kernel (y, x, z) float32 nan nan ... nan nan\n", + " air_mass_factor_total (y, x) float32 nan nan nan ... nan nan\n", + " air_mass_factor_troposphere (y, x) float32 nan nan nan ... nan nan\n", + " latitude (y, x) float32 -79.0 -79.02 ... 53.18\n", + " longitude (y, x) float32 137.1 137.6 ... -44.36\n", + " preslev (z, y, x) float32 nan nan ... nan nan\n", + " troppres (y, x) float32 nan nan nan ... nan nan\n", + " Attributes:\n", + " reference_time_string: 2019-07-15\n", + " quality_flag: qa_value\n", + " quality_thresh_min: 0.7\n", + " var_applied: ['nitrogendioxide_tropospheric_column'],\n", + " \n", + " Dimensions: (y: 3225, x: 450, z: 34)\n", + " Coordinates:\n", + " lon (y, x) float32 111.7 112.1 ... -69.73\n", + " lat (y, x) float32 -79.01 -79.03 ... 53.2\n", + " time datetime64[ns] 2019-07-15\n", + " scan_time (y) datetime64[ns] 2019-07-15T04:37:...\n", + " Dimensions without coordinates: y, x, z\n", + " Data variables:\n", + " qa_value (y, x) float32 0.0 0.0 0.0 ... 0.0 0.0\n", + " nitrogendioxide_tropospheric_column (y, x) float32 nan nan nan ... nan nan\n", + " averaging_kernel (y, x, z) float32 nan nan ... nan nan\n", + " air_mass_factor_total (y, x) float32 nan nan nan ... nan nan\n", + " air_mass_factor_troposphere (y, x) float32 nan nan nan ... nan nan\n", + " latitude (y, x) float32 -79.01 -79.03 ... 53.2\n", + " longitude (y, x) float32 111.7 112.1 ... -69.73\n", + " preslev (z, y, x) float32 nan nan ... nan nan\n", + " troppres (y, x) float32 nan nan nan ... nan nan\n", + " Attributes:\n", + " reference_time_string: 2019-07-15\n", + " quality_flag: qa_value\n", + " quality_thresh_min: 0.7\n", + " var_applied: ['nitrogendioxide_tropospheric_column'],\n", + " \n", + " Dimensions: (y: 2246, x: 450, z: 34)\n", + " Coordinates:\n", + " lon (y, x) float32 86.33 86.78 ... 107.8\n", + " lat (y, x) float32 -79.0 -79.02 ... 64.78\n", + " time datetime64[ns] 2019-07-15\n", + " scan_time (y) datetime64[ns] 2019-07-15T06:18:...\n", + " Dimensions without coordinates: y, x, z\n", + " Data variables:\n", + " qa_value (y, x) float32 0.0 0.0 0.0 ... 0.74 1.0\n", + " nitrogendioxide_tropospheric_column (y, x) float32 nan nan ... 1.63e+15\n", + " averaging_kernel (y, x, z) float32 nan nan ... 1.014\n", + " air_mass_factor_total (y, x) float32 nan nan ... 3.517 3.525\n", + " air_mass_factor_troposphere (y, x) float32 nan nan ... 2.117 2.078\n", + " latitude (y, x) float32 -79.0 -79.02 ... 64.78\n", + " longitude (y, x) float32 86.33 86.78 ... 107.8\n", + " preslev (z, y, x) float32 nan nan ... 9.304\n", + " troppres (y, x) float32 nan nan ... 1.791e+04\n", + " Attributes:\n", + " reference_time_string: 2019-07-15\n", + " quality_flag: qa_value\n", + " quality_thresh_min: 0.7\n", + " var_applied: ['nitrogendioxide_tropospheric_column'],\n", + " \n", + " Dimensions: (y: 963, x: 450, z: 34)\n", + " Coordinates:\n", + " lon (y, x) float32 -45.02 -44.92 ... 163.4\n", + " lat (y, x) float32 58.39 58.45 ... 53.22\n", + " time datetime64[ns] 2019-07-15\n", + " scan_time (y) datetime64[ns] 2019-07-15T13:45:...\n", + " Dimensions without coordinates: y, x, z\n", + " Data variables:\n", + " qa_value (y, x) float32 0.0 0.74 ... 0.0 0.0\n", + " nitrogendioxide_tropospheric_column (y, x) float32 nan -5.75e+13 ... nan\n", + " averaging_kernel (y, x, z) float32 nan nan ... nan nan\n", + " air_mass_factor_total (y, x) float32 nan 3.545 ... nan nan\n", + " air_mass_factor_troposphere (y, x) float32 nan 2.925 ... nan nan\n", + " latitude (y, x) float32 58.39 58.45 ... 53.22\n", + " longitude (y, x) float32 -45.02 -44.92 ... 163.4\n", + " preslev (z, y, x) float32 1.012e+05 ... nan\n", + " troppres (y, x) float32 nan 2.072e+04 ... nan\n", + " Attributes:\n", + " reference_time_string: 2019-07-15\n", + " quality_flag: qa_value\n", + " quality_thresh_min: 0.7\n", + " var_applied: ['nitrogendioxide_tropospheric_column'],\n", + " \n", + " Dimensions: (y: 3246, x: 450, z: 34)\n", + " Coordinates:\n", + " lon (y, x) float32 -40.67 -40.21 ... 138.0\n", + " lat (y, x) float32 -79.08 -79.09 ... 53.2\n", + " time datetime64[ns] 2019-07-15\n", + " scan_time (y) datetime64[ns] 2019-07-15T14:46:...\n", + " Dimensions without coordinates: y, x, z\n", + " Data variables:\n", + " qa_value (y, x) float32 0.0 0.0 0.0 ... 0.0 0.0\n", + " nitrogendioxide_tropospheric_column (y, x) float32 nan nan nan ... nan nan\n", + " averaging_kernel (y, x, z) float32 nan nan ... nan nan\n", + " air_mass_factor_total (y, x) float32 nan nan nan ... nan nan\n", + " air_mass_factor_troposphere (y, x) float32 nan nan nan ... nan nan\n", + " latitude (y, x) float32 -79.08 -79.09 ... 53.2\n", + " longitude (y, x) float32 -40.67 -40.21 ... 138.0\n", + " preslev (z, y, x) float32 nan nan ... nan nan\n", + " troppres (y, x) float32 nan nan nan ... nan nan\n", + " Attributes:\n", + " reference_time_string: 2019-07-15\n", + " quality_flag: qa_value\n", + " quality_thresh_min: 0.7\n", + " var_applied: ['nitrogendioxide_tropospheric_column'],\n", + " \n", + " Dimensions: (y: 3245, x: 450, z: 34)\n", + " Coordinates:\n", + " lon (y, x) float32 -66.08 -65.62 ... 112.6\n", + " lat (y, x) float32 -79.09 -79.11 ... 53.28\n", + " time datetime64[ns] 2019-07-15\n", + " scan_time (y) datetime64[ns] 2019-07-15T16:27:...\n", + " Dimensions without coordinates: y, x, z\n", + " Data variables:\n", + " qa_value (y, x) float32 0.0 0.0 0.0 ... 0.0 0.0\n", + " nitrogendioxide_tropospheric_column (y, x) float32 nan nan nan ... nan nan\n", + " averaging_kernel (y, x, z) float32 nan nan ... nan nan\n", + " air_mass_factor_total (y, x) float32 nan nan nan ... nan nan\n", + " air_mass_factor_troposphere (y, x) float32 nan nan nan ... nan nan\n", + " latitude (y, x) float32 -79.09 -79.11 ... 53.28\n", + " longitude (y, x) float32 -66.08 -65.62 ... 112.6\n", + " preslev (z, y, x) float32 nan nan ... nan nan\n", + " troppres (y, x) float32 nan nan nan ... nan nan\n", + " Attributes:\n", + " reference_time_string: 2019-07-15\n", + " quality_flag: qa_value\n", + " quality_thresh_min: 0.7\n", + " var_applied: ['nitrogendioxide_tropospheric_column'],\n", + " \n", + " Dimensions: (y: 3245, x: 450, z: 34)\n", + " Coordinates:\n", + " lon (y, x) float32 -91.43 -90.97 ... 87.27\n", + " lat (y, x) float32 -79.08 -79.09 ... 53.27\n", + " time datetime64[ns] 2019-07-15\n", + " scan_time (y) datetime64[ns] 2019-07-15T18:09:...\n", + " Dimensions without coordinates: y, x, z\n", + " Data variables:\n", + " qa_value (y, x) float32 0.0 0.0 0.0 ... 0.0 0.0\n", + " nitrogendioxide_tropospheric_column (y, x) float32 nan nan nan ... nan nan\n", + " averaging_kernel (y, x, z) float32 nan nan ... nan nan\n", + " air_mass_factor_total (y, x) float32 nan nan nan ... nan nan\n", + " air_mass_factor_troposphere (y, x) float32 nan nan nan ... nan nan\n", + " latitude (y, x) float32 -79.08 -79.09 ... 53.27\n", + " longitude (y, x) float32 -91.43 -90.97 ... 87.27\n", + " preslev (z, y, x) float32 nan nan ... nan nan\n", + " troppres (y, x) float32 nan nan nan ... nan nan\n", + " Attributes:\n", + " reference_time_string: 2019-07-15\n", + " quality_flag: qa_value\n", + " quality_thresh_min: 0.7\n", + " var_applied: ['nitrogendioxide_tropospheric_column'],\n", + " \n", + " Dimensions: (y: 3246, x: 450, z: 34)\n", + " Coordinates:\n", + " lon (y, x) float32 -116.9 -116.4 ... 61.9\n", + " lat (y, x) float32 -79.11 -79.13 ... 53.24\n", + " time datetime64[ns] 2019-07-15\n", + " scan_time (y) datetime64[ns] 2019-07-15T19:50:...\n", + " Dimensions without coordinates: y, x, z\n", + " Data variables:\n", + " qa_value (y, x) float32 0.0 0.0 0.0 ... 0.0 0.0\n", + " nitrogendioxide_tropospheric_column (y, x) float32 nan nan nan ... nan nan\n", + " averaging_kernel (y, x, z) float32 nan nan ... nan nan\n", + " air_mass_factor_total (y, x) float32 nan nan nan ... nan nan\n", + " air_mass_factor_troposphere (y, x) float32 nan nan nan ... nan nan\n", + " latitude (y, x) float32 -79.11 -79.13 ... 53.24\n", + " longitude (y, x) float32 -116.9 -116.4 ... 61.9\n", + " preslev (z, y, x) float32 nan nan ... nan nan\n", + " troppres (y, x) float32 nan nan nan ... nan nan\n", + " Attributes:\n", + " reference_time_string: 2019-07-15\n", + " quality_flag: qa_value\n", + " quality_thresh_min: 0.7\n", + " var_applied: ['nitrogendioxide_tropospheric_column'],\n", + " \n", + " Dimensions: (y: 3245, x: 450, z: 34)\n", + " Coordinates:\n", + " lon (y, x) float32 -142.3 -141.8 ... 36.52\n", + " lat (y, x) float32 -79.11 -79.12 ... 53.3\n", + " time datetime64[ns] 2019-07-15\n", + " scan_time (y) datetime64[ns] 2019-07-15T21:32:...\n", + " Dimensions without coordinates: y, x, z\n", + " Data variables:\n", + " qa_value (y, x) float32 0.0 0.0 0.0 ... 0.0 0.0\n", + " nitrogendioxide_tropospheric_column (y, x) float32 nan nan nan ... nan nan\n", + " averaging_kernel (y, x, z) float32 nan nan ... nan nan\n", + " air_mass_factor_total (y, x) float32 nan nan nan ... nan nan\n", + " air_mass_factor_troposphere (y, x) float32 nan nan nan ... nan nan\n", + " latitude (y, x) float32 -79.11 -79.12 ... 53.3\n", + " longitude (y, x) float32 -142.3 -141.8 ... 36.52\n", + " preslev (z, y, x) float32 nan nan ... nan nan\n", + " troppres (y, x) float32 nan nan nan ... nan nan\n", + " Attributes:\n", + " reference_time_string: 2019-07-15\n", + " quality_flag: qa_value\n", + " quality_thresh_min: 0.7\n", + " var_applied: ['nitrogendioxide_tropospheric_column'],\n", + " \n", + " Dimensions: (y: 2905, x: 450, z: 34)\n", + " Coordinates:\n", + " lon (y, x) float32 -167.7 -167.2 ... 8.484\n", + " lat (y, x) float32 -79.14 -79.16 ... 72.07\n", + " time datetime64[ns] 2019-07-15\n", + " scan_time (y) datetime64[ns] 2019-07-15T23:13:...\n", + " Dimensions without coordinates: y, x, z\n", + " Data variables:\n", + " qa_value (y, x) float32 0.0 0.0 0.0 ... 0.1 0.1\n", + " nitrogendioxide_tropospheric_column (y, x) float32 nan nan nan ... nan nan\n", + " averaging_kernel (y, x, z) float32 nan nan ... 0.8178\n", + " air_mass_factor_total (y, x) float32 nan nan ... 10.62 10.67\n", + " air_mass_factor_troposphere (y, x) float32 nan nan ... 2.433 2.544\n", + " latitude (y, x) float32 -79.14 -79.16 ... 72.07\n", + " longitude (y, x) float32 -167.7 -167.2 ... 8.484\n", + " preslev (z, y, x) float32 nan nan ... 9.304\n", + " troppres (y, x) float32 nan nan ... 2.605e+04\n", + " Attributes:\n", + " reference_time_string: 2019-07-15\n", + " quality_flag: qa_value\n", + " quality_thresh_min: 0.7\n", + " var_applied: ['nitrogendioxide_tropospheric_column']])])" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "an.models['wrfchem_v4.2']" + "an.obs['tropomi_l2_no2'].obj" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "58f836ce", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1, in pair data\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:52: RuntimeWarning: Mean of empty slice\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:166: RuntimeWarning: Mean of empty slice\n", " no2col_satm = np.nanmean(modobj_tm['no2col'].values, axis = 0)\n", - "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n", - " warnings.warn(\n" + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:204: RuntimeWarning: Mean of empty slice\n", + " modvalue_pb2 = np.nanmean(modobj_tm['pres_pa_mid'].values, axis = 0)\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:205: RuntimeWarning: Mean of empty slice\n", + " modvalue_no2 = np.nanmean(modobj_tm['no2col'].values, axis = 0)\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:329: RuntimeWarning: invalid value encountered in true_divide\n", + " amf_wrfchem = nume / deno * tamf_org\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Done with TROPOMI regridding 2019-07-14 0\n", - " no2 satellite: 0.0 0.0\n" + "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:88: RuntimeWarning: Mean of empty slice\n", + "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('y', 'x') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n", + " warnings.warn(\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:231: RuntimeWarning: Mean of empty slice\n", " no2_nt[nd,:,:] = np.nanmean(np.where(no2_modgrid_all > 0.0, no2_modgrid_all, np.nan), axis=2)\n", - "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n", - " warnings.warn(\n" + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:329: RuntimeWarning: invalid value encountered in true_divide\n", + " amf_wrfchem = nume / deno * tamf_org\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('y', 'x') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n", + " warnings.warn(\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:329: RuntimeWarning: invalid value encountered in true_divide\n", + " amf_wrfchem = nume / deno * tamf_org\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('y', 'x') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n", + " warnings.warn(\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:302: RuntimeWarning: divide by zero encountered in log10\n", + " f = interpolate.interp1d(np.log10(vertical_pres[:]),vertical_scatw[:], fill_value=\"extrapolate\")# relationship between pressure to avk\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:329: RuntimeWarning: invalid value encountered in true_divide\n", + " amf_wrfchem = nume / deno * tamf_org\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('y', 'x') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n", + " warnings.warn(\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:329: RuntimeWarning: invalid value encountered in true_divide\n", + " amf_wrfchem = nume / deno * tamf_org\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('y', 'x') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n", + " warnings.warn(\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:329: RuntimeWarning: invalid value encountered in true_divide\n", + " amf_wrfchem = nume / deno * tamf_org\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('y', 'x') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n", + " warnings.warn(\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:302: RuntimeWarning: divide by zero encountered in log10\n", + " f = interpolate.interp1d(np.log10(vertical_pres[:]),vertical_scatw[:], fill_value=\"extrapolate\")# relationship between pressure to avk\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:329: RuntimeWarning: invalid value encountered in true_divide\n", + " amf_wrfchem = nume / deno * tamf_org\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('y', 'x') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n", + " warnings.warn(\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:302: RuntimeWarning: divide by zero encountered in log10\n", + " f = interpolate.interp1d(np.log10(vertical_pres[:]),vertical_scatw[:], fill_value=\"extrapolate\")# relationship between pressure to avk\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done with Averaging Kernel revision, factor min: 0.47800988 max: 4.323498\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('y', 'x') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n", + " warnings.warn(\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:302: RuntimeWarning: divide by zero encountered in log10\n", + " f = interpolate.interp1d(np.log10(vertical_pres[:]),vertical_scatw[:], fill_value=\"extrapolate\")# relationship between pressure to avk\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done with Averaging Kernel revision, factor min: 0.16577756 max: 8.095462\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('y', 'x') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n", + " warnings.warn(\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:302: RuntimeWarning: divide by zero encountered in log10\n", + " f = interpolate.interp1d(np.log10(vertical_pres[:]),vertical_scatw[:], fill_value=\"extrapolate\")# relationship between pressure to avk\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done with Averaging Kernel revision, factor min: 0.2888653 max: 7.061387\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('y', 'x') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n", + " warnings.warn(\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:302: RuntimeWarning: divide by zero encountered in log10\n", + " f = interpolate.interp1d(np.log10(vertical_pres[:]),vertical_scatw[:], fill_value=\"extrapolate\")# relationship between pressure to avk\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Done with TROPOMI regridding 2019-07-15 0\n", - " no2 satellite: 0.0 0.0\n", - "Done with TROPOMI regridding 2019-07-15 1\n", - " no2 satellite: 0.0 0.0\n", - "Done with TROPOMI regridding 2019-07-15 2\n", - " no2 satellite: 0.0 0.0\n", - "Done with TROPOMI regridding 2019-07-15 3\n", - " no2 satellite: 0.0 0.0\n", - "Done with TROPOMI regridding 2019-07-15 4\n", - " no2 satellite: 0.0 0.0\n", - "Done with TROPOMI regridding 2019-07-15 5\n", - " no2 satellite: 0.0 0.0\n", - "Done with TROPOMI regridding 2019-07-15 6\n", - " no2 satellite: -1309892300000000.0 1.3650907e+16\n" + "Done with Averaging Kernel revision, factor min: 0.38666221 max: 5.1998305\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n", - " warnings.warn(\n" + "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('y', 'x') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n", + " warnings.warn(\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:329: RuntimeWarning: invalid value encountered in true_divide\n", + " amf_wrfchem = nume / deno * tamf_org\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Done with TROPOMI regridding 2019-07-15 7\n", - " no2 satellite: -3913532600000000.0 2.2299026e+16\n", - "Done with TROPOMI regridding 2019-07-15 8\n", - " no2 satellite: -2818903500000000.0 1.8684126e+16\n", - "Done with TROPOMI regridding 2019-07-15 9\n", - " no2 satellite: -1082265660000000.0 7392744700000000.0\n", - "Done with TROPOMI regridding 2019-07-15 10\n", - " no2 satellite: 0.0 0.0\n" + "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:88: RuntimeWarning: Mean of empty slice\n", + "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('y', 'x') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n", + " warnings.warn(\n", + "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:231: RuntimeWarning: Mean of empty slice\n", " no2_nt[nd,:,:] = np.nanmean(np.where(no2_modgrid_all > 0.0, no2_modgrid_all, np.nan), axis=2)\n" ] } @@ -460,7 +1749,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "37dc4d2a", "metadata": {}, "outputs": [ @@ -828,18 +2117,18 @@ " ll (y) int64 0 1 2 3 4 ... 436 437 438 439\n", "Dimensions without coordinates: y\n", "Data variables:\n", - " nitrogendioxide_tropospheric_column (time, y) float32 7.279e+14 ... 3.10...\n", - " no2trpcol (time, y) float32 5.608e+14 ... 6.98...\n", + " nitrogendioxide_tropospheric_column (time, y) float32 7.235e+14 ... 2.73...\n", " latitude (y) float32 21.19 21.22 ... 50.24 50.2\n", " longitude (y) float32 -122.3 -122.2 ... -60.37\n", + " no2trpcol (time, y) float32 5.606e+14 ... 6.98...\n", "Attributes:\n", - " description: daily tropomi data at model grids" + " description: daily tropomi data at model grids,passing at localtime 13:30" ], "text/plain": [ "\n", @@ -852,15 +2141,15 @@ " ll (y) int64 0 1 2 3 4 ... 436 437 438 439\n", "Dimensions without coordinates: y\n", "Data variables:\n", - " nitrogendioxide_tropospheric_column (time, y) float32 7.279e+14 ... 3.10...\n", - " no2trpcol (time, y) float32 5.608e+14 ... 6.98...\n", + " nitrogendioxide_tropospheric_column (time, y) float32 7.235e+14 ... 2.73...\n", " latitude (y) float32 21.19 21.22 ... 50.24 50.2\n", " longitude (y) float32 -122.3 -122.2 ... -60.37\n", + " no2trpcol (time, y) float32 5.606e+14 ... 6.98...\n", "Attributes:\n", - " description: daily tropomi data at model grids" + " description: daily tropomi data at model grids,passing at localtime 13:30" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -872,7 +2161,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "02aa8f8a", "metadata": {}, "outputs": [ @@ -881,25 +2170,25 @@ "output_type": "stream", "text": [ "Paired TROPOMI NO2: \n", - "array([[7.2789676e+14, 5.0237021e+14, 6.1511361e+14, ..., 5.7081149e+14,\n", - " 5.9785468e+14, 6.4125546e+14],\n", - " [6.7514457e+14, 6.3476610e+14, 6.9058511e+14, ..., 5.8134932e+14,\n", - " 4.8917876e+14, 3.5947947e+14],\n", - " [6.7086309e+14, 7.4024929e+14, 6.8229354e+14, ..., 6.3158803e+14,\n", - " 3.4523021e+14, 4.4847257e+14],\n", + "array([[7.2350751e+14, 4.9604758e+14, 6.1336985e+14, ..., 5.7691584e+14,\n", + " 6.0004485e+14, 6.3978900e+14],\n", + " [6.7961845e+14, 6.3697620e+14, 6.9292680e+14, ..., 6.0560455e+14,\n", + " 5.0066021e+14, 3.5759582e+14],\n", + " [6.7708676e+14, 7.4916974e+14, 6.8801886e+14, ..., 6.5409339e+14,\n", + " 3.5647202e+14, 4.3862501e+14],\n", " ...,\n", - " [8.0331162e+14, 7.3679949e+14, 3.9228205e+14, ..., 4.1725221e+14,\n", - " 4.7237195e+14, 3.8848184e+14],\n", - " [7.7998539e+14, 7.1245521e+14, 7.7136734e+14, ..., 4.5094415e+14,\n", - " 2.7837158e+14, 6.0029832e+14],\n", - " [5.4706535e+14, 1.0423989e+15, 8.2307787e+14, ..., 2.7740053e+14,\n", - " 2.5191384e+14, 3.1055362e+14]], dtype=float32)\n", + " [6.8594648e+14, 5.0632987e+14, 2.9184981e+14, ..., 3.9032381e+14,\n", + " 4.2708256e+14, 3.7429123e+14],\n", + " [6.0560609e+14, 4.7556817e+14, 5.0024820e+14, ..., 3.9216313e+14,\n", + " 2.1985891e+14, 5.0862053e+14],\n", + " [4.5288112e+14, 9.3726226e+14, 5.8744227e+14, ..., 2.6180060e+14,\n", + " 2.3501170e+14, 2.7386159e+14]], dtype=float32)\n", "Coordinates:\n", " time datetime64[ns] 2019-07-15\n", " lon (x, ll) float32 -122.3 -122.2 -122.1 ... -60.68 -60.52 -60.37\n", " lat (x, ll) float32 21.19 21.22 21.24 21.27 ... 50.33 50.28 50.24 50.2\n", " * x (x) int64 0 1 2 3 4 5 6 7 8 ... 275 276 277 278 279 280 281 282 283\n", - " * ll (ll) int64 0 1 2 3 4 5 6 7 8 ... 432 433 434 435 436 437 438 439 11667814000.0 2.2299026e+16\n" + " * ll (ll) int64 0 1 2 3 4 5 6 7 8 ... 432 433 434 435 436 437 438 439 6191588000.0 2.7297904e+16\n" ] } ], @@ -914,13 +2203,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "dddbb49b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -943,7 +2232,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "b352bf65", "metadata": {}, "outputs": [ @@ -952,25 +2241,25 @@ "output_type": "stream", "text": [ "Paired WRF-Chem NO2: \n", - "array([[5.6082549e+14, 5.6167260e+14, 5.6204234e+14, ..., 5.0799893e+14,\n", - " 5.1312666e+14, 5.1356541e+14],\n", - " [4.3689763e+14, 4.3674170e+14, 4.3980378e+14, ..., 4.4611728e+14,\n", - " 4.5124336e+14, 5.9029769e+14],\n", - " [4.4412458e+14, 4.4394268e+14, 4.4651554e+14, ..., 4.3647317e+14,\n", - " 4.4146513e+14, 5.8461524e+14],\n", + "array([[5.6063946e+14, 5.6148628e+14, 5.6185598e+14, ..., 5.0786596e+14,\n", + " 5.1299150e+14, 5.1343012e+14],\n", + " [4.3677925e+14, 4.3662336e+14, 4.3968456e+14, ..., 4.4600756e+14,\n", + " 4.5113108e+14, 5.9011710e+14],\n", + " [4.4400607e+14, 4.4382407e+14, 4.4639642e+14, ..., 4.3636465e+14,\n", + " 4.4135456e+14, 5.8443506e+14],\n", " ...,\n", - " [6.1838302e+14, 5.8272177e+14, 6.3751018e+14, ..., 7.8216743e+14,\n", - " 7.7528978e+14, 7.7019125e+14],\n", - " [6.0601183e+14, 5.6466828e+14, 5.7435168e+14, ..., 6.6657966e+14,\n", - " 6.9247543e+14, 7.3247808e+14],\n", - " [6.1570953e+14, 5.6744309e+14, 5.6898807e+14, ..., 6.5825655e+14,\n", - " 6.7548454e+14, 6.9849134e+14]], dtype=float32)\n", + " [6.1816444e+14, 5.8253554e+14, 6.3732496e+14, ..., 7.8202738e+14,\n", + " 7.7514617e+14, 7.7002522e+14],\n", + " [6.0579326e+14, 5.6447869e+14, 5.7416452e+14, ..., 6.6643739e+14,\n", + " 6.9233309e+14, 7.3231373e+14],\n", + " [6.1549170e+14, 5.6722579e+14, 5.6877071e+14, ..., 6.5810026e+14,\n", + " 6.7532677e+14, 6.9833242e+14]], dtype=float32)\n", "Coordinates:\n", " time datetime64[ns] 2019-07-15\n", " lon (x, ll) float32 -122.3 -122.2 -122.1 ... -60.68 -60.52 -60.37\n", " lat (x, ll) float32 21.19 21.22 21.24 21.27 ... 50.33 50.28 50.24 50.2\n", " * x (x) int64 0 1 2 3 4 5 6 7 8 ... 275 276 277 278 279 280 281 282 283\n", - " * ll (ll) int64 0 1 2 3 4 5 6 7 8 ... 432 433 434 435 436 437 438 439 209019260000000.0 2.06299e+16\n" + " * ll (ll) int64 0 1 2 3 4 5 6 7 8 ... 432 433 434 435 436 437 438 439 208949120000000.0 2.0629314e+16\n" ] } ], @@ -983,13 +2272,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "id": "54f394e8", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1012,7 +2301,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "id": "a3543089", "metadata": {}, "outputs": [ @@ -1031,7 +2320,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "id": "1d44d3ca", "metadata": {}, "outputs": [ @@ -1407,16 +2696,16 @@ "Dimensions without coordinates: y\n", "Data variables:\n", " nitrogendioxide_tropospheric_column (time, y) float32 ...\n", - " no2trpcol (time, y) float32 ...\n", " latitude (y) float32 ...\n", " longitude (y) float32 ...\n", + " no2trpcol (time, y) float32 ...\n", "Attributes:\n", - " description: daily tropomi data at model grids\n", + " description: daily tropomi data at model grids,passing at localtime 13:30\n", " title: \n", " format: NetCDF-4\n", - " date_created: 2024-01-08\n", + " date_created: 2024-10-03\n", " dict_json: {\\n "type": "sat_swath_clm",\\n "radius_of_influence"...\n", - " group_name: tropomi_l2_no2_wrfchem_v4.2