figure_1_land_use_topog_precip_winds.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-15T23:23:39.470057Z",
     "start_time": "2018-08-15T23:23:38.456811Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import netCDF4\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as mp\n",
    "import datetime\n",
    "import scipy\n",
    "import scipy.ndimage\n",
    "import matplotlib.cm as mcm\n",
    "\n",
    "mp.rcParams.update({'mathtext.default': 'regular'})\n",
    "\n",
    "import matplotlib.patches\n",
    "import matplotlib.ticker\n",
    "\n",
    "import mpl_toolkits.axes_grid1\n",
    "import cartopy\n",
    "\n",
    "DEGREE_SIGN = u'\\u00B0'\n",
    "\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-15T23:23:40.627425Z",
     "start_time": "2018-08-15T23:23:40.621745Z"
    }
   },
   "outputs": [],
   "source": [
    "run_info = '50km_idealized_noahmp_ens_mean'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-15T23:23:41.054686Z",
     "start_time": "2018-08-15T23:23:40.635669Z"
    }
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: b'/Users/baird/Dropbox/_data_WRF/geo_em.d01_amazon_50km.nc'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-20aa009a7922>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgeo_em_ncfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnetCDF4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/baird/Dropbox/_data_WRF/geo_em.d01_amazon_50km.nc'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mlu_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeo_em_ncfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'LU_INDEX'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mXLONG_M\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeo_em_ncfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'XLONG_M'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mXLAT_M\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeo_em_ncfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'XLAT_M'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mHGT_M\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeo_em_ncfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'HGT_M'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: b'/Users/baird/Dropbox/_data_WRF/geo_em.d01_amazon_50km.nc'"
     ]
    }
   ],
   "source": [
    "geo_em_ncfile = netCDF4.Dataset('/Users/baird/Dropbox/_data_WRF/geo_em.d01_amazon_50km.nc')\n",
    "lu_index = geo_em_ncfile.variables['LU_INDEX'][0,:,:]\n",
    "XLONG_M = geo_em_ncfile.variables['XLONG_M'][0,:,:]\n",
    "XLAT_M = geo_em_ncfile.variables['XLAT_M'][0,:,:]\n",
    "HGT_M = geo_em_ncfile.variables['HGT_M'][0,:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LAND USE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-15T23:23:44.081198Z",
     "start_time": "2018-08-15T23:23:44.068641Z"
    }
   },
   "outputs": [],
   "source": [
    "modis_type_dict = {}\n",
    "modis_type_dict['0'] = 'water'\n",
    "modis_type_dict['1'] = 'evergreen needleleaf forest'\n",
    "modis_type_dict['2'] = 'evergreen broadleaf forest'\n",
    "modis_type_dict['3'] = 'deciduous needleleaf forest'\n",
    "modis_type_dict['4'] = 'deciduous broadleaf forest'\n",
    "modis_type_dict['5'] = 'mixed forest'\n",
    "modis_type_dict['6'] = 'closed shrublands'\n",
    "modis_type_dict['7'] = 'open shrublands'\n",
    "modis_type_dict['8'] = 'woody savannas'\n",
    "modis_type_dict['9'] = 'savannas'\n",
    "modis_type_dict['10'] = 'grasslands'\n",
    "modis_type_dict['11'] = 'permanent wetlands'\n",
    "modis_type_dict['12'] = 'croplands'\n",
    "modis_type_dict['13'] = 'urban and built-up'\n",
    "modis_type_dict['14'] = 'cropland/natural veg. mosaic'\n",
    "modis_type_dict['15'] = 'snow and ice'\n",
    "modis_type_dict['16'] = 'barren or sparsely vegetated'\n",
    "modis_type_dict['254'] = 'unclassified'\n",
    "modis_type_dict['255'] = 'fill Value'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-15T23:23:44.413759Z",
     "start_time": "2018-08-15T23:23:44.409512Z"
    }
   },
   "outputs": [],
   "source": [
    "type_numbers = numpy.arange(0,17,dtype=numpy.int)\n",
    "type_name = [modis_type_dict[str(i)] for i in type_numbers]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T16:57:25.130316Z",
     "start_time": "2018-08-02T16:57:25.115693Z"
    }
   },
   "outputs": [],
   "source": [
    "human_cmap = mcm.YlOrRd_r #Purples\n",
    "water_cmap = mcm.Blues\n",
    "veg_cmap = mcm.Greens_r\n",
    "shrub_cmap = mcm.pink #YlOrBr\n",
    "\n",
    "svals = numpy.linspace(0.2,0.85,5)\n",
    "vvals = numpy.linspace(0.2,0.85,5)\n",
    "hvals = [0.25,0.5,0.75]\n",
    "\n",
    "colors_list = [veg_cmap(vvals[0]),veg_cmap(vvals[1]),veg_cmap(vvals[2]),veg_cmap(vvals[3]),veg_cmap(vvals[4]),\\\n",
    "               shrub_cmap(svals[0]),shrub_cmap(svals[1]),shrub_cmap(svals[2]),shrub_cmap(svals[3]),shrub_cmap(svals[4]),\\\n",
    "               water_cmap(0.5),\\\n",
    "               human_cmap(hvals[0]),human_cmap(hvals[1]),human_cmap(hvals[2]),\\\n",
    "               '0.7','0.9',water_cmap(0.75)]\n",
    "\n",
    "categorical_cmap = matplotlib.colors.ListedColormap(colors_list, name='None')\n",
    "\n",
    "norm = matplotlib.colors.BoundaryNorm(numpy.arange(type_numbers.min()-0.5,type_numbers.max()+0.5+1,1),\\\n",
    "                                                   categorical_cmap.N)\n",
    "n_categories = len(colors_list)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# colors_list = ['darkgreen','forestgreen','darkolivegreen','olivedrab','yellowgreen',\\\n",
    "#               'saddlebrown','sienna','peru','burlywood','wheat',\n",
    "#               'darkturquoise','gold',\\\n",
    "#               'red','firebrick',\\\n",
    "#               'lightcyan','None']\n",
    "# colors_list = ['darkgreen','forestgreen','darkolivegreen','olivedrab','yellowgreen',\\\n",
    "#               'darkred','sienna','peru','burlywood','wheat',\n",
    "#               'gold','orange',\\\n",
    "#               'red','palegreen',\\\n",
    "#               'lightcyan','None']\n",
    "colors_list = ['darkgreen','forestgreen','darkolivegreen','olivedrab','yellowgreen',\\\n",
    "              'darkred','sienna','peru','burlywood','wheat',\n",
    "              'gold','orange',\\\n",
    "              'red','palegreen',\\\n",
    "              'lightcyan','0.9']\n",
    "categorical_cmap = matplotlib.colors.ListedColormap(colors_list, name='baird')\n",
    "n_categories = len(colors_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T16:57:27.234697Z",
     "start_time": "2018-08-02T16:57:27.194686Z"
    }
   },
   "outputs": [],
   "source": [
    "season='FMAASO'\n",
    "\n",
    "data_dir = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/METGRID_FILES_RAW_OUTPUT/'+season+'/'\n",
    "filename00 = data_dir+'met_em.d01.1979-01-01_00:00:00.nc'\n",
    "filename06 = data_dir+'met_em.d01.1979-01-01_06:00:00.nc'\n",
    "filename12 = data_dir+'met_em.d01.1979-01-01_12:00:00.nc'\n",
    "filename18 = data_dir+'met_em.d01.1979-01-01_18:00:00.nc'\n",
    "ncfile00 = netCDF4.Dataset(filename00, 'r', 'NetCDF4')\n",
    "ncfile06 = netCDF4.Dataset(filename06, 'r', 'NetCDF4')\n",
    "ncfile12 = netCDF4.Dataset(filename12, 'r', 'NetCDF4')\n",
    "ncfile18 = netCDF4.Dataset(filename18, 'r', 'NetCDF4')\n",
    "ncfile_list = [ncfile00,ncfile06,ncfile12,ncfile18]\n",
    "\n",
    "XLAT_M = ncfile00.variables['XLAT_M'][0,:,:]\n",
    "XLONG_M = ncfile00.variables['XLONG_M'][0,:,:]\n",
    "XLAT_U = ncfile00.variables['XLAT_U'][0,:,:]\n",
    "XLONG_U = ncfile00.variables['XLONG_U'][0,:,:]\n",
    "XLAT_V = ncfile00.variables['XLAT_V'][0,:,:]\n",
    "XLONG_V = ncfile00.variables['XLONG_V'][0,:,:]\n",
    "LANDMASK = ncfile00.variables['LANDMASK'][0,:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMPORT SKINTEMP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T16:57:28.696155Z",
     "start_time": "2018-08-02T16:57:28.657479Z"
    }
   },
   "outputs": [],
   "source": [
    "skintemp_list = [file.variables['SKINTEMP'][0,:,:]-273.15 for file in ncfile_list]\n",
    "skintemp_mean = numpy.mean(numpy.array(skintemp_list), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMPORT UU,VV FOR THIS SEASON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T16:57:29.694118Z",
     "start_time": "2018-08-02T16:57:29.657800Z"
    }
   },
   "outputs": [],
   "source": [
    "uu850_list = [file.variables['UU'][0,3,:,:] for file in ncfile_list]\n",
    "uu850_mean_FMAASO = numpy.mean(numpy.array(uu850_list), axis=0)\n",
    "\n",
    "vv850_list = [file.variables['VV'][0,3,:,:] for file in ncfile_list]\n",
    "vv850_mean_FMAASO = numpy.mean(numpy.array(vv850_list), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NDJ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T16:57:30.624652Z",
     "start_time": "2018-08-02T16:57:30.603910Z"
    }
   },
   "outputs": [],
   "source": [
    "season='NDJ'\n",
    "\n",
    "data_dir = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/METGRID_FILES_RAW_OUTPUT/'+season+'/'\n",
    "filename00 = data_dir+'met_em.d01.1979-01-01_00:00:00.nc'\n",
    "filename06 = data_dir+'met_em.d01.1979-01-01_06:00:00.nc'\n",
    "filename12 = data_dir+'met_em.d01.1979-01-01_12:00:00.nc'\n",
    "filename18 = data_dir+'met_em.d01.1979-01-01_18:00:00.nc'\n",
    "ncfile00 = netCDF4.Dataset(filename00, 'r', 'NetCDF4')\n",
    "ncfile06 = netCDF4.Dataset(filename06, 'r', 'NetCDF4')\n",
    "ncfile12 = netCDF4.Dataset(filename12, 'r', 'NetCDF4')\n",
    "ncfile18 = netCDF4.Dataset(filename18, 'r', 'NetCDF4')\n",
    "ncfile_list = [ncfile00,ncfile06,ncfile12,ncfile18]\n",
    "\n",
    "# XLAT_M = ncfile00.variables['XLAT_M'][0,:,:]\n",
    "# XLONG_M = ncfile00.variables['XLONG_M'][0,:,:]\n",
    "# XLAT_U = ncfile00.variables['XLAT_U'][0,:,:]\n",
    "# XLONG_U = ncfile00.variables['XLONG_U'][0,:,:]\n",
    "# XLAT_V = ncfile00.variables['XLAT_V'][0,:,:]\n",
    "# XLONG_V = ncfile00.variables['XLONG_V'][0,:,:]\n",
    "# LANDMASK = ncfile00.variables['LANDMASK'][0,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T16:57:31.234271Z",
     "start_time": "2018-08-02T16:57:31.201431Z"
    }
   },
   "outputs": [],
   "source": [
    "uu850_list = [file.variables['UU'][0,3,:,:] for file in ncfile_list]\n",
    "uu850_mean_NDJ = numpy.mean(numpy.array(uu850_list), axis=0)\n",
    "\n",
    "vv850_list = [file.variables['VV'][0,3,:,:] for file in ncfile_list]\n",
    "vv850_mean_NDJ = numpy.mean(numpy.array(vv850_list), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MJJ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T16:57:31.889595Z",
     "start_time": "2018-08-02T16:57:31.870638Z"
    }
   },
   "outputs": [],
   "source": [
    "season='MJJ'\n",
    "\n",
    "data_dir = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/METGRID_FILES_RAW_OUTPUT/'+season+'/'\n",
    "filename00 = data_dir+'met_em.d01.1979-01-01_00:00:00.nc'\n",
    "filename06 = data_dir+'met_em.d01.1979-01-01_06:00:00.nc'\n",
    "filename12 = data_dir+'met_em.d01.1979-01-01_12:00:00.nc'\n",
    "filename18 = data_dir+'met_em.d01.1979-01-01_18:00:00.nc'\n",
    "ncfile00 = netCDF4.Dataset(filename00, 'r', 'NetCDF4')\n",
    "ncfile06 = netCDF4.Dataset(filename06, 'r', 'NetCDF4')\n",
    "ncfile12 = netCDF4.Dataset(filename12, 'r', 'NetCDF4')\n",
    "ncfile18 = netCDF4.Dataset(filename18, 'r', 'NetCDF4')\n",
    "ncfile_list = [ncfile00,ncfile06,ncfile12,ncfile18]\n",
    "\n",
    "# XLAT_M = ncfile00.variables['XLAT_M'][0,:,:]\n",
    "# XLONG_M = ncfile00.variables['XLONG_M'][0,:,:]\n",
    "# XLAT_U = ncfile00.variables['XLAT_U'][0,:,:]\n",
    "# XLONG_U = ncfile00.variables['XLONG_U'][0,:,:]\n",
    "# XLAT_V = ncfile00.variables['XLAT_V'][0,:,:]\n",
    "# XLONG_V = ncfile00.variables['XLONG_V'][0,:,:]\n",
    "# LANDMASK = ncfile00.variables['LANDMASK'][0,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T16:57:32.421407Z",
     "start_time": "2018-08-02T16:57:32.385918Z"
    }
   },
   "outputs": [],
   "source": [
    "uu850_list = [file.variables['UU'][0,3,:,:] for file in ncfile_list]\n",
    "uu850_mean_MJJ = numpy.mean(numpy.array(uu850_list), axis=0)\n",
    "\n",
    "vv850_list = [file.variables['VV'][0,3,:,:] for file in ncfile_list]\n",
    "vv850_mean_MJJ = numpy.mean(numpy.array(vv850_list), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OBS PRECIP DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T16:57:41.292325Z",
     "start_time": "2018-08-02T16:57:41.039738Z"
    }
   },
   "outputs": [],
   "source": [
    "precip_ncfile_name = '/Users/baird/Dropbox/_data_observations/GPCP/precip.mon.mean.nc'\n",
    "ncfile_gpcp = netCDF4.Dataset(precip_ncfile_name)\n",
    "data_gpcp = ncfile_gpcp.variables['precip'][:]\n",
    "time_data_gpcp = ncfile_gpcp.variables['time'][:]\n",
    "time_var_gpcp = ncfile_gpcp.variables['time']\n",
    "lat_gpcp = ncfile_gpcp.variables['lat'][:]\n",
    "lon_gpcp = ncfile_gpcp.variables['lon'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T16:57:41.501211Z",
     "start_time": "2018-08-02T16:57:41.490638Z"
    }
   },
   "outputs": [],
   "source": [
    "time_gpcp_datetime = netCDF4.num2date(time_data_gpcp, time_var_gpcp.units, calendar='standard')\n",
    "FMAASO_indices = [t.month in [2,3,4,8,9,10] for t in time_gpcp_datetime]\n",
    "NDJ_indices = [t.month in [11,12,1] for t in time_gpcp_datetime]\n",
    "MJJ_indices = [t.month in [5,6,7] for t in time_gpcp_datetime]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1979-01-01 00:00:00 2017-12-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(time_gpcp_datetime[0], time_gpcp_datetime[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpcp_FMAASO_mean = data_gpcp[FMAASO_indices,:,:].mean(axis=0)\n",
    "gpcp_NDJ_mean = data_gpcp[NDJ_indices,:,:].mean(axis=0)\n",
    "gpcp_MJJ_mean = data_gpcp[MJJ_indices,:,:].mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WRF PRECIP DATA AND WINDS DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "xlat_ncfile_name = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/FMAASO/1000/concat_nc_files/wrfout_d01_1979-01-01_00:00:00_XLAT.nc'\n",
    "xlong_ncfile_name = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/FMAASO/1000/concat_nc_files/wrfout_d01_1979-01-01_00:00:00_XLONG.nc'\n",
    "landmask_ncfile_name = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/FMAASO/1000/concat_nc_files/wrfout_d01_1979-01-01_00:00:00_LANDMASK.nc'\n",
    "times_ncfile_name = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/FMAASO/1000/concat_nc_files/wrfout_d01_1979-01-01_00:00:00_1979-01-11_00:00:00_Times_ens_mean.nc'\n",
    "\n",
    "data_dir = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/FMAASO/400/concat_nc_files/'\n",
    "filename_prefix = 'wrfout_d01_1979-01-01_00:00:00_1979-01-11_00:00:00_'\n",
    "filename_suffix = '_ens_mean.nc'\n",
    "\n",
    "time_ncfile = netCDF4.Dataset(times_ncfile_name)\n",
    "xlat_ncfile = netCDF4.Dataset(xlat_ncfile_name)\n",
    "xlong_ncfile = netCDF4.Dataset(xlong_ncfile_name)\n",
    "landmask_ncfile = netCDF4.Dataset(landmask_ncfile_name)\n",
    "\n",
    "rainc_ncfile = netCDF4.Dataset(data_dir + filename_prefix + 'RAINC' + filename_suffix)\n",
    "rainnc_ncfile = netCDF4.Dataset(data_dir + filename_prefix + 'RAINNC' + filename_suffix)\n",
    "u_ncfile = netCDF4.Dataset(data_dir + filename_prefix + 'U' + '_ens_mean_vinterp.nc')\n",
    "v_ncfile = netCDF4.Dataset(data_dir + filename_prefix + 'V' + '_ens_mean_vinterp.nc')\n",
    "\n",
    "time_var = time_ncfile.variables['Times']\n",
    "time = time_ncfile.variables['Times'][:]\n",
    "xlat = xlat_ncfile.variables['XLAT'][0,:,:]\n",
    "xlong = xlong_ncfile.variables['XLONG'][0,:,:]\n",
    "landmask = landmask_ncfile.variables['LANDMASK'][0,:,:]\n",
    "\n",
    "rainc_400_FMAASO = rainc_ncfile.variables['RAINC'][:]\n",
    "rainnc_400_FMAASO = rainnc_ncfile.variables['RAINNC'][:]\n",
    "\n",
    "u_400_FMAASO = u_ncfile.variables['U'][:]\n",
    "v_400_FMAASO = v_ncfile.variables['V'][:]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "u_400_FMAASO_850mb = u_400_FMAASO[:,3,:,:]\n",
    "v_400_FMAASO_850mb = v_400_FMAASO[:,3,:,:]\n",
    "\n",
    "skip_time=24\n",
    "raintot_400_FMAASO = rainc_400_FMAASO+rainnc_400_FMAASO\n",
    "raintot_400_FMAASO = raintot_400_FMAASO[skip_time:,:,:]\n",
    "raintot_400_FMAASO_diff = numpy.zeros((raintot_400_FMAASO.shape))*numpy.nan\n",
    "raintot_400_FMAASO_diff[:-1,:,:] = numpy.diff(raintot_400_FMAASO, axis=0) * 24.\n",
    "\n",
    "raintot_400_FMAASO_clim = numpy.nanmean(raintot_400_FMAASO_diff, axis=0)\n",
    "\n",
    "u_400_FMAASO_850mb_clim = numpy.nanmean(u_400_FMAASO_850mb[skip_time:,:,:], axis=0)\n",
    "v_400_FMAASO_850mb_clim = numpy.nanmean(v_400_FMAASO_850mb[skip_time:,:,:], axis=0)\n",
    "\n",
    "xgrid,ygrid=numpy.meshgrid(lon_gpcp,lat_gpcp)\n",
    "\n",
    "raintot_400_FMAASO_clim_interp = scipy.interpolate.griddata(points=(XLONG_M.ravel()+180., XLAT_M.ravel()), \\\n",
    "                                                     values=raintot_400_FMAASO_clim.ravel(), \\\n",
    "                                                     xi=(xgrid,ygrid), method='cubic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MJJ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "xlat_ncfile_name = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/MJJ/1000/concat_nc_files/wrfout_d01_1979-01-01_00:00:00_XLAT.nc'\n",
    "xlong_ncfile_name = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/MJJ/1000/concat_nc_files/wrfout_d01_1979-01-01_00:00:00_XLONG.nc'\n",
    "landmask_ncfile_name = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/MJJ/1000/concat_nc_files/wrfout_d01_1979-01-01_00:00:00_LANDMASK.nc'\n",
    "times_ncfile_name = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/MJJ/1000/concat_nc_files/wrfout_d01_1979-01-01_00:00:00_1979-01-11_00:00:00_Times_ens_mean.nc'\n",
    "\n",
    "data_dir = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/MJJ/400/concat_nc_files/'\n",
    "filename_prefix = 'wrfout_d01_1979-01-01_00:00:00_1979-01-11_00:00:00_'\n",
    "filename_suffix = '_ens_mean.nc'\n",
    "\n",
    "#time_ncfile = netCDF4.Dataset(times_ncfile_name)\n",
    "#xlat_ncfile = netCDF4.Dataset(xlat_ncfile_name)\n",
    "#xlong_ncfile = netCDF4.Dataset(xlong_ncfile_name)\n",
    "#landmask_ncfile = netCDF4.Dataset(landmask_ncfile_name)\n",
    "\n",
    "rainc_ncfile = netCDF4.Dataset(data_dir + filename_prefix + 'RAINC' + filename_suffix)\n",
    "rainnc_ncfile = netCDF4.Dataset(data_dir + filename_prefix + 'RAINNC' + filename_suffix)\n",
    "#u_ncfile = netCDF4.Dataset(data_dir + filename_prefix + 'U' + '_ens_mean_vinterp.nc')\n",
    "#v_ncfile = netCDF4.Dataset(data_dir + filename_prefix + 'V' + '_ens_mean_vinterp.nc')\n",
    "\n",
    "time_var = time_ncfile.variables['Times']\n",
    "time = time_ncfile.variables['Times'][:]\n",
    "xlat = xlat_ncfile.variables['XLAT'][0,:,:]\n",
    "xlong = xlong_ncfile.variables['XLONG'][0,:,:]\n",
    "landmask = landmask_ncfile.variables['LANDMASK'][0,:,:]\n",
    "\n",
    "rainc_400_MJJ = rainc_ncfile.variables['RAINC'][:]\n",
    "rainnc_400_MJJ = rainnc_ncfile.variables['RAINNC'][:]\n",
    "\n",
    "#u_400_MJJ = u_ncfile.variables['U'][:]\n",
    "#v_400_MJJ = v_ncfile.variables['V'][:]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#u_400_MJJ_850mb = u_400_MJJ[:,3,:,:]\n",
    "#v_400_MJJ_850mb = v_400_MJJ[:,3,:,:]\n",
    "\n",
    "skip_time=24\n",
    "raintot_400_MJJ = rainc_400_MJJ+rainnc_400_MJJ\n",
    "raintot_400_MJJ = raintot_400_MJJ[skip_time:,:,:]\n",
    "raintot_400_MJJ_diff = numpy.zeros((raintot_400_MJJ.shape))*numpy.nan\n",
    "raintot_400_MJJ_diff[:-1,:,:] = numpy.diff(raintot_400_MJJ, axis=0) * 24.\n",
    "\n",
    "raintot_400_MJJ_clim = numpy.nanmean(raintot_400_MJJ_diff, axis=0)\n",
    "\n",
    "#u_400_MJJ_850mb_clim = numpy.nanmean(u_400_MJJ_850mb[skip_time:,:,:], axis=0)\n",
    "#v_400_MJJ_850mb_clim = numpy.nanmean(v_400_MJJ_850mb[skip_time:,:,:], axis=0)\n",
    "\n",
    "xgrid,ygrid=numpy.meshgrid(lon_gpcp,lat_gpcp)\n",
    "\n",
    "raintot_400_MJJ_clim_interp = scipy.interpolate.griddata(points=(XLONG_M.ravel()+180., XLAT_M.ravel()), \\\n",
    "                                                     values=raintot_400_MJJ_clim.ravel(), \\\n",
    "                                                     xi=(xgrid,ygrid), method='cubic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NDJ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "xlat_ncfile_name = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/NDJ/1000/concat_nc_files/wrfout_d01_1979-01-01_00:00:00_XLAT.nc'\n",
    "xlong_ncfile_name = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/NDJ/1000/concat_nc_files/wrfout_d01_1979-01-01_00:00:00_XLONG.nc'\n",
    "landmask_ncfile_name = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/NDJ/1000/concat_nc_files/wrfout_d01_1979-01-01_00:00:00_LANDMASK.nc'\n",
    "times_ncfile_name = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/NDJ/1000/concat_nc_files/wrfout_d01_1979-01-01_00:00:00_1979-01-11_00:00:00_Times_ens_mean.nc'\n",
    "\n",
    "data_dir = '/Users/baird/Dropbox/_data_WRF/CHEYENNE/NDJ/400/concat_nc_files/'\n",
    "filename_prefix = 'wrfout_d01_1979-01-01_00:00:00_1979-01-11_00:00:00_'\n",
    "filename_suffix = '_ens_mean.nc'\n",
    "\n",
    "#time_ncfile = netCDF4.Dataset(times_ncfile_name)\n",
    "#xlat_ncfile = netCDF4.Dataset(xlat_ncfile_name)\n",
    "#xlong_ncfile = netCDF4.Dataset(xlong_ncfile_name)\n",
    "#landmask_ncfile = netCDF4.Dataset(landmask_ncfile_name)\n",
    "\n",
    "rainc_ncfile = netCDF4.Dataset(data_dir + filename_prefix + 'RAINC' + filename_suffix)\n",
    "rainnc_ncfile = netCDF4.Dataset(data_dir + filename_prefix + 'RAINNC' + filename_suffix)\n",
    "#u_ncfile = netCDF4.Dataset(data_dir + filename_prefix + 'U' + '_ens_mean_vinterp.nc')\n",
    "#v_ncfile = netCDF4.Dataset(data_dir + filename_prefix + 'V' + '_ens_mean_vinterp.nc')\n",
    "\n",
    "time_var = time_ncfile.variables['Times']\n",
    "time = time_ncfile.variables['Times'][:]\n",
    "xlat = xlat_ncfile.variables['XLAT'][0,:,:]\n",
    "xlong = xlong_ncfile.variables['XLONG'][0,:,:]\n",
    "landmask = landmask_ncfile.variables['LANDMASK'][0,:,:]\n",
    "\n",
    "rainc_400_NDJ = rainc_ncfile.variables['RAINC'][:]\n",
    "rainnc_400_NDJ = rainnc_ncfile.variables['RAINNC'][:]\n",
    "\n",
    "#u_400_NDJ = u_ncfile.variables['U'][:]\n",
    "#v_400_NDJ = v_ncfile.variables['V'][:]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#u_400_NDJ_850mb = u_400_NDJ[:,3,:,:]\n",
    "#v_400_NDJ_850mb = v_400_NDJ[:,3,:,:]\n",
    "\n",
    "skip_time=24\n",
    "raintot_400_NDJ = rainc_400_NDJ+rainnc_400_NDJ\n",
    "raintot_400_NDJ = raintot_400_NDJ[skip_time:,:,:]\n",
    "raintot_400_NDJ_diff = numpy.zeros((raintot_400_NDJ.shape))*numpy.nan\n",
    "raintot_400_NDJ_diff[:-1,:,:] = numpy.diff(raintot_400_NDJ, axis=0) * 24.\n",
    "\n",
    "raintot_400_NDJ_clim = numpy.nanmean(raintot_400_NDJ_diff, axis=0)\n",
    "\n",
    "#u_400_NDJ_850mb_clim = numpy.nanmean(u_400_NDJ_850mb[skip_time:,:,:], axis=0)\n",
    "#v_400_NDJ_850mb_clim = numpy.nanmean(v_400_NDJ_850mb[skip_time:,:,:], axis=0)\n",
    "\n",
    "xgrid,ygrid=numpy.meshgrid(lon_gpcp,lat_gpcp)\n",
    "\n",
    "raintot_400_NDJ_clim_interp = scipy.interpolate.griddata(points=(XLONG_M.ravel()+180., XLAT_M.ravel()), \\\n",
    "                                                     values=raintot_400_NDJ_clim.ravel(), \\\n",
    "                                                     xi=(xgrid,ygrid), method='cubic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make color map\n",
    "minval=0.15 # for inferno:  0.18\n",
    "maxval=0.85 # for inferno: 1.0\n",
    "n=256\n",
    "full_cmap = mp.get_cmap('magma')\n",
    "cmap_orog = matplotlib.colors.LinearSegmentedColormap.from_list('trunc({n},{a:.2f},{b:.2f})'.format(n=full_cmap.name, a=minval, b=maxval), full_cmap(numpy.linspace(minval, maxval, n)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make color map\n",
    "minval=0. # for inferno:  0.18\n",
    "maxval=0.9 # for inferno: 1.0\n",
    "n=256\n",
    "full_cmap = mp.get_cmap('gist_earth_r')\n",
    "cmap_precip = matplotlib.colors.LinearSegmentedColormap.from_list('trunc({n},{a:.2f},{b:.2f})'.format(n=full_cmap.name, a=minval, b=maxval), full_cmap(numpy.linspace(minval, maxval, n)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMPORT SKINTEMP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "skintemp_list = [file.variables['SKINTEMP'][0,:,:]-273.15 for file in ncfile_list]\n",
    "skintemp_mean = numpy.mean(numpy.array(skintemp_list), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x18151b8c18>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x184e749ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c=mp.contourf(HGT_M); mp.colorbar(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106ba6128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(7.5,10))\n",
    "\n",
    "gs1 = matplotlib.gridspec.GridSpec(1,7)\n",
    "ax1 = mp.subplot(gs1[0:5], projection=cartopy.crs.PlateCarree())\n",
    "gs1.update(bottom=0.63)\n",
    "\n",
    "levels = numpy.arange(-20,21,5)\n",
    "ax1.gridlines(ylocs=[0], color='0.25')\n",
    "ax1.coastlines(resolution='110m')\n",
    "ax1.set_extent([XLONG_M.min()+23., XLONG_M.max()-2, XLAT_M.min(), XLAT_M.max()], crs=cartopy.crs.PlateCarree())\n",
    "\n",
    "land_use_map = ax1.pcolormesh(XLONG_M,XLAT_M,lu_index,cmap=categorical_cmap,vmin=1,vmax=17)\n",
    "levels_orog=numpy.arange(1500,4501,500) # max is about 4800\n",
    "contour_orog = ax1.contour(XLONG_M,XLAT_M,HGT_M,\\\n",
    "                           levels=levels_orog,\\\n",
    "                           cmap=cmap_orog,linewidths=1,extend='both')#,alpha=0.75)\n",
    "\n",
    "bbox_ax = ax1.get_position()\n",
    "\n",
    "ax1.text(s='(a) land use categories and elevation', x=0, y=1.02, \\\n",
    "         fontsize=fontsize, ha='left', va='bottom', transform=ax1.transAxes)\n",
    "\n",
    "every_nth_lat=20\n",
    "every_nth_lon=20\n",
    "quiver_scale = 0.75 # 0.05 for 850-600, 0.1 for below 850\n",
    "quiver_shaft_width = 1.#.75\n",
    "quiver_key_scale = 5\n",
    "quiver_color = 'firebrick'\n",
    "quiver_head_length = 3\n",
    "quiver_head_axis_length = quiver_head_length\n",
    "\n",
    "#line_colors = ['firebrick','orange']\n",
    "line_colors = ['aquamarine','aqua']\n",
    "\n",
    "gs2 = matplotlib.gridspec.GridSpec(3,9)\n",
    "gs2.update(top=0.6)\n",
    "\n",
    "levels=numpy.arange(0,11,1)\n",
    "\n",
    "ax2 = mp.subplot(gs2[0,0:4], projection=cartopy.crs.PlateCarree())\n",
    "ax2.gridlines(ylocs=[0], color='0.25')\n",
    "ax2.coastlines(resolution='110m', lw=1)\n",
    "ax2.set_extent([XLONG_M.min(), XLONG_M.max(), XLAT_M.min(), XLAT_M.max()], crs=cartopy.crs.PlateCarree())\n",
    "contour_plot1 = ax2.contourf(lon_gpcp, lat_gpcp, gpcp_FMAASO_mean, levels=levels, cmap=cmap_precip, extend='max')\n",
    "contour_lines1 = ax2.contour(lon_gpcp, lat_gpcp, gpcp_FMAASO_mean, levels=[4,8], colors=line_colors, linewidths=[1])\n",
    "ax2.text(s='(b) obs. equinox', x=0, y=1.02, transform=ax2.transAxes, ha='left', va='bottom', fontsize=fontsize)\n",
    "quiver_plot1 = ax2.quiver(XLONG_M[::every_nth_lat,::every_nth_lon], \\\n",
    "                        XLAT_M[::every_nth_lat,::every_nth_lon], \\\n",
    "                        uu850_mean_FMAASO[::every_nth_lat,::every_nth_lon], \\\n",
    "                        vv850_mean_FMAASO[::every_nth_lat,::every_nth_lon], \\\n",
    "                        units='xy', scale=quiver_scale, width=quiver_shaft_width, \\\n",
    "                        color=quiver_color, headlength=quiver_head_length, \\\n",
    "                        headaxislength=quiver_head_axis_length, \\\n",
    "                        zorder=2)    \n",
    "\n",
    "ax4 = mp.subplot(gs2[1,0:4], projection=cartopy.crs.PlateCarree())\n",
    "ax4.gridlines(ylocs=[0], color='0.25')\n",
    "ax4.coastlines(resolution='110m')\n",
    "ax4.set_extent([XLONG_M.min(), XLONG_M.max(), XLAT_M.min(), XLAT_M.max()], crs=cartopy.crs.PlateCarree())\n",
    "contour_plot2 = ax4.contourf(lon_gpcp, lat_gpcp, gpcp_MJJ_mean, levels=levels, cmap=cmap_precip, extend='max')\n",
    "contour_lines2 = ax4.contour(lon_gpcp, lat_gpcp, gpcp_MJJ_mean, levels=[4,8], colors=line_colors, linewidths=[1])\n",
    "ax4.text(s='(c) obs. summer solstice', x=0, y=1.02, transform=ax4.transAxes, ha='left', va='bottom', fontsize=fontsize)\n",
    "quiver_plot2 = ax4.quiver(XLONG_M[::every_nth_lat,::every_nth_lon], \\\n",
    "                        XLAT_M[::every_nth_lat,::every_nth_lon], \\\n",
    "                        uu850_mean_MJJ[::every_nth_lat,::every_nth_lon], \\\n",
    "                        vv850_mean_MJJ[::every_nth_lat,::every_nth_lon], \\\n",
    "                        units='xy', scale=quiver_scale, width=quiver_shaft_width, \\\n",
    "                        color=quiver_color, headlength=quiver_head_length, \\\n",
    "                        headaxislength=quiver_head_axis_length, \\\n",
    "                        zorder=2) \n",
    "\n",
    "ax6 = mp.subplot(gs2[2,0:4], projection=cartopy.crs.PlateCarree())\n",
    "ax6.gridlines(ylocs=[0], color='0.25')\n",
    "ax6.coastlines(resolution='110m')\n",
    "ax6.set_extent([XLONG_M.min(), XLONG_M.max(), XLAT_M.min(), XLAT_M.max()], crs=cartopy.crs.PlateCarree())\n",
    "contour_plot3 = ax6.contourf(lon_gpcp, lat_gpcp, gpcp_NDJ_mean, levels=levels, cmap=cmap_precip, extend='max')\n",
    "contour_lines3 = ax6.contour(lon_gpcp, lat_gpcp, gpcp_NDJ_mean, levels=[4,8], colors=line_colors, linewidths=[1])\n",
    "ax6.text(s='(d) obs. winter solstice', x=0, y=1.02, transform=ax6.transAxes, ha='left', va='bottom', fontsize=fontsize)\n",
    "quiver_plot3 = ax6.quiver(XLONG_M[::every_nth_lat,::every_nth_lon], \\\n",
    "                        XLAT_M[::every_nth_lat,::every_nth_lon], \\\n",
    "                        uu850_mean_NDJ[::every_nth_lat,::every_nth_lon], \\\n",
    "                        vv850_mean_NDJ[::every_nth_lat,::every_nth_lon], \\\n",
    "                        units='xy', scale=quiver_scale, width=quiver_shaft_width, \\\n",
    "                        color=quiver_color, headlength=quiver_head_length, \\\n",
    "                        headaxislength=quiver_head_axis_length, \\\n",
    "                        zorder=2) \n",
    "\n",
    "# # ==================================================\n",
    "# lons,lats = numpy.meshgrid(XLONG_M[0,:],XLAT_M[:,0])\n",
    "# field = raintot_400_clim\n",
    "# xi_smooth = scipy.ndimage.zoom(lons,0.5)\n",
    "# yi_smooth = scipy.ndimage.zoom(lats,0.5)\n",
    "# field_smooth = scipy.ndimage.zoom(field,0.5)\n",
    "# # ==================================================\n",
    "\n",
    "ax3 = mp.subplot(gs2[0,4:8], projection=cartopy.crs.PlateCarree())\n",
    "ax3.gridlines(ylocs=[0], color='0.25')\n",
    "ax3.coastlines(resolution='110m', lw=1)\n",
    "ax3.set_extent([XLONG_M.min(), XLONG_M.max(), XLAT_M.min(), XLAT_M.max()], crs=cartopy.crs.PlateCarree())\n",
    "#contour_plot4 = ax3.contourf(XLONG_M, XLAT_M, raintot_400_clim, levels=levels, cmap=cmap_precip, extend='max')\n",
    "contour_plot4 = ax3.contourf(lon_gpcp-180., lat_gpcp, raintot_400_FMAASO_clim_interp, levels=levels, cmap=cmap_precip, extend='max')\n",
    "contour_lines4 = ax3.contour(lon_gpcp-180., lat_gpcp, raintot_400_FMAASO_clim_interp, levels=[4,8], colors=line_colors, linewidths=[1])\n",
    "ax3.text(s='(e) WRF equinox', x=0, y=1.02, transform=ax3.transAxes, ha='left', va='bottom', fontsize=fontsize)\n",
    "quiver_plot4 = ax3.quiver(XLONG_M[::every_nth_lat,::every_nth_lon], \\\n",
    "                        XLAT_M[::every_nth_lat,::every_nth_lon], \\\n",
    "                        u_400_FMAASO_850mb_clim[::every_nth_lat,::every_nth_lon], \\\n",
    "                        v_400_FMAASO_850mb_clim[::every_nth_lat,::every_nth_lon], \\\n",
    "                        units='xy', scale=quiver_scale, width=quiver_shaft_width, \\\n",
    "                        color=quiver_color, headlength=quiver_head_length, \\\n",
    "                        headaxislength=quiver_head_axis_length, \\\n",
    "                        zorder=2)\n",
    "# contour_lines4 = ax3.contour(xi_smooth, yi_smooth, field_smooth, \\\n",
    "#                              levels=[4], colors=['orange'], linewidths=[1])\n",
    "\n",
    "ax5 = mp.subplot(gs2[1,4:8], projection=cartopy.crs.PlateCarree())\n",
    "ax5.gridlines(ylocs=[0], color='0.25')\n",
    "ax5.coastlines(resolution='110m')\n",
    "ax5.set_extent([XLONG_M.min(), XLONG_M.max(), XLAT_M.min(), XLAT_M.max()], crs=cartopy.crs.PlateCarree())\n",
    "contour_plot5 = ax5.contourf(lon_gpcp-180., lat_gpcp, raintot_400_MJJ_clim_interp, levels=levels, cmap=cmap_precip, extend='max')\n",
    "contour_lines5 = ax5.contour(lon_gpcp-180., lat_gpcp, raintot_400_MJJ_clim_interp, levels=[4,8], colors=line_colors, linewidths=[1])\n",
    "#contour_plot5 = ax5.contourf(lon_gpcp, lat_gpcp, gpcp_MJJ_mean, levels=levels, cmap=cmap_precip, extend='max')\n",
    "#contour_lines2 = ax5.contour(lon_gpcp, lat_gpcp, gpcp_MJJ_mean, levels=[4,8], colors=['firebrick'], linewidths=[2])\n",
    "ax5.text(s='(f) WRF summer solstice', x=0, y=1.02, transform=ax5.transAxes, ha='left', va='bottom', fontsize=fontsize)\n",
    "\n",
    "ax7 = mp.subplot(gs2[2,4:8], projection=cartopy.crs.PlateCarree())\n",
    "ax7.gridlines(ylocs=[0], color='0.25')\n",
    "ax7.coastlines(resolution='110m')\n",
    "ax7.set_extent([XLONG_M.min(), XLONG_M.max(), XLAT_M.min(), XLAT_M.max()], crs=cartopy.crs.PlateCarree())\n",
    "contour_plot7 = ax7.contourf(lon_gpcp-180., lat_gpcp, raintot_400_NDJ_clim_interp, levels=levels, cmap=cmap_precip, extend='max')\n",
    "contour_lines7 = ax7.contour(lon_gpcp-180., lat_gpcp, raintot_400_NDJ_clim_interp, levels=[4,8], colors=line_colors, linewidths=[1])\n",
    "#contour_plot6 = ax7.contourf(lon_gpcp, lat_gpcp, gpcp_NDJ_mean, levels=levels, cmap=cmap_precip, extend='max')\n",
    "#contour_lines3 = ax7.contour(lon_gpcp, lat_gpcp, gpcp_NDJ_mean, levels=[4,8], colors=['firebrick'], linewidths=[2])\n",
    "ax7.text(s='(g) WRF winter solstice', x=0, y=1.02, transform=ax7.transAxes, ha='left', va='bottom', fontsize=fontsize)\n",
    "\n",
    "ax_pos = ax1.get_position()\n",
    "cbar_ax = fig.add_axes([ax_pos.x1-0.05,ax_pos.y0,0.03,ax_pos.height])\n",
    "cbar = fig.colorbar(land_use_map, cax=cbar_ax, drawedges=True)\n",
    "cbar.dividers.set_color('0')\n",
    "cbar.dividers.set_linewidth(1)\n",
    "cbar.set_ticks(numpy.arange(1.5,16.6,1))\n",
    "cbar.set_ticklabels(type_name)\n",
    "cbar.ax.tick_params(labelsize=fontsize, length=0)\n",
    "\n",
    "cbar_orog_ax = fig.add_axes([ax_pos.x0+0.025,ax_pos.y0+0.015,0.02,ax_pos.height*0.5])\n",
    "cbar_orog = fig.colorbar(contour_orog, cax=cbar_orog_ax)\n",
    "cbar_orog_ax.tick_params(labelsize=fontsize)#, length=0)\n",
    "cbar_orog.set_label('m', fontsize=fontsize)\n",
    "#cbar_orog.set_ticks(levels_orog)\n",
    "#cbar_orog_ax.get_children().set_linewidths([2]*7)\n",
    "cbar_orog_ax.text(s='elevation', x=0, y=1.05, ha='left', va='bottom', \\\n",
    "                  transform=cbar_orog_ax.transAxes, fontsize=fontsize)\n",
    "cbar_orog.outline.set_visible(False)\n",
    "cbar_orog_ax.tick_params(axis='y', which='both', right='off')\n",
    "cbar_orog_ax.get_children()[0].set_linewidths(3)\n",
    "cbar_orog_ax.get_children()[3].set_facecolor('None')#('None')\n",
    "\n",
    "ax_top = ax3.get_position()\n",
    "ax_bottom = ax7.get_position()\n",
    "cbar_ax = fig.add_axes([ax_top.x1+0.02,ax_bottom.y0,0.03,ax_top.y1-ax_bottom.y0])\n",
    "cbar = mp.colorbar(contour_plot1, cax=cbar_ax, orientation='vertical')\n",
    "cbar.ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_label('mm day$^{\\,-1}$', fontsize=fontsize)\n",
    "cbar.add_lines(contour_lines3)\n",
    "\n",
    "gs1.update(left=ax1.get_position().x0-0.05225)\n",
    "\n",
    "quiver_key = ax2.quiverkey(quiver_plot1, X=1.0, Y=1.06, labelpos='E', label='', \\\n",
    "                                  U=quiver_key_scale, coordinates='axes')\n",
    "quiver_key = ax3.quiverkey(quiver_plot4, X=1.0, Y=1.06, labelpos='E', label='', \\\n",
    "                                  U=quiver_key_scale, coordinates='axes')\n",
    "quiver_key_text = ax2.text(s=str(quiver_key_scale)+' m s$^{\\,-1}$', x=0.94, y=1.006, \\\n",
    "                                  transform=ax2.transAxes, fontsize=fontsize, ha='right', va='bottom')\n",
    "quiver_key_text = ax3.text(s=str(quiver_key_scale)+' m s$^{\\,-1}$', x=0.94, y=1.006, \\\n",
    "                                  transform=ax3.transAxes, fontsize=fontsize, ha='right', va='bottom')\n",
    "\n",
    "mp.savefig('figure1_land_use_precipitation_winds.png', dpi=300, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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