Planeflight diagnostics from GEOS-Chem plane.log.YYYYMMDD

GC_plane_diag.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import cartopy.crs as ccrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TYPE</th>\n",
       "      <th>YYYYMMDD</th>\n",
       "      <th>HHMM</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>PRESS</th>\n",
       "      <th>TRA_001</th>\n",
       "      <th>TRA_002</th>\n",
       "      <th>TRA_003</th>\n",
       "      <th>TRA_004</th>\n",
       "      <th>...</th>\n",
       "      <th>GMAO_SURF</th>\n",
       "      <th>GMAO_PSFC</th>\n",
       "      <th>GMAO_UWND</th>\n",
       "      <th>GMAO_VWND</th>\n",
       "      <th>GMAO_RH</th>\n",
       "      <th>ISOR_HPLUS</th>\n",
       "      <th>ISOR_PH</th>\n",
       "      <th>ISOR_AH2O</th>\n",
       "      <th>OH</th>\n",
       "      <th>HO2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POINT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DC8</td>\n",
       "      <td>20160817</td>\n",
       "      <td>804</td>\n",
       "      <td>-7.98</td>\n",
       "      <td>-14.38</td>\n",
       "      <td>1004.90</td>\n",
       "      <td>1.987000e-13</td>\n",
       "      <td>6.219000e-08</td>\n",
       "      <td>1.472000e-12</td>\n",
       "      <td>3.276000e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>-7.717</td>\n",
       "      <td>3.370</td>\n",
       "      <td>74.35</td>\n",
       "      <td>0.003305</td>\n",
       "      <td>2.481</td>\n",
       "      <td>0.5688</td>\n",
       "      <td>97300.0</td>\n",
       "      <td>26930000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DC8</td>\n",
       "      <td>20160817</td>\n",
       "      <td>804</td>\n",
       "      <td>-7.98</td>\n",
       "      <td>-14.38</td>\n",
       "      <td>997.31</td>\n",
       "      <td>2.081000e-13</td>\n",
       "      <td>6.228000e-08</td>\n",
       "      <td>1.573000e-12</td>\n",
       "      <td>3.278000e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>-8.108</td>\n",
       "      <td>3.477</td>\n",
       "      <td>78.02</td>\n",
       "      <td>0.004975</td>\n",
       "      <td>2.303</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>98150.0</td>\n",
       "      <td>27050000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DC8</td>\n",
       "      <td>20160817</td>\n",
       "      <td>804</td>\n",
       "      <td>-7.98</td>\n",
       "      <td>-14.37</td>\n",
       "      <td>988.14</td>\n",
       "      <td>2.081000e-13</td>\n",
       "      <td>6.228000e-08</td>\n",
       "      <td>1.573000e-12</td>\n",
       "      <td>3.278000e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>-8.108</td>\n",
       "      <td>3.477</td>\n",
       "      <td>78.02</td>\n",
       "      <td>0.004975</td>\n",
       "      <td>2.303</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>98150.0</td>\n",
       "      <td>27050000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DC8</td>\n",
       "      <td>20160817</td>\n",
       "      <td>804</td>\n",
       "      <td>-7.99</td>\n",
       "      <td>-14.36</td>\n",
       "      <td>979.92</td>\n",
       "      <td>2.174000e-13</td>\n",
       "      <td>6.234000e-08</td>\n",
       "      <td>1.688000e-12</td>\n",
       "      <td>3.281000e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>-8.339</td>\n",
       "      <td>3.495</td>\n",
       "      <td>81.83</td>\n",
       "      <td>0.005792</td>\n",
       "      <td>2.237</td>\n",
       "      <td>0.7165</td>\n",
       "      <td>98690.0</td>\n",
       "      <td>26850000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DC8</td>\n",
       "      <td>20160817</td>\n",
       "      <td>805</td>\n",
       "      <td>-7.99</td>\n",
       "      <td>-14.35</td>\n",
       "      <td>971.31</td>\n",
       "      <td>2.268000e-13</td>\n",
       "      <td>6.241000e-08</td>\n",
       "      <td>1.806000e-12</td>\n",
       "      <td>3.284000e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>-8.494</td>\n",
       "      <td>3.470</td>\n",
       "      <td>85.83</td>\n",
       "      <td>0.005588</td>\n",
       "      <td>2.253</td>\n",
       "      <td>0.8696</td>\n",
       "      <td>98620.0</td>\n",
       "      <td>26330000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      TYPE  YYYYMMDD  HHMM   LAT    LON    PRESS       TRA_001       TRA_002  \\\n",
       "POINT                                                                          \n",
       "1      DC8  20160817   804 -7.98 -14.38  1004.90  1.987000e-13  6.219000e-08   \n",
       "2      DC8  20160817   804 -7.98 -14.38   997.31  2.081000e-13  6.228000e-08   \n",
       "3      DC8  20160817   804 -7.98 -14.37   988.14  2.081000e-13  6.228000e-08   \n",
       "4      DC8  20160817   804 -7.99 -14.36   979.92  2.174000e-13  6.234000e-08   \n",
       "5      DC8  20160817   805 -7.99 -14.35   971.31  2.268000e-13  6.241000e-08   \n",
       "\n",
       "            TRA_003       TRA_004     ...      GMAO_SURF  GMAO_PSFC  \\\n",
       "POINT                                 ...                             \n",
       "1      1.472000e-12  3.276000e-07     ...       0.000001     1017.0   \n",
       "2      1.573000e-12  3.278000e-07     ...       0.000001     1017.0   \n",
       "3      1.573000e-12  3.278000e-07     ...       0.000001     1017.0   \n",
       "4      1.688000e-12  3.281000e-07     ...       0.000001     1017.0   \n",
       "5      1.806000e-12  3.284000e-07     ...       0.000001     1017.0   \n",
       "\n",
       "       GMAO_UWND  GMAO_VWND  GMAO_RH  ISOR_HPLUS  ISOR_PH  ISOR_AH2O       OH  \\\n",
       "POINT                                                                           \n",
       "1         -7.717      3.370    74.35    0.003305    2.481     0.5688  97300.0   \n",
       "2         -8.108      3.477    78.02    0.004975    2.303     0.6250  98150.0   \n",
       "3         -8.108      3.477    78.02    0.004975    2.303     0.6250  98150.0   \n",
       "4         -8.339      3.495    81.83    0.005792    2.237     0.7165  98690.0   \n",
       "5         -8.494      3.470    85.83    0.005588    2.253     0.8696  98620.0   \n",
       "\n",
       "              HO2  \n",
       "POINT              \n",
       "1      26930000.0  \n",
       "2      27050000.0  \n",
       "3      27050000.0  \n",
       "4      26850000.0  \n",
       "5      26330000.0  \n",
       "\n",
       "[5 rows x 135 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./plane.log', delim_whitespace=True, index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3076, 135)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['TYPE', 'YYYYMMDD', 'HHMM', 'LAT', 'LON', 'PRESS', 'TRA_001', 'TRA_002', 'TRA_003', 'TRA_004', 'TRA_005', 'TRA_006', 'TRA_007', 'TRA_008', 'TRA_009', 'TRA_010', 'TRA_011', 'TRA_012', 'TRA_013', 'TRA_014', 'TRA_015', 'TRA_016', 'TRA_017', 'TRA_018', 'TRA_019', 'TRA_020', 'TRA_021', 'TRA_022', 'TRA_023', 'TRA_024', 'TRA_025', 'TRA_026', 'TRA_027', 'TRA_028', 'TRA_029', 'TRA_030', 'TRA_031', 'TRA_032', 'TRA_033', 'TRA_034', 'TRA_035', 'TRA_036', 'TRA_037', 'TRA_038', 'TRA_039', 'TRA_040', 'TRA_041', 'TRA_042', 'TRA_043', 'TRA_044', 'TRA_045', 'TRA_046', 'TRA_047', 'TRA_048', 'TRA_049', 'TRA_050', 'TRA_051', 'TRA_052', 'TRA_053', 'TRA_054', 'TRA_055', 'TRA_056', 'TRA_057', 'TRA_058', 'TRA_059', 'TRA_060', 'TRA_061', 'TRA_062', 'TRA_063', 'TRA_064', 'TRA_065', 'TRA_066', 'TRA_067', 'TRA_068', 'TRA_069', 'TRA_070', 'TRA_071', 'TRA_072', 'TRA_073', 'TRA_074', 'TRA_075', 'TRA_076', 'TRA_077', 'TRA_078', 'TRA_079', 'TRA_080', 'TRA_081', 'TRA_082', 'TRA_083', 'TRA_084', 'TRA_085', 'TRA_086', 'TRA_087', 'TRA_088', 'TRA_089', 'TRA_090', 'TRA_091', 'TRA_092', 'TRA_093', 'TRA_094', 'TRA_095', 'TRA_096', 'TRA_097', 'TRA_098', 'TRA_099', 'TRA_100', 'TRA_101', 'TRA_102', 'TRA_103', 'TRA_104', 'TRA_105', 'TRA_106', 'TRA_107', 'TRA_108', 'TRA_109', 'TRA_110', 'TRA_111', 'TRA_112', 'TRA_113', 'TRA_114', 'TRA_115', 'TRA_116', 'TRA_117', 'GMAO_TEMP', 'GMAO_ABSH', 'GMAO_SURF', 'GMAO_PSFC', 'GMAO_UWND', 'GMAO_VWND', 'GMAO_RH', 'ISOR_HPLUS', 'ISOR_PH', 'ISOR_AH2O', 'OH', 'HO2']\n"
     ]
    }
   ],
   "source": [
    "# all variables\n",
    "print(list(df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f151550>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# time series\n",
    "df.plot(y=['OH', 'HO2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fb2bcc0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the trajectory\n",
    "df.plot(x='LON', y='LAT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting on map looks better\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.coastlines()\n",
    "ax.plot(df['LON'], df['LAT'])\n",
    "ax.set_global()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (POINT: 3076)\n",
       "Coordinates:\n",
       "  * POINT       (POINT) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ...\n",
       "Data variables:\n",
       "    TYPE        (POINT) object 'DC8' 'DC8' 'DC8' 'DC8' 'DC8' 'DC8' 'DC8' ...\n",
       "    YYYYMMDD    (POINT) int64 20160817 20160817 20160817 20160817 20160817 ...\n",
       "    HHMM        (POINT) int64 804 804 804 804 805 805 805 805 805 805 806 ...\n",
       "    LAT         (POINT) float64 -7.98 -7.98 -7.98 -7.99 -7.99 -8.0 -8.01 ...\n",
       "    LON         (POINT) float64 -14.38 -14.38 -14.37 -14.36 -14.35 -14.35 ...\n",
       "    PRESS       (POINT) float64 1.005e+03 997.3 988.1 979.9 971.3 964.7 ...\n",
       "    TRA_001     (POINT) float64 1.987e-13 2.081e-13 2.081e-13 2.174e-13 ...\n",
       "    TRA_002     (POINT) float64 6.219e-08 6.228e-08 6.228e-08 6.234e-08 ...\n",
       "    TRA_003     (POINT) float64 1.472e-12 1.573e-12 1.573e-12 1.688e-12 ...\n",
       "    TRA_004     (POINT) float64 3.276e-07 3.278e-07 3.278e-07 3.281e-07 ...\n",
       "    TRA_005     (POINT) float64 5.542e-10 5.547e-10 5.547e-10 5.558e-10 ...\n",
       "    TRA_006     (POINT) float64 1.61e-18 1.614e-18 1.614e-18 1.618e-18 ...\n",
       "    TRA_007     (POINT) float64 1.669e-10 1.739e-10 1.739e-10 1.762e-10 ...\n",
       "    TRA_008     (POINT) float64 2.45e-10 2.504e-10 2.504e-10 2.51e-10 ...\n",
       "    TRA_009     (POINT) float64 2.4e-09 2.409e-09 2.409e-09 2.416e-09 ...\n",
       "    TRA_010     (POINT) float64 1.403e-10 1.406e-10 1.406e-10 1.411e-10 ...\n",
       "    TRA_011     (POINT) float64 5.922e-10 5.792e-10 5.792e-10 5.74e-10 ...\n",
       "    TRA_012     (POINT) float64 3.846e-12 3.843e-12 3.843e-12 3.84e-12 ...\n",
       "    TRA_013     (POINT) float64 1.125e-14 1.145e-14 1.145e-14 1.175e-14 ...\n",
       "    TRA_014     (POINT) float64 5.285e-14 5.378e-14 5.378e-14 5.527e-14 ...\n",
       "    TRA_015     (POINT) float64 1.718e-19 1.832e-19 1.832e-19 1.962e-19 ...\n",
       "    TRA_016     (POINT) float64 5.52e-14 5.88e-14 5.88e-14 6.289e-14 ...\n",
       "    TRA_017     (POINT) float64 3.985e-14 4.051e-14 4.051e-14 4.067e-14 ...\n",
       "    TRA_018     (POINT) float64 1.466e-13 1.373e-13 1.373e-13 1.338e-13 ...\n",
       "    TRA_019     (POINT) float64 3.908e-10 3.91e-10 3.91e-10 3.914e-10 ...\n",
       "    TRA_020     (POINT) float64 1.123e-10 1.139e-10 1.139e-10 1.142e-10 ...\n",
       "    TRA_021     (POINT) float64 3.544e-09 3.546e-09 3.546e-09 3.55e-09 ...\n",
       "    TRA_022     (POINT) float64 7.018e-17 7.759e-17 7.759e-17 8.55e-17 ...\n",
       "    TRA_023     (POINT) float64 1.967e-15 2.297e-15 2.297e-15 2.644e-15 ...\n",
       "    TRA_024     (POINT) float64 4.419e-10 4.417e-10 4.417e-10 4.414e-10 ...\n",
       "    TRA_025     (POINT) float64 2.202e-10 2.158e-10 2.158e-10 2.139e-10 ...\n",
       "    TRA_026     (POINT) float64 1.376e-11 1.354e-11 1.354e-11 1.328e-11 ...\n",
       "    TRA_027     (POINT) float64 7.67e-11 7.742e-11 7.742e-11 7.733e-11 ...\n",
       "    TRA_028     (POINT) float64 2.684e-12 2.703e-12 2.703e-12 2.69e-12 ...\n",
       "    TRA_029     (POINT) float64 4.759e-12 4.781e-12 4.781e-12 4.755e-12 ...\n",
       "    TRA_030     (POINT) float64 1.724e-10 1.699e-10 1.699e-10 1.673e-10 ...\n",
       "    TRA_031     (POINT) float64 2.43e-12 5.471e-12 5.471e-12 8.103e-12 ...\n",
       "    TRA_032     (POINT) float64 9.599e-12 8.155e-12 8.155e-12 8.279e-12 ...\n",
       "    TRA_033     (POINT) float64 2.25e-11 2.27e-11 2.27e-11 2.263e-11 ...\n",
       "    TRA_034     (POINT) float64 7.642e-11 7.847e-11 7.847e-11 7.987e-11 ...\n",
       "    TRA_035     (POINT) float64 5.109e-10 5.248e-10 5.248e-10 5.344e-10 ...\n",
       "    TRA_036     (POINT) float64 8.555e-14 8.743e-14 8.743e-14 9.027e-14 ...\n",
       "    TRA_037     (POINT) float64 3.574e-13 3.654e-13 3.654e-13 3.776e-13 ...\n",
       "    TRA_038     (POINT) float64 1.027e-10 1.029e-10 1.029e-10 1.03e-10 ...\n",
       "    TRA_039     (POINT) float64 2.893e-11 2.919e-11 2.919e-11 2.909e-11 ...\n",
       "    TRA_040     (POINT) float64 3.636e-11 3.667e-11 3.667e-11 3.653e-11 ...\n",
       "    TRA_041     (POINT) float64 3.255e-12 3.288e-12 3.288e-12 3.276e-12 ...\n",
       "    TRA_042     (POINT) float64 3.888e-10 3.809e-10 3.809e-10 3.749e-10 ...\n",
       "    TRA_043     (POINT) float64 8.282e-09 7.934e-09 7.934e-09 7.7e-09 ...\n",
       "    TRA_044     (POINT) float64 2.233e-14 2.303e-14 2.303e-14 2.43e-14 ...\n",
       "    TRA_045     (POINT) float64 6.954e-15 7.314e-15 7.314e-15 7.702e-15 ...\n",
       "    TRA_046     (POINT) float64 1.114e-12 1.13e-12 1.13e-12 1.143e-12 ...\n",
       "    TRA_047     (POINT) float64 5.392e-13 5.316e-13 5.316e-13 5.087e-13 ...\n",
       "    TRA_048     (POINT) float64 2.399e-13 2.723e-13 2.723e-13 3.066e-13 ...\n",
       "    TRA_049     (POINT) float64 2.71e-15 2.693e-15 2.693e-15 2.676e-15 ...\n",
       "    TRA_050     (POINT) float64 7.321e-13 7.161e-13 7.161e-13 6.807e-13 ...\n",
       "    TRA_051     (POINT) float64 1.816e-12 1.803e-12 1.803e-12 1.797e-12 ...\n",
       "    TRA_052     (POINT) float64 3.204e-12 3.201e-12 3.201e-12 3.2e-12 ...\n",
       "    TRA_053     (POINT) float64 6.94e-12 6.94e-12 6.94e-12 6.94e-12 6.94e-12 ...\n",
       "    TRA_054     (POINT) float64 1.231e-15 1.391e-15 1.391e-15 1.577e-15 ...\n",
       "    TRA_055     (POINT) float64 5.879e-22 6.049e-22 6.049e-22 6.086e-22 ...\n",
       "    TRA_056     (POINT) float64 7.558e-20 7.732e-20 7.732e-20 7.736e-20 ...\n",
       "    TRA_057     (POINT) float64 9.852e-20 1.014e-19 1.014e-19 1.044e-19 ...\n",
       "    TRA_058     (POINT) float64 5.344e-16 5.586e-16 5.586e-16 5.736e-16 ...\n",
       "    TRA_059     (POINT) float64 7.002e-12 7.232e-12 7.232e-12 7.324e-12 ...\n",
       "    TRA_060     (POINT) float64 4.019e-13 4.163e-13 4.163e-13 4.251e-13 ...\n",
       "    TRA_061     (POINT) float64 1.947e-17 2e-17 2e-17 2.011e-17 2.011e-17 ...\n",
       "    TRA_062     (POINT) float64 1.014e-19 1.047e-19 1.047e-19 1.058e-19 ...\n",
       "    TRA_063     (POINT) float64 5.421e-11 5.476e-11 5.476e-11 5.497e-11 ...\n",
       "    TRA_064     (POINT) float64 3.654e-12 3.6e-12 3.6e-12 3.549e-12 ...\n",
       "    TRA_065     (POINT) float64 1.746e-14 1.68e-14 1.68e-14 1.614e-14 ...\n",
       "    TRA_066     (POINT) float64 1.806e-13 1.791e-13 1.791e-13 1.781e-13 ...\n",
       "    TRA_067     (POINT) float64 5.039e-10 5.05e-10 5.05e-10 5.069e-10 ...\n",
       "    TRA_068     (POINT) float64 3.201e-11 3.221e-11 3.221e-11 3.255e-11 ...\n",
       "    TRA_069     (POINT) float64 1.017e-12 1.004e-12 1.004e-12 9.986e-13 ...\n",
       "    TRA_070     (POINT) float64 1.981e-26 1.995e-26 1.995e-26 2.012e-26 ...\n",
       "    TRA_071     (POINT) float64 6.394e-33 6.482e-33 6.482e-33 6.563e-33 ...\n",
       "    TRA_072     (POINT) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    TRA_073     (POINT) float64 4.361e-13 4.316e-13 4.316e-13 4.219e-13 ...\n",
       "    TRA_074     (POINT) float64 1.025e-11 1.051e-11 1.051e-11 1.06e-11 ...\n",
       "    TRA_075     (POINT) float64 1.973e-11 2.039e-11 2.039e-11 2.071e-11 ...\n",
       "    TRA_076     (POINT) float64 2.166e-13 2.048e-13 2.048e-13 1.921e-13 ...\n",
       "    TRA_077     (POINT) float64 2.945e-13 3.168e-13 3.168e-13 3.349e-13 ...\n",
       "    TRA_078     (POINT) float64 6.924e-13 7.766e-13 7.766e-13 8.456e-13 ...\n",
       "    TRA_079     (POINT) float64 1.332e-13 1.515e-13 1.515e-13 1.659e-13 ...\n",
       "    TRA_080     (POINT) float64 1.463e-12 1.499e-12 1.499e-12 1.521e-12 ...\n",
       "    TRA_081     (POINT) float64 2.012e-12 1.991e-12 1.991e-12 1.944e-12 ...\n",
       "    TRA_082     (POINT) float64 3.057e-23 3.108e-23 3.108e-23 3.103e-23 ...\n",
       "    TRA_083     (POINT) float64 9.101e-12 9.361e-12 9.361e-12 9.457e-12 ...\n",
       "    TRA_084     (POINT) float64 1.359e-12 1.461e-12 1.461e-12 1.543e-12 ...\n",
       "    TRA_085     (POINT) float64 2.064e-24 2.283e-24 2.283e-24 2.465e-24 ...\n",
       "    TRA_086     (POINT) float64 6.145e-14 6.926e-14 6.926e-14 7.554e-14 ...\n",
       "    TRA_087     (POINT) float64 2.812e-13 2.784e-13 2.784e-13 2.723e-13 ...\n",
       "    TRA_088     (POINT) float64 2.981e-13 3.063e-13 3.063e-13 3.098e-13 ...\n",
       "    TRA_089     (POINT) float64 3.997e-12 4.108e-12 4.108e-12 4.147e-12 ...\n",
       "    TRA_090     (POINT) float64 3.198e-12 3.224e-12 3.224e-12 3.224e-12 ...\n",
       "    TRA_091     (POINT) float64 1.899e-13 2.043e-13 2.043e-13 2.161e-13 ...\n",
       "    TRA_092     (POINT) float64 2.013e-14 2.265e-14 2.265e-14 2.473e-14 ...\n",
       "    TRA_093     (POINT) float64 2.699e-14 3.035e-14 3.035e-14 3.309e-14 ...\n",
       "    TRA_094     (POINT) float64 1.116e-10 1.122e-10 1.122e-10 1.129e-10 ...\n",
       "    TRA_095     (POINT) float64 2.961e-13 3.005e-13 3.005e-13 3.018e-13 ...\n",
       "    TRA_096     (POINT) float64 9.397e-14 9.615e-14 9.615e-14 9.648e-14 ...\n",
       "    TRA_097     (POINT) float64 8.428e-14 7.91e-14 7.91e-14 7.538e-14 ...\n",
       "    TRA_098     (POINT) float64 1.145e-15 1.192e-15 1.192e-15 1.246e-15 ...\n",
       "    TRA_099     (POINT) float64 4.369e-16 4.442e-16 4.442e-16 4.426e-16 ...\n",
       "    TRA_100     (POINT) float64 6.81e-21 7.076e-21 7.076e-21 7.2e-21 ...\n",
       "    TRA_101     (POINT) float64 8.773e-16 9.066e-16 9.066e-16 9.285e-16 ...\n",
       "    TRA_102     (POINT) float64 1.271e-12 1.311e-12 1.311e-12 1.326e-12 ...\n",
       "    TRA_103     (POINT) float64 2.062e-13 2.137e-13 2.137e-13 2.172e-13 ...\n",
       "    TRA_104     (POINT) float64 6.189e-14 6.381e-14 6.381e-14 6.45e-14 ...\n",
       "    TRA_105     (POINT) float64 6.178e-20 6.431e-20 6.431e-20 6.57e-20 ...\n",
       "    TRA_106     (POINT) float64 3.892e-17 4.101e-17 4.101e-17 4.244e-17 ...\n",
       "    TRA_107     (POINT) float64 1.379e-18 1.434e-18 1.434e-18 1.463e-18 ...\n",
       "    TRA_108     (POINT) float64 1.876e-19 1.95e-19 1.95e-19 1.99e-19 ...\n",
       "    TRA_109     (POINT) float64 2.504e-11 2.572e-11 2.572e-11 2.592e-11 ...\n",
       "    TRA_110     (POINT) float64 2.018e-11 2.028e-11 2.028e-11 2.02e-11 ...\n",
       "    TRA_111     (POINT) float64 2.058e-12 2.068e-12 2.068e-12 2.061e-12 ...\n",
       "    TRA_112     (POINT) float64 2.84e-12 2.905e-12 2.905e-12 2.955e-12 ...\n",
       "    TRA_113     (POINT) float64 2.687e-15 2.733e-15 2.733e-15 2.76e-15 ...\n",
       "    TRA_114     (POINT) float64 1.072e-21 1.123e-21 1.123e-21 1.125e-21 ...\n",
       "    TRA_115     (POINT) float64 6.133e-13 6.174e-13 6.174e-13 6.163e-13 ...\n",
       "    TRA_116     (POINT) float64 2.7e-24 2.776e-24 2.776e-24 2.742e-24 ...\n",
       "    TRA_117     (POINT) float64 1.366e-13 1.392e-13 1.392e-13 1.409e-13 ...\n",
       "    GMAO_TEMP   (POINT) float64 296.3 294.9 294.9 293.6 292.4 292.4 292.4 ...\n",
       "    GMAO_ABSH   (POINT) float64 1.449e+16 1.322e+16 1.322e+16 1.21e+16 ...\n",
       "    GMAO_SURF   (POINT) float64 1.022e-06 1.051e-06 1.051e-06 1.125e-06 ...\n",
       "    GMAO_PSFC   (POINT) float64 1.017e+03 1.017e+03 1.017e+03 1.017e+03 ...\n",
       "    GMAO_UWND   (POINT) float64 -7.717 -8.108 -8.108 -8.339 -8.494 -8.494 ...\n",
       "    GMAO_VWND   (POINT) float64 3.37 3.477 3.477 3.495 3.47 3.47 3.47 3.47 ...\n",
       "    GMAO_RH     (POINT) float64 74.35 78.02 78.02 81.83 85.83 85.83 85.83 ...\n",
       "    ISOR_HPLUS  (POINT) float64 0.003305 0.004975 0.004975 0.005792 0.005588 ...\n",
       "    ISOR_PH     (POINT) float64 2.481 2.303 2.303 2.237 2.253 2.253 2.253 ...\n",
       "    ISOR_AH2O   (POINT) float64 0.5688 0.625 0.625 0.7165 0.8696 0.8696 ...\n",
       "    OH          (POINT) float64 9.73e+04 9.815e+04 9.815e+04 9.869e+04 ...\n",
       "    HO2         (POINT) float64 2.693e+07 2.705e+07 2.705e+07 2.685e+07 ..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# or convert it to xarray if you are more familiar with it...\n",
    "df.to_xarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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