create_global_inventory_from_regional.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Global Inventory from Regional"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "from cartopy import crs\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings; warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in regional inventory and print file info\n",
    "xarray will automatically convert time values in the file to type datetime[ns]. If the times are converted and then used in the global dataset then the time variable encoding must also be used. The encoding contains metadata necessary for conversion back to the original netcdf units. However, even if you do this, the times will be written to file as int64 which is incompatible with MAPL. It is easiest simply to avoid the conversion in the first place and manually assign attributes. Turn off the conversion by passing decode_times=False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 700, lon: 1250, time: 26)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -174.95 -174.85 -174.75 -174.65 -174.55 -174.45 ...\n",
       "  * lat      (lat) float32 15.05 15.15 15.25 15.35 15.45 15.55 15.65 15.75 ...\n",
       "  * time     (time) float32 0.0 8760.0 17520.0 26280.0 35064.0 43824.0 ...\n",
       "Data variables:\n",
       "    CO       (time, lat, lon) float32 ...\n",
       "    SOx      (time, lat, lon) float32 ...\n",
       "    NOx      (time, lat, lon) float32 ...\n",
       "    NH3      (time, lat, lon) float32 ...\n",
       "    VOC      (time, lat, lon) float32 ...\n",
       "    OC       (time, lat, lon) float32 ...\n",
       "    EC       (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Title:        Historical emission from CAC (Anthropogenic)\n",
       "    Conventions:  COARDS\n",
       "    Format:       NetCDF\n",
       "    Model:        GENERIC\n",
       "    NLayers:      1\n",
       "    Contact:      Chi Li (chi.li@dal.ca)\n",
       "    History:      2016-11-1\n",
       "    Start_Date:   1989-01-01\n",
       "    Start_Time:   0\n",
       "    End_Date:     2015-01-01\n",
       "    End_Time:     0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orig_file = '/n/home08/elundgren/ExtData/HEMCO/APEI/v2016-11/APEI.0.1x0.1.nc'\n",
    "ds_regional = xr.open_dataset(orig_file, decode_times=False)\n",
    "ds_regional"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot regional inventory variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iLlBOMXrq8MCnwQPAgB5XOmCFNSpU5HEOUIy8AT1nSUEtNVhjSwkFZKK4MSIYigsdUAkV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ba434280f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = ds_regional['CO'].isel(time=10)\n",
    "vmin = 0\n",
    "vmax = ds.mean() + ds.var() # adjust as needed so data shows up\n",
    "\n",
    "ax = plt.axes(projection=crs.PlateCarree())\n",
    "ax.coastlines()  # overlay continents\n",
    "ax.gridlines(draw_labels=True, linestyle='--')\n",
    "im = ax.pcolormesh(ds_regional['lon'], ds_regional['lat'], ds, vmin=vmin, vmax=vmax)\n",
    "ax.set_global()\n",
    "plt.colorbar(im, orientation='horizontal', shrink=0.8);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define function to make global lat/lon half-polar grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_grid_LL(llres):\n",
    "    [dlat,dlon] = list(map(float, llres.split('x')))\n",
    "    lon_b = np.linspace(-180 - dlon/2, 180 - dlon/2, int(360/dlon) + 1, endpoint=True)\n",
    "    lat_b = np.linspace(-90 - dlat/2, 90 + dlat/2, int(180/dlat) + 2, endpoint=True).clip(-90,90)\n",
    "    lat = (lat_b[1:] + lat_b[:-1]) / 2\n",
    "    lon = (lon_b[1:] + lon_b[:-1]) / 2\n",
    "    llgrid = {'lat': lat, 'lon': lon, 'lat_b':lat_b, 'lon_b':lon_b}\n",
    "    return llgrid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a global grid at the same resolution as the regional file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lat': array([-89.975, -89.9  , -89.8  , ...,  89.8  ,  89.9  ,  89.975]),\n",
       " 'lat_b': array([-90.  , -89.95, -89.85, ...,  89.85,  89.95,  90.  ]),\n",
       " 'lon': array([-180. , -179.9, -179.8, ...,  179.7,  179.8,  179.9]),\n",
       " 'lon_b': array([-180.05, -179.95, -179.85, ...,  179.75,  179.85,  179.95])}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_res = '0.1x0.1'\n",
    "global_grid = make_grid_LL(grid_res)\n",
    "global_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity check the grids and find regional index bounds in global array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid resolution: 0.1x0.1\n",
      "Longitude [min, max, size]: [-180.0, 179.90000000000001, 3600]\n",
      "Latitude [min, max, size]: [-89.974999999999994, 89.974999999999994, 1801]\n"
     ]
    }
   ],
   "source": [
    "# First, print basic information about the global grid\n",
    "print('Grid resolution: {}'.format(grid_res))\n",
    "print('Longitude [min, max, size]: {}'.format([global_grid['lon'].min(), \n",
    "                                                 global_grid['lon'].max(), \n",
    "                                                 global_grid['lon'].size]))\n",
    "print('Latitude [min, max, size]: {}'.format([global_grid['lat'].min(), \n",
    "                                                 global_grid['lat'].max(), \n",
    "                                                 global_grid['lat'].size]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min lon regional: -174.9499969482422\n",
      "Max lon regional: -50.04998779296875\n",
      "Number longitudes: 1250\n"
     ]
    }
   ],
   "source": [
    "# Show info about longitudes in the original regional file\n",
    "print('Min lon regional: {}'.format(ds_regional['lon'].values.min()))\n",
    "print('Max lon regional: {}'.format(ds_regional['lon'].values.max()))\n",
    "print('Number longitudes: {}'.format(np.size(ds_regional['lon'].values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lat': array([-90.  , -89.95, -89.85, ...,  89.85,  89.95,  90.  ]),\n",
       " 'lon': array([-179.95, -179.85, -179.75, ...,  179.75,  179.85,  179.95])}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looks like this inventory is actually polar and dateline edge, not half-polar\n",
    "# Adjust grid accordingly\n",
    "edge_lats = global_grid['lat_b']\n",
    "edge_lons = global_grid['lon_b'][1:]\n",
    "global_grid = {'lat':edge_lats,'lon':edge_lons}\n",
    "global_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Longitude [min, max, size]: [-179.95000000000002, 179.94999999999999, 3600]\n",
      "Latitude [min, max, size]: [-90.0, 90.0, 1802]\n"
     ]
    }
   ],
   "source": [
    "# Print out the new global grid to inspect the bounds and size\n",
    "print('Longitude [min, max, size]: {}'.format([global_grid['lon'].min(), \n",
    "                                                 global_grid['lon'].max(), \n",
    "                                                 global_grid['lon'].size]))\n",
    "print('Latitude [min, max, size]: {}'.format([global_grid['lat'].min(), \n",
    "                                                 global_grid['lat'].max(), \n",
    "                                                 global_grid['lat'].size]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corresponding lon in global grid at index 50: -174.95000000000002\n",
      "Corresponding lon in global grid at index 1299: -50.05000000000001\n",
      "Number longitudes bounded: 1250\n"
     ]
    }
   ],
   "source": [
    "# Play with global longitude indexing to see if you can get a match\n",
    "iminlon=50 \n",
    "imaxlon=1299\n",
    "print('Corresponding lon in global grid at index {}: {}'.format(iminlon,global_grid['lon'][iminlon]))\n",
    "print('Corresponding lon in global grid at index {}: {}'.format(imaxlon,global_grid['lon'][imaxlon]))\n",
    "print('Number longitudes bounded: {}'.format(np.size(global_grid['lon'][iminlon:imaxlon+1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min lat regional: 15.050003051757812\n",
      "Max lat regional: 84.95001220703125\n",
      "Number latitudes: 700\n"
     ]
    }
   ],
   "source": [
    "# Show info about latitudes in the original regional file\n",
    "print('Min lat regional: {}'.format(ds_regional['lat'].values.min()))\n",
    "print('Max lat regional: {}'.format(ds_regional['lat'].values.max()))\n",
    "print('Number latitudes: {}'.format(np.size(ds_regional['lat'].values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corresponding lat in global grid at index 1051: 15.049999999999997\n",
      "Corresponding lat in global grid at index 1750: 84.94999999999997\n",
      "Number latitudes bounded: 700\n"
     ]
    }
   ],
   "source": [
    "# Play with global latitude indexing to see if you can get a match\n",
    "iminlat=1051\n",
    "imaxlat=1750 \n",
    "print('Corresponding lat in global grid at index {}: {}'.format(iminlat,global_grid['lat'][iminlat]))\n",
    "print('Corresponding lat in global grid at index {}: {}'.format(imaxlat,global_grid['lat'][imaxlat]))\n",
    "print('Number latitudes bounded: {}'.format(np.size(global_grid['lat'][iminlat:imaxlat+1])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define numpy arrays for the global grid using the data types in the regional file\n",
    "NOTE: The original file landmask does not have a time dimension but I am adding the time dimension for the global file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dimension ordering MUST be (time,lat,lon) for successful read in GEOS-Chem\n",
    "timearr = ds_regional['time']\n",
    "latarr = np.array(global_grid['lat'], dtype='single')\n",
    "lonarr = np.array(global_grid['lon'], dtype='single')\n",
    "dataarr = np.zeros([timearr.size, latarr.size, lonarr.size], dtype='single')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Dataset for each variable with global bounds and set attributes/encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 CO\n",
      "1 SOx\n",
      "2 NOx\n",
      "3 NH3\n",
      "4 VOC\n",
      "5 OC\n",
      "6 EC\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 1802, lon: 3600, time: 26)\n",
       "Coordinates:\n",
       "  * time     (time) float32 0.0 8760.0 17520.0 26280.0 35064.0 43824.0 ...\n",
       "  * lat      (lat) float32 -90.0 -89.95 -89.85 -89.75 -89.65 -89.55 -89.45 ...\n",
       "  * lon      (lon) float32 -179.95 -179.85 -179.75 -179.65 -179.55 -179.45 ...\n",
       "Data variables:\n",
       "    NH3      (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "varnames = ds_regional.data_vars.keys()\n",
    "dslist = []\n",
    "for i,v in enumerate(varnames):\n",
    "    print(i,v)\n",
    "    ds = xr.Dataset({v: (['time','lat','lon'], dataarr)},\n",
    "                    coords={'time':timearr,\n",
    "                            'lat':latarr,\n",
    "                            'lon':lonarr})\n",
    "    ds[v].data[:,iminlat:imaxlat+1,iminlon:imaxlon+1] = ds_regional[v].values[:,:,:]\n",
    "    ds[v].attrs = {\n",
    "        'long_name': v,\n",
    "        'units': 'kg/m2/s'}\n",
    "    ds['lon'].attrs = {\n",
    "        'standard_name': 'longitude',\n",
    "        'long_name': 'Longitude',\n",
    "        'units': 'degrees_east',\n",
    "        'axis': 'X'}\n",
    "    ds['lat'].attrs = {\n",
    "        'standard_name': 'latitude',\n",
    "        'long_name': 'Latitude',\n",
    "        'units': 'degrees_north',\n",
    "        'axis': 'Y'}\n",
    "    ds['time'].attrs = {\n",
    "        'standard_name': 'time',\n",
    "        'long_name':'Time',\n",
    "        'units': 'hours since 1989-01-01 00:00:00',\n",
    "        'calendar': 'gregorian',\n",
    "        'delta_t': '0001-00-00 00:00:00'}\n",
    "    dslist.append(ds)\n",
    "    \n",
    "dslist[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge all Datasets into one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 1802, lon: 3600, time: 26)\n",
       "Coordinates:\n",
       "  * time     (time) float32 0.0 8760.0 17520.0 26280.0 35064.0 43824.0 ...\n",
       "  * lat      (lat) float32 -90.0 -89.95 -89.85 -89.75 -89.65 -89.55 -89.45 ...\n",
       "  * lon      (lon) float32 -179.95 -179.85 -179.75 -179.65 -179.55 -179.45 ...\n",
       "Data variables:\n",
       "    CO       (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    SOx      (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    NOx      (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    NH3      (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    VOC      (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    OC       (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    EC       (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_global = xr.merge(dslist)\n",
    "ds_global"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot one variable for one time in global Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TFrjppkmv8ZrDQmYxn6tcCinvTHJoooYk0nHSSQA/XtwIQgv1BPBjxYbCoLsaGqjgSmjU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\nQXas3A4+H4ggPj/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WyvbJbsKYMVpZenBiR/MFVxgop4h0mXnLgLWZzGMm824bJd7LtLSZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ba443cd5668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Must first subset the time dimension and pick a variable to plot 2D not 3D\n",
    "# Also might need to adjust min/max on plot to see actual data\n",
    "co_global_t0 = ds_global['CO'].isel(time=0)\n",
    "vmin = 0\n",
    "vmax = co_global_t0.mean() + co_global_t0.var()\n",
    "\n",
    "# Plot\n",
    "ax = plt.axes(projection=crs.PlateCarree())\n",
    "ax.coastlines()  # overlay continents\n",
    "ax.gridlines(draw_labels=True, linestyle='--')\n",
    "im = ax.pcolormesh(ds_global['lon'], ds_global['lat'], co_global_t0, vmin=vmin, vmax=vmax)\n",
    "ax.set_global()\n",
    "plt.colorbar(im, orientation='horizontal', shrink=0.8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save global Dataset  to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Remove _FillValue attribute which is automatically placed into the file by xarray\n",
    "# Doing this using the method below requires xarray v0.10.1 or higher due to this bug:\n",
    "# https://github.com/pydata/xarray/issues/1865\n",
    "ds_global.to_netcdf('/n/home08/elundgren/APEI.0.1x0.1.global.nc',\n",
    "                    'w', format='NETCDF4',\n",
    "                    encoding={'lat': {'_FillValue': None}, \n",
    "                              'lon': {'_FillValue': None},\n",
    "                              'time': {'_FillValue': None}})"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}