gp1_wrf_MANUSCRIPT_energy_fluxes_cape_skewt_ANDES_REGION.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T23:42:47.271953Z",
     "start_time": "2018-06-29T23:42:43.880994Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import xarray\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as mp\n",
    "import datetime\n",
    "import glob\n",
    "import netCDF4\n",
    "import pandas\n",
    "import wrf\n",
    "import string\n",
    "import seaborn\n",
    "import matplotlib.colors\n",
    "import cmocean\n",
    "import matplotlib.cm\n",
    "\n",
    "import metpy\n",
    "import metpy.plots\n",
    "import metpy.calc\n",
    "import metpy.units\n",
    "\n",
    "import cartopy\n",
    "\n",
    "DEGREE_SIGN = u'\\u00B0'\n",
    "mp.rcParams.update({'mathtext.default': 'regular'})\n",
    "\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# set up wet and dry regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T23:43:00.967867Z",
     "start_time": "2018-06-29T23:43:00.958378Z"
    }
   },
   "outputs": [],
   "source": [
    "dry_lon_lo, dry_lon_hi, dry_lat_lo, dry_lat_hi = -60,-50,-10,10\n",
    "wet_lon_lo, wet_lon_hi, wet_lat_lo, wet_lat_hi = -80,-75,-10,10\n",
    "\n",
    "dry_lon_lo_degE = 360+dry_lon_lo\n",
    "dry_lon_hi_degE = 360+dry_lon_hi\n",
    "\n",
    "wet_lon_lo_degE = 360+wet_lon_lo\n",
    "wet_lon_hi_degE = 360+wet_lon_hi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T05:54:53.039819Z",
     "start_time": "2018-07-01T05:54:53.035730Z"
    }
   },
   "outputs": [],
   "source": [
    "lon_lo = dry_lon_lo\n",
    "lon_hi = dry_lon_hi\n",
    "lat_lo = dry_lat_lo\n",
    "lat_hi = dry_lat_hi\n",
    "\n",
    "# lon_lo = wet_lon_lo\n",
    "# lon_hi = wet_lon_hi\n",
    "# lat_lo = wet_lat_lo\n",
    "# lat_hi = wet_lat_hi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# open control data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T23:43:02.923012Z",
     "start_time": "2018-06-29T23:43:02.627732Z"
    }
   },
   "outputs": [],
   "source": [
    "root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/400/ens_mean_wrfout_files'\n",
    "file_list = sorted(glob.glob(root_dir+'/'+'wrfout*.nc'))\n",
    "ds_list = [netCDF4.Dataset(file) for file in file_list]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T20:25:50.798686Z",
     "start_time": "2018-06-27T20:16:43.318449Z"
    }
   },
   "source": [
    "td_400 = wrf.getvar(ds_list, 'td', units='K', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_td_400.npy', td_400)\n",
    "\n",
    "cape3d_400 = wrf.getvar(ds_list, 'cape_3d', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_cape3d_400.npy', cape3d_400)\n",
    "\n",
    "cape2d_400 = wrf.getvar(ds_list, 'cape_2d', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_cape2d_400.npy', cape2d_400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T23:43:12.911049Z",
     "start_time": "2018-06-29T23:43:03.566152Z"
    }
   },
   "outputs": [],
   "source": [
    "cont_mfds = xarray.open_mfdataset(root_dir + '/' + 'wrfout*.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T23:47:44.776196Z",
     "start_time": "2018-06-29T23:43:12.919618Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400 = numpy.load('DATA_ptot_400.npy')\n",
    "geopot_400 = numpy.load('DATA_geopot_400.npy')\n",
    "#cape3d_400 = numpy.load('DATA_cape3d_400.npy')\n",
    "#cape2d_400 = numpy.load('DATA_cape2d_400.npy')\n",
    "td_400 = numpy.load('DATA_td_400.npy')\n",
    "tk_400 = numpy.load('DATA_tk_400.npy')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T23:47:44.809283Z",
     "start_time": "2018-06-29T23:47:44.784593Z"
    }
   },
   "source": [
    "cldfra_400 = cont_mfds['CLDFRA']\n",
    "\n",
    "fira_400 = cont_mfds['FIRA']\n",
    "fsa_400 = cont_mfds['FSA']\n",
    "glw_400 = cont_mfds['GLW']\n",
    "swdown_400 = cont_mfds['SWDOWN']\n",
    "\n",
    "alwdnb_400 = cont_mfds['ACLWDNB']\n",
    "aswdnb_400 = cont_mfds['ACSWDNB']\n",
    "alwupb_400 = cont_mfds['ACLWUPB']\n",
    "aswupb_400 = cont_mfds['ACSWUPB']\n",
    "\n",
    "alwdnbc_400 = cont_mfds['ACLWDNBC']\n",
    "aswdnbc_400 = cont_mfds['ACSWDNBC']\n",
    "alwupbc_400 = cont_mfds['ACLWUPBC']\n",
    "aswupbc_400 = cont_mfds['ACSWUPBC']\n",
    "\n",
    "alwdnt_400 = cont_mfds['ACLWDNT']\n",
    "aswdnt_400 = cont_mfds['ACSWDNT']\n",
    "alwupt_400 = cont_mfds['ACLWUPT']\n",
    "aswupt_400 = cont_mfds['ACSWUPT']\n",
    "\n",
    "alwdntc_400 = cont_mfds['ACLWDNTC']\n",
    "aswdntc_400 = cont_mfds['ACSWDNTC']\n",
    "alwuptc_400 = cont_mfds['ACLWUPTC']\n",
    "aswuptc_400 = cont_mfds['ACSWUPTC']"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T23:47:45.139322Z",
     "start_time": "2018-06-29T23:47:44.817437Z"
    }
   },
   "source": [
    "qvapor_400 = cont_mfds['QVAPOR']"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T23:47:53.150529Z",
     "start_time": "2018-06-29T23:47:45.147773Z"
    }
   },
   "source": [
    "net_atm_400_sw = (aswupb_400-aswupt_400)+(aswdnt_400-aswdnb_400)\n",
    "net_atm_400_lw = (alwupb_400-alwupt_400)+(alwdnt_400-alwdnb_400)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T23:48:02.910898Z",
     "start_time": "2018-06-29T23:47:55.383388Z"
    }
   },
   "source": [
    "net_atm_400_swc = (aswupbc_400-aswuptc_400)+(aswdntc_400-aswdnbc_400)\n",
    "net_atm_400_lwc = (alwupbc_400-alwuptc_400)+(alwdntc_400-alwdnbc_400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T05:54:34.168830Z",
     "start_time": "2018-07-01T05:54:34.152161Z"
    }
   },
   "outputs": [],
   "source": [
    "hgt = cont_mfds['HGT'].sel(Time=0).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T23:48:04.451249Z",
     "start_time": "2018-06-29T23:48:02.919472Z"
    }
   },
   "outputs": [],
   "source": [
    "time = cont_mfds['Times'].values\n",
    "xlat = cont_mfds['XLAT'].values[0,:,:]\n",
    "xlong = cont_mfds['XLONG'].values[0,:,:]\n",
    "landmask = cont_mfds['LANDMASK'].values[0,:,:]\n",
    "landmask_midpoints = landmask[1:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T23:48:05.591448Z",
     "start_time": "2018-06-29T23:48:05.584363Z"
    }
   },
   "outputs": [],
   "source": [
    "landmask_indices = numpy.copy(landmask)\n",
    "landmask_indices[landmask==1]=1\n",
    "landmask_indices[landmask!=1]=0\n",
    "landmask_indices = numpy.array(landmask_indices, dtype=bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T05:55:30.185146Z",
     "start_time": "2018-07-01T05:55:30.175684Z"
    }
   },
   "outputs": [],
   "source": [
    "lon_indices = (xlong>=lon_lo)&(xlong<=lon_hi)\n",
    "lat_indices = (xlat>=lat_lo)&(xlat<=lat_hi)\n",
    "latlon_indices = (lon_indices)&(lat_indices)&(landmask_indices)\n",
    "latlon_indices_high_andes = (lon_indices)&(lat_indices)&(landmask_indices)&(hgt>1500.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T05:55:35.824667Z",
     "start_time": "2018-07-01T05:55:35.512713Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2b9bdc754240>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mp.contourf(latlon_indices);mp.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# open phys data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T05:55:49.044483Z",
     "start_time": "2018-07-01T05:55:48.914452Z"
    }
   },
   "outputs": [],
   "source": [
    "root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/1000/ens_mean_wrfout_files'\n",
    "file_list = sorted(glob.glob(root_dir+'/'+'wrfout*.nc'))\n",
    "ds_list = [netCDF4.Dataset(file) for file in file_list]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T20:36:34.057718Z",
     "start_time": "2018-06-27T20:25:57.865123Z"
    }
   },
   "source": [
    "td_1000 = wrf.getvar(ds_list, 'td', units='K', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_td_1000.npy', td_1000)\n",
    "\n",
    "cape3d_1000 = wrf.getvar(ds_list, 'cape_3d', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_cape3d_1000.npy', cape3d_1000)\n",
    "\n",
    "cape2d_1000 = wrf.getvar(ds_list, 'cape_2d', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_cape2d_1000.npy', cape2d_1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T05:57:09.013494Z",
     "start_time": "2018-07-01T05:57:04.784340Z"
    }
   },
   "outputs": [],
   "source": [
    "phys_mfds = xarray.open_mfdataset(root_dir + '/' + 'wrfout*.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T05:59:22.334295Z",
     "start_time": "2018-07-01T05:57:10.555265Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_1000 = numpy.load('DATA_ptot_1000.npy')\n",
    "#cape3d_1000 = numpy.load('DATA_cape3d_1000.npy')\n",
    "#cape2d_1000 = numpy.load('DATA_cape2d_1000.npy')\n",
    "td_1000 = numpy.load('DATA_td_1000.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T05:59:46.568470Z",
     "start_time": "2018-07-01T05:59:24.243254Z"
    }
   },
   "outputs": [],
   "source": [
    "tk_1000 = numpy.load('DATA_tk_1000.npy')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T05:59:48.565539Z",
     "start_time": "2018-07-01T05:59:48.541320Z"
    }
   },
   "source": [
    "cldfra_1000 = phys_mfds['CLDFRA']\n",
    "\n",
    "fira_1000 = phys_mfds['FIRA']\n",
    "fsa_1000 = phys_mfds['FSA']\n",
    "glw_1000 = phys_mfds['GLW']\n",
    "swdown_1000 = phys_mfds['SWDOWN']\n",
    "\n",
    "alwdnb_1000 = phys_mfds['ACLWDNB']\n",
    "aswdnb_1000 = phys_mfds['ACSWDNB']\n",
    "alwupb_1000 = phys_mfds['ACLWUPB']\n",
    "aswupb_1000 = phys_mfds['ACSWUPB']\n",
    "\n",
    "alwdnbc_1000 = phys_mfds['ACLWDNBC']\n",
    "aswdnbc_1000 = phys_mfds['ACSWDNBC']\n",
    "alwupbc_1000 = phys_mfds['ACLWUPBC']\n",
    "aswupbc_1000 = phys_mfds['ACSWUPBC']\n",
    "\n",
    "alwdnt_1000 = phys_mfds['ACLWDNT']\n",
    "aswdnt_1000 = phys_mfds['ACSWDNT']\n",
    "alwupt_1000 = phys_mfds['ACLWUPT']\n",
    "aswupt_1000 = phys_mfds['ACSWUPT']\n",
    "\n",
    "alwdntc_1000 = phys_mfds['ACLWDNTC']\n",
    "aswdntc_1000 = phys_mfds['ACSWDNTC']\n",
    "alwuptc_1000 = phys_mfds['ACLWUPTC']\n",
    "aswuptc_1000 = phys_mfds['ACSWUPTC']"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T05:59:50.581848Z",
     "start_time": "2018-07-01T05:59:50.575783Z"
    }
   },
   "source": [
    "qvapor_1000 = phys_mfds['QVAPOR']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## calculate from perspective of atmosphere"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T02:03:21.743237Z",
     "start_time": "2018-06-27T02:03:03.566249Z"
    }
   },
   "source": [
    "net_atm_1000_sw = (aswupb_1000-aswupt_1000)+(aswdnt_1000-aswdnb_1000)\n",
    "net_atm_1000_lw = (alwupb_1000-alwupt_1000)+(alwdnt_1000-alwdnb_1000)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T02:03:30.849201Z",
     "start_time": "2018-06-27T02:03:21.748664Z"
    }
   },
   "source": [
    "net_atm_1000_swc = (aswupbc_1000-aswuptc_1000)+(aswdntc_1000-aswdnbc_1000)\n",
    "net_atm_1000_lwc = (alwupbc_1000-alwuptc_1000)+(alwdntc_1000-alwdnbc_1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate area mean net bottom and top diffs"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T02:07:54.965118Z",
     "start_time": "2018-06-27T02:03:37.800079Z"
    }
   },
   "source": [
    "net_atm_diffs_1000_sw = numpy.zeros((net_atm_1000_sw.shape))*numpy.nan\n",
    "net_atm_diffs_1000_sw[:-1,:,:] = numpy.diff(net_atm_1000_sw, axis=0)\n",
    "\n",
    "net_atm_diffs_1000_lw = numpy.zeros((net_atm_1000_lw.shape))*numpy.nan\n",
    "net_atm_diffs_1000_lw[:-1,:,:] = numpy.diff(net_atm_1000_lw, axis=0)\n",
    "\n",
    "net_atm_diffs_400_sw = numpy.zeros((net_atm_400_sw.shape))*numpy.nan\n",
    "net_atm_diffs_400_sw[:-1,:,:] = numpy.diff(net_atm_400_sw, axis=0)\n",
    "\n",
    "net_atm_diffs_400_lw = numpy.zeros((net_atm_400_lw.shape))*numpy.nan\n",
    "net_atm_diffs_400_lw[:-1,:,:] = numpy.diff(net_atm_400_lw, axis=0)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T02:09:42.990640Z",
     "start_time": "2018-06-27T02:07:56.717945Z"
    }
   },
   "source": [
    "net_atm_diffs_1000_swc = numpy.zeros((net_atm_1000_swc.shape))*numpy.nan\n",
    "net_atm_diffs_1000_swc[:-1,:,:] = numpy.diff(net_atm_1000_swc, axis=0)\n",
    "\n",
    "net_atm_diffs_1000_lwc = numpy.zeros((net_atm_1000_lwc.shape))*numpy.nan\n",
    "net_atm_diffs_1000_lwc[:-1,:,:] = numpy.diff(net_atm_1000_lwc, axis=0)\n",
    "\n",
    "net_atm_diffs_400_swc = numpy.zeros((net_atm_400_swc.shape))*numpy.nan\n",
    "net_atm_diffs_400_swc[:-1,:,:] = numpy.diff(net_atm_400_swc, axis=0)\n",
    "\n",
    "net_atm_diffs_400_lwc = numpy.zeros((net_atm_400_lwc.shape))*numpy.nan\n",
    "net_atm_diffs_400_lwc[:-1,:,:] = numpy.diff(net_atm_400_lwc, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pressure averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T05:59:52.602978Z",
     "start_time": "2018-07-01T05:59:52.587661Z"
    }
   },
   "outputs": [],
   "source": [
    "#ptot_latlon_subset_timemean = numpy.mean(ptot_400[:,:,latlon_indices], axis=2).mean(axis=0)\n",
    "ptot_latlon_subset_timemean = numpy.mean(ptot_400[:,:,latlon_indices_high_andes], axis=2).mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# td diffs"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T06:14:29.036936Z",
     "start_time": "2018-06-29T06:12:25.002153Z"
    }
   },
   "source": [
    "td_400_latlon_subset = td_400[:,:,latlon_indices]\n",
    "td_1000_latlon_subset = td_1000[:,:,latlon_indices]\n",
    "\n",
    "td_400_latlon_mean = numpy.nanmean(td_400_latlon_subset, axis=2)\n",
    "td_1000_latlon_mean = numpy.nanmean(td_1000_latlon_subset, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T05:59:54.646483Z",
     "start_time": "2018-07-01T05:59:54.606321Z"
    }
   },
   "outputs": [],
   "source": [
    "td_400_latlon_subset = td_400[:,:,latlon_indices_high_andes]\n",
    "td_1000_latlon_subset = td_1000[:,:,latlon_indices_high_andes]\n",
    "\n",
    "td_400_latlon_mean = numpy.nanmean(td_400_latlon_subset, axis=2)\n",
    "td_1000_latlon_mean = numpy.nanmean(td_1000_latlon_subset, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T06:00:47.363573Z",
     "start_time": "2018-07-01T06:00:47.337437Z"
    }
   },
   "outputs": [],
   "source": [
    "td_400_latlon_alltime = numpy.nanmean(td_400_latlon_subset, axis=(0,2))\n",
    "td_1000_latlon_alltime = numpy.nanmean(td_400_latlon_subset, axis=(0,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# tk diffs"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T06:15:57.941316Z",
     "start_time": "2018-06-29T06:14:41.695003Z"
    }
   },
   "source": [
    "tk_400_latlon_subset = tk_400[:,:,latlon_indices]\n",
    "tk_1000_latlon_subset = tk_1000[:,:,latlon_indices]\n",
    "\n",
    "tk_400_latlon_mean = numpy.nanmean(tk_400_latlon_subset, axis=2)\n",
    "tk_1000_latlon_mean = numpy.nanmean(tk_1000_latlon_subset, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T06:01:03.456236Z",
     "start_time": "2018-07-01T06:01:03.415043Z"
    }
   },
   "outputs": [],
   "source": [
    "tk_400_latlon_subset = tk_400[:,:,latlon_indices_high_andes]\n",
    "tk_1000_latlon_subset = tk_1000[:,:,latlon_indices_high_andes]\n",
    "\n",
    "tk_400_latlon_mean = numpy.nanmean(tk_400_latlon_subset, axis=2)\n",
    "tk_1000_latlon_mean = numpy.nanmean(tk_1000_latlon_subset, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T06:01:10.038630Z",
     "start_time": "2018-07-01T06:01:10.015002Z"
    }
   },
   "outputs": [],
   "source": [
    "tk_400_latlon_alltime = numpy.nanmean(tk_400_latlon_subset, axis=(0,2))\n",
    "tk_1000_latlon_alltime = numpy.nanmean(tk_1000_latlon_subset, axis=(0,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# qvapor diffs"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T06:19:58.070992Z",
     "start_time": "2018-06-29T06:16:10.604997Z"
    }
   },
   "source": [
    "qvapor_400_latlon_subset = qvapor_400.values[:,:,latlon_indices]\n",
    "qvapor_1000_latlon_subset = qvapor_1000.values[:,:,latlon_indices]\n",
    "\n",
    "qvapor_400_latlon_mean = numpy.nanmean(qvapor_400_latlon_subset, axis=(2))\n",
    "qvapor_1000_latlon_mean = numpy.nanmean(qvapor_1000_latlon_subset, axis=(2))\n",
    "\n",
    "qvapor_diffs_latlon_mean = qvapor_1000_latlon_mean-qvapor_400_latlon_mean"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T06:01:54.808524Z",
     "start_time": "2018-07-01T06:01:24.443012Z"
    }
   },
   "source": [
    "qvapor_400_latlon_subset = qvapor_400.values[:,:,latlon_indices_high_andes]\n",
    "qvapor_1000_latlon_subset = qvapor_1000.values[:,:,latlon_indices_high_andes]\n",
    "\n",
    "qvapor_400_latlon_mean = numpy.nanmean(qvapor_400_latlon_subset, axis=(2))\n",
    "qvapor_1000_latlon_mean = numpy.nanmean(qvapor_1000_latlon_subset, axis=(2))\n",
    "\n",
    "qvapor_diffs_latlon_mean = qvapor_1000_latlon_mean-qvapor_400_latlon_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# cape diffs"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T06:05:18.824065Z",
     "start_time": "2018-07-01T06:05:18.743317Z"
    }
   },
   "source": [
    "cape3d_cape_400_latlon_subset = cape3d_400[0,:,:,:,:][:,:,latlon_indices_high_andes]\n",
    "cape3d_cape_1000_latlon_subset = cape3d_1000[0,:,:,:,:][:,:,latlon_indices_high_andes]\n",
    "\n",
    "cape3d_cape_diffs_latlon_mean = numpy.nanmean((cape3d_cape_1000_latlon_subset-cape3d_cape_400_latlon_subset), axis=2)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-28T01:51:35.833827Z",
     "start_time": "2018-06-28T01:51:35.639060Z"
    }
   },
   "source": [
    "cape2d_diffs = cape2d_1000-cape2d_400\n",
    "\n",
    "cape2d_mcape_diffs = cape2d_diffs[0,:,:,:]\n",
    "cape2d_mcin_diffs = cape2d_diffs[1,:,:,:]\n",
    "cape2d_lcl_diffs = cape2d_diffs[2,:,:,:]\n",
    "cape2d_lfc_diffs = cape2d_diffs[3,:,:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot skew T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T06:05:30.375890Z",
     "start_time": "2018-07-01T06:05:30.350092Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Quantity(184.75765048671235, 'joule / kilogram')>,\n",
       " <Quantity(-4.730256710574907, 'joule / kilogram')>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metpy.calc.surface_based_cape_cin(ptot_latlon_subset_timemean/100.*metpy.units.units.hPa, \\\n",
    "                                  (tk_400_latlon_alltime[:]-273.15)*metpy.units.units.degC, \\\n",
    "                                  (td_400_latlon_alltime[:]-273.15)*metpy.units.units.degC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# cape calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T06:05:42.649434Z",
     "start_time": "2018-07-01T06:05:41.979556Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(<Quantity(603.7628252154021, 'joule / kilogram')>, <Quantity(0.8418014298477846, 'joule / kilogram')>)\n",
      "(<Quantity(531.9287088912644, 'joule / kilogram')>, <Quantity(1.2607577286802822, 'joule / kilogram')>)\n"
     ]
    }
   ],
   "source": [
    "ptot_lo = 100\n",
    "ptot_hi = 1013.25\n",
    "hour = 19\n",
    "\n",
    "ptot_indices = (ptot_latlon_subset_timemean/100.<=ptot_hi)&(ptot_latlon_subset_timemean/100.>=ptot_lo)\n",
    "\n",
    "cape_400 = metpy.calc.surface_based_cape_cin(ptot_latlon_subset_timemean[ptot_indices]/100.*metpy.units.units.hPa, \\\n",
    "                                  (tk_400_latlon_mean[hour,ptot_indices]-273.15)*metpy.units.units.degC, \\\n",
    "                                  (td_400_latlon_mean[hour,ptot_indices]-273.15)*metpy.units.units.degC)\n",
    "\n",
    "cape_1000 = metpy.calc.surface_based_cape_cin(ptot_latlon_subset_timemean[ptot_indices]/100.*metpy.units.units.hPa, \\\n",
    "                                  (tk_1000_latlon_mean[hour,ptot_indices]-273.15)*metpy.units.units.degC, \\\n",
    "                                  (td_1000_latlon_mean[hour,ptot_indices]-273.15)*metpy.units.units.degC)\n",
    "\n",
    "print(cape_400)\n",
    "print(cape_1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3-hour afternoon mean"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-28T22:48:05.917221Z",
     "start_time": "2018-06-28T22:48:05.471278Z"
    }
   },
   "source": [
    "ptot_lo = 300\n",
    "ptot_hi = 1013.25\n",
    "hour = 19\n",
    "\n",
    "tk_400_afternoon = numpy.nanmean(tk_400_latlon_mean[(hour-1):(hour+2),:], axis=0)\n",
    "td_400_afternoon = numpy.nanmean(td_400_latlon_mean[(hour-1):(hour+2),:], axis=0)\n",
    "\n",
    "ptot_indices = (ptot_latlon_subset_timemean/100.<=ptot_hi)&(ptot_latlon_subset_timemean/100.>=ptot_lo)\n",
    "\n",
    "cape_400 = metpy.calc.surface_based_cape_cin(ptot_latlon_subset_timemean[ptot_indices]/100.*metpy.units.units.hPa, \\\n",
    "                                  (tk_400_afternoon[ptot_indices]-273.15)*metpy.units.units.degC, \\\n",
    "                                  (td_400_afternoon[ptot_indices]-273.15)*metpy.units.units.degC)\n",
    "\n",
    "cape_1000 = metpy.calc.surface_based_cape_cin(ptot_latlon_subset_timemean[ptot_indices]/100.*metpy.units.units.hPa, \\\n",
    "                                  (tk_1000_latlon_mean[hour,ptot_indices]-273.15)*metpy.units.units.degC, \\\n",
    "                                  (td_1000_latlon_mean[hour,ptot_indices]-273.15)*metpy.units.units.degC)\n",
    "\n",
    "print(cape_400)\n",
    "print(cape_1000)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-28T03:25:25.377744Z",
     "start_time": "2018-06-28T03:25:23.942102Z"
    }
   },
   "source": [
    "ptot_lo = 300\n",
    "ptot_hi = 1013.25\n",
    "\n",
    "ptot_indices = (ptot_latlon_subset_timemean/100.<=ptot_hi)&(ptot_latlon_subset_timemean/100.>=ptot_lo)\n",
    "\n",
    "cape_400 = metpy.calc.surface_based_cape_cin(ptot_latlon_subset_timemean[ptot_indices]/100.*metpy.units.units.hPa, \\\n",
    "                                  (tk_400_latlon_alltime[ptot_indices]-273.15)*metpy.units.units.degC, \\\n",
    "                                  (td_400_latlon_alltime[ptot_indices]-273.15)*metpy.units.units.degC)\n",
    "\n",
    "cape_1000 = metpy.calc.surface_based_cape_cin(ptot_latlon_subset_timemean[ptot_indices]/100.*metpy.units.units.hPa, \\\n",
    "                                  (tk_1000_latlon_alltime[ptot_indices]-273.15)*metpy.units.units.degC, \\\n",
    "                                  (td_1000_latlon_alltime[ptot_indices]-273.15)*metpy.units.units.degC)\n",
    "\n",
    "print(cape_400)\n",
    "print(cape_1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T06:05:48.109921Z",
     "start_time": "2018-07-01T06:05:48.101292Z"
    }
   },
   "outputs": [],
   "source": [
    "color_list = [\\\n",
    "matplotlib.cm.tab20c(4/20), \\\n",
    "matplotlib.cm.tab20c(5.5/20), \\\n",
    "matplotlib.cm.tab20c(0/20), \\\n",
    "matplotlib.cm.tab20c(1.5/20) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T06:09:30.105315Z",
     "start_time": "2018-06-29T06:09:25.552079Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 320.4x306 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "hour = 19\n",
    "\n",
    "skewt_plot = metpy.plots.SkewT()\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., tk_1000_latlon_mean[hour,:]-273.15, lw=3, label='phys. T$_{env}$', c=color_list[0])\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., tk_400_latlon_mean[hour,:]-273.15, lw=3, label='cont. T$_{env}$', ls='--', c=color_list[1])\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., td_1000_latlon_mean[hour,:]-273.15, lw=3, label='phys. T$_d$', c=color_list[2])\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., td_400_latlon_mean[hour,:]-273.15, lw=3, label='cont. T$_d$', ls='--', c=color_list[3])\n",
    "\n",
    "pprofile_400 = metpy.calc.parcel_profile(ptot_latlon_subset_timemean/100.*metpy.units.units.hPa, \\\n",
    "                                         (tk_400_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC, \\\n",
    "                                         (td_400_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC).to('degC')\n",
    "pprofile_1000 = metpy.calc.parcel_profile(ptot_latlon_subset_timemean/100.*metpy.units.units.hPa, \\\n",
    "                                         (tk_1000_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC, \\\n",
    "                                         (td_1000_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC).to('degC')\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., pprofile_1000, label='phys. profile', c='0.1', lw=3, ls='-')\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., pprofile_400, label='cont. profile', c='0.5', lw=3, ls='--')\n",
    "\n",
    "\n",
    "# fill in control and phys CAPE\n",
    "skewt_plot.shade_cape(ptot_latlon_subset_timemean/100.,\\\n",
    "                      tk_1000_latlon_mean[hour,:]-273.15,\\\n",
    "                      numpy.array(pprofile_1000),\\\n",
    "                      edgecolor='None',facecolor=matplotlib.cm.tab20c(8/20),alpha=0.666,\\\n",
    "                      label='phys. CAPE')\n",
    "\n",
    "skewt_plot.shade_cape(ptot_latlon_subset_timemean/100.,\\\n",
    "                      tk_400_latlon_mean[hour,:]-273.15,\\\n",
    "                      numpy.array(pprofile_400),\\\n",
    "                      edgecolor='None',facecolor=matplotlib.cm.tab20c(8/20),alpha=0.333,\\\n",
    "                      label='cont. CAPE')\n",
    "\n",
    "skewt_plot.ax.set_ylim(1013.25,600)\n",
    "skewt_plot.ax.set_xlim(10,30)\n",
    "\n",
    "fig = mp.gcf()\n",
    "fig.set_size_inches(4.45,4.25)\n",
    "fig.tight_layout()\n",
    "\n",
    "skewt_plot.ax.tick_params(labelsize=fontsize)\n",
    "skewt_plot.ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "skewt_plot.ax.set_xlabel('$\\degree$C', fontsize=fontsize)\n",
    "\n",
    "skewt_plot.plot_dry_adiabats(color='0.5',linewidth=1,linestyle='-')\n",
    "skewt_plot.plot_moist_adiabats(color='0.5',linestyle=':')\n",
    "skewt_plot.plot_mixing_lines(color='0.5',linestyle='--',lw=1)\n",
    "\n",
    "handles, labels = skewt_plot.ax.get_legend_handles_labels()\n",
    "skewt_plot.ax.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "                     bbox_to_anchor=(1.03,1.0), \\\n",
    "                     bbox_transform=skewt_plot.ax.transAxes, loc='upper left', \\\n",
    "                     borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "                     edgecolor='0', framealpha=0, borderpad=0.25, \\\n",
    "                     columnspacing=0.5, handletextpad=0.5, markerscale=1)\n",
    "\n",
    "skewt_plot.ax.text(s='skew-T diagram at 15:00 on day 1',\\\n",
    "                  x=0,y=1.02,\\\n",
    "                  transform=skewt_plot.ax.transAxes,\\\n",
    "                  ha='left',va='bottom', fontsize=fontsize)\n",
    "\n",
    "skewt_plot.ax.annotate(\"\",xy=(11.6,700), \\\n",
    "                       xycoords='data',\\\n",
    "                       xytext=(13.6, 700), \\\n",
    "                       textcoords='data',\\\n",
    "                       arrowprops=dict(facecolor='0.1',ec='None',width=2,headwidth=6,headlength=10))\n",
    "\n",
    "skewt_plot.ax.annotate(\"\",xy=(10.9,700), \\\n",
    "                       xycoords='data',\\\n",
    "                       xytext=(8.9, 700), \\\n",
    "                       textcoords='data',\\\n",
    "                       arrowprops=dict(facecolor='0.1',ec='None',width=2,headwidth=6,headlength=10))\n",
    "\n",
    "CAPE_CIN_text = 'CAPE$_{\\,cont}$ = '+'{:.0f}'.format(numpy.array(cape_400[0]))+' J kg$^{\\,-1}$\\n'\\\n",
    "                'CAPE$_{\\,phys}$ = '+'{:.0f}'.format(numpy.array(cape_1000[0]))+' J kg$^{\\,-1}$\\n'\\\n",
    "                'CIN$_{\\,cont}$ = '+'{:.0f}'.format(numpy.array(cape_400[1]))+' J kg$^{\\,-1}$\\n'\\\n",
    "                'CIN$_{\\,phys}$ = '+'{:.0f}'.format(numpy.array(cape_1000[1]))+' J kg$^{\\,-1}$'\n",
    "skewt_plot.ax.text(s=CAPE_CIN_text,x=1.02,y=0,ha='left',va='bottom',transform=skewt_plot.ax.transAxes,fontsize=fontsize)\n",
    "\n",
    "fig.savefig('figures/manuscript_both_cape_skewT_hour'+str(hour-4)+'_zoom_amazon_region.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fontsize=12\n",
    "hour = 19\n",
    "\n",
    "skewt_plot = metpy.plots.SkewT()\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., tk_1000_latlon_mean[hour,:]-273.15, lw=3, label='phys. T$_{env}$', c=color_list[0])\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., tk_400_latlon_mean[hour,:]-273.15, lw=3, label='cont. T$_{env}$', ls='--', c=color_list[1])\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., td_1000_latlon_mean[hour,:]-273.15, lw=3, label='phys. T$_d$', c=color_list[2])\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., td_400_latlon_mean[hour,:]-273.15, lw=3, label='cont. T$_d$', ls='--', c=color_list[3])\n",
    "\n",
    "pprofile_400 = metpy.calc.parcel_profile(ptot_latlon_subset_timemean/100.*metpy.units.units.hPa, \\\n",
    "                                         (tk_400_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC, \\\n",
    "                                         (td_400_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC).to('degC')\n",
    "pprofile_1000 = metpy.calc.parcel_profile(ptot_latlon_subset_timemean/100.*metpy.units.units.hPa, \\\n",
    "                                         (tk_1000_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC, \\\n",
    "                                         (td_1000_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC).to('degC')\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., pprofile_1000, label='phys. profile', c='0.1', lw=3, ls='-')\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., pprofile_400, label='cont. profile', c='0.5', lw=3, ls='--')\n",
    "\n",
    "\n",
    "# fill in control and phys CAPE\n",
    "skewt_plot.shade_cape(ptot_latlon_subset_timemean/100.,\\\n",
    "                      tk_1000_latlon_mean[hour,:]-273.15,\\\n",
    "                      numpy.array(pprofile_1000),\\\n",
    "                      edgecolor='None',facecolor=matplotlib.cm.tab20c(8/20),alpha=0.666,\\\n",
    "                      label='phys. CAPE')\n",
    "\n",
    "skewt_plot.shade_cape(ptot_latlon_subset_timemean/100.,\\\n",
    "                      tk_400_latlon_mean[hour,:]-273.15,\\\n",
    "                      numpy.array(pprofile_400),\\\n",
    "                      edgecolor='None',facecolor=matplotlib.cm.tab20c(8/20),alpha=0.333,\\\n",
    "                      label='cont. CAPE')\n",
    "\n",
    "skewt_plot.ax.set_ylim(1013.25,600)\n",
    "skewt_plot.ax.set_xlim(10,25)\n",
    "\n",
    "fig = mp.gcf()\n",
    "fig.set_size_inches(4.45,4.25)\n",
    "fig.tight_layout()\n",
    "\n",
    "skewt_plot.ax.tick_params(labelsize=fontsize)\n",
    "skewt_plot.ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "skewt_plot.ax.set_xlabel('$\\degree$C', fontsize=fontsize)\n",
    "\n",
    "skewt_plot.plot_dry_adiabats(color='0.5',linewidth=1,linestyle='-')\n",
    "skewt_plot.plot_moist_adiabats(color='0.5',linestyle=':')\n",
    "skewt_plot.plot_mixing_lines(color='0.5',linestyle='--',lw=1)\n",
    "\n",
    "handles, labels = skewt_plot.ax.get_legend_handles_labels()\n",
    "skewt_plot.ax.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "                     bbox_to_anchor=(1.03,1.0), \\\n",
    "                     bbox_transform=skewt_plot.ax.transAxes, loc='upper left', \\\n",
    "                     borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "                     edgecolor='0', framealpha=0, borderpad=0.25, \\\n",
    "                     columnspacing=0.5, handletextpad=0.5, markerscale=1)\n",
    "\n",
    "skewt_plot.ax.text(s='skew-T diagram at 15:00 on day 1',\\\n",
    "                  x=0,y=1.02,\\\n",
    "                  transform=skewt_plot.ax.transAxes,\\\n",
    "                  ha='left',va='bottom', fontsize=fontsize)\n",
    "\n",
    "skewt_plot.ax.annotate(\"\",xy=(11.6,700), \\\n",
    "                       xycoords='data',\\\n",
    "                       xytext=(13.6, 700), \\\n",
    "                       textcoords='data',\\\n",
    "                       arrowprops=dict(facecolor='0.1',ec='None',width=2,headwidth=6,headlength=10))\n",
    "\n",
    "skewt_plot.ax.annotate(\"\",xy=(10.9,700), \\\n",
    "                       xycoords='data',\\\n",
    "                       xytext=(8.9, 700), \\\n",
    "                       textcoords='data',\\\n",
    "                       arrowprops=dict(facecolor='0.1',ec='None',width=2,headwidth=6,headlength=10))\n",
    "\n",
    "CAPE_CIN_text = 'CAPE$_{\\,cont}$ = '+'{:.0f}'.format(numpy.array(cape_400[0]))+' J kg$^{\\,-1}$\\n'\\\n",
    "                'CAPE$_{\\,phys}$ = '+'{:.0f}'.format(numpy.array(cape_1000[0]))+' J kg$^{\\,-1}$\\n'\\\n",
    "                'CIN$_{\\,cont}$ = '+'{:.0f}'.format(numpy.array(cape_400[1]))+' J kg$^{\\,-1}$\\n'\\\n",
    "                'CIN$_{\\,phys}$ = '+'{:.0f}'.format(numpy.array(cape_1000[1]))+' J kg$^{\\,-1}$'\n",
    "skewt_plot.ax.text(s=CAPE_CIN_text,x=1.02,y=0,ha='left',va='bottom',transform=skewt_plot.ax.transAxes,fontsize=fontsize)\n",
    "\n",
    "fig.savefig('figures/manuscript_both_cape_skewT_hour'+str(hour-4)+'_zoom_andes_region.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T06:08:17.018831Z",
     "start_time": "2018-07-01T06:08:12.458567Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 320.4x306 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "hour = 19\n",
    "\n",
    "skewt_plot = metpy.plots.SkewT()\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., tk_1000_latlon_mean[hour,:]-273.15, lw=3, label='phys. T$_{env}$', c=color_list[0])\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., tk_400_latlon_mean[hour,:]-273.15, lw=3, label='cont. T$_{env}$', ls='--', c=color_list[1])\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., td_1000_latlon_mean[hour,:]-273.15, lw=3, label='phys. T$_d$', c=color_list[2])\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., td_400_latlon_mean[hour,:]-273.15, lw=3, label='cont. T$_d$', ls='--', c=color_list[3])\n",
    "\n",
    "pprofile_400 = metpy.calc.parcel_profile(ptot_latlon_subset_timemean/100.*metpy.units.units.hPa, \\\n",
    "                                         (tk_400_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC, \\\n",
    "                                         (td_400_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC).to('degC')\n",
    "pprofile_1000 = metpy.calc.parcel_profile(ptot_latlon_subset_timemean/100.*metpy.units.units.hPa, \\\n",
    "                                         (tk_1000_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC, \\\n",
    "                                         (td_1000_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC).to('degC')\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., pprofile_1000, label='phys. profile', c='0.1', lw=3, ls='-')\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., pprofile_400, label='cont. profile', c='0.5', lw=3, ls='--')\n",
    "\n",
    "\n",
    "# fill in control and phys CAPE\n",
    "skewt_plot.shade_cape(ptot_latlon_subset_timemean/100.,\\\n",
    "                      tk_1000_latlon_mean[hour,:]-273.15,\\\n",
    "                      numpy.array(pprofile_1000),\\\n",
    "                      edgecolor='None',facecolor=matplotlib.cm.tab20c(8/20),alpha=0.666,\\\n",
    "                      label='phys. CAPE')\n",
    "\n",
    "skewt_plot.shade_cape(ptot_latlon_subset_timemean/100.,\\\n",
    "                      tk_400_latlon_mean[hour,:]-273.15,\\\n",
    "                      numpy.array(pprofile_400),\\\n",
    "                      edgecolor='None',facecolor=matplotlib.cm.tab20c(8/20),alpha=0.333,\\\n",
    "                      label='cont. CAPE')\n",
    "\n",
    "skewt_plot.ax.set_ylim(1013.25,600)\n",
    "skewt_plot.ax.set_xlim(10,25)\n",
    "\n",
    "fig = mp.gcf()\n",
    "fig.set_size_inches(4.45,4.25)\n",
    "fig.tight_layout()\n",
    "\n",
    "skewt_plot.ax.tick_params(labelsize=fontsize)\n",
    "skewt_plot.ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "skewt_plot.ax.set_xlabel('$\\degree$C', fontsize=fontsize)\n",
    "\n",
    "skewt_plot.plot_dry_adiabats(color='0.5',linewidth=1,linestyle='-')\n",
    "skewt_plot.plot_moist_adiabats(color='0.5',linestyle=':')\n",
    "skewt_plot.plot_mixing_lines(color='0.5',linestyle='--',lw=1)\n",
    "\n",
    "handles, labels = skewt_plot.ax.get_legend_handles_labels()\n",
    "skewt_plot.ax.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "                     bbox_to_anchor=(1.03,1.0), \\\n",
    "                     bbox_transform=skewt_plot.ax.transAxes, loc='upper left', \\\n",
    "                     borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "                     edgecolor='0', framealpha=0, borderpad=0.25, \\\n",
    "                     columnspacing=0.5, handletextpad=0.5, markerscale=1)\n",
    "\n",
    "skewt_plot.ax.text(s='skew-T diagram at 15:00 on day 1',\\\n",
    "                  x=0,y=1.02,\\\n",
    "                  transform=skewt_plot.ax.transAxes,\\\n",
    "                  ha='left',va='bottom', fontsize=fontsize)\n",
    "\n",
    "skewt_plot.ax.annotate(\"\",xy=(11.6,700), \\\n",
    "                       xycoords='data',\\\n",
    "                       xytext=(13.6, 700), \\\n",
    "                       textcoords='data',\\\n",
    "                       arrowprops=dict(facecolor='0.1',ec='None',width=2,headwidth=6,headlength=10))\n",
    "\n",
    "skewt_plot.ax.annotate(\"\",xy=(10.9,700), \\\n",
    "                       xycoords='data',\\\n",
    "                       xytext=(8.9, 700), \\\n",
    "                       textcoords='data',\\\n",
    "                       arrowprops=dict(facecolor='0.1',ec='None',width=2,headwidth=6,headlength=10))\n",
    "\n",
    "CAPE_CIN_text = 'CAPE$_{\\,cont}$ = '+'{:.0f}'.format(numpy.array(cape_400[0]))+' J kg$^{\\,-1}$\\n'\\\n",
    "                'CAPE$_{\\,phys}$ = '+'{:.0f}'.format(numpy.array(cape_1000[0]))+' J kg$^{\\,-1}$\\n'\\\n",
    "                'CIN$_{\\,cont}$ = '+'{:.0f}'.format(numpy.array(cape_400[1]))+' J kg$^{\\,-1}$\\n'\\\n",
    "                'CIN$_{\\,phys}$ = '+'{:.0f}'.format(numpy.array(cape_1000[1]))+' J kg$^{\\,-1}$'\n",
    "skewt_plot.ax.text(s=CAPE_CIN_text,x=1.02,y=0,ha='left',va='bottom',transform=skewt_plot.ax.transAxes,fontsize=fontsize)\n",
    "\n",
    "fig.savefig('figures/manuscript_both_cape_skewT_hour'+str(hour-4)+'_zoom_andes_region.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T06:09:05.806762Z",
     "start_time": "2018-06-29T06:08:59.252380Z"
    }
   },
   "source": [
    "fontsize=12\n",
    "hour = 18\n",
    "\n",
    "skewt_plot = metpy.plots.SkewT()\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., tk_1000_latlon_mean[hour,:]-273.15, lw=3, label='phys. Tenv', c='firebrick')\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., tk_400_latlon_mean[hour,:]-273.15, lw=3, label='cont. Tenv', c='coral', ls='--')\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., td_1000_latlon_mean[hour,:]-273.15, lw=3, label='phys. Td', c='dodgerblue')\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., td_400_latlon_mean[hour,:]-273.15, lw=3, label='cont. Td', c='skyblue', ls='--')\n",
    "\n",
    "pprofile_400 = metpy.calc.parcel_profile(ptot_latlon_subset_timemean/100.*metpy.units.units.hPa, \\\n",
    "                                         (tk_400_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC, \\\n",
    "                                         (td_400_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC).to('degC')\n",
    "pprofile_1000 = metpy.calc.parcel_profile(ptot_latlon_subset_timemean/100.*metpy.units.units.hPa, \\\n",
    "                                         (tk_1000_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC, \\\n",
    "                                         (td_1000_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC).to('degC')\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., pprofile_1000, label='phys. profile', c='0.1', lw=3, ls='-')\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., pprofile_400, label='cont. profile', c='0.5', lw=3, ls='--')\n",
    "\n",
    "skewt_plot.ax.set_ylim(1013.25,100)\n",
    "skewt_plot.ax.set_xlim(-45,35)\n",
    "\n",
    "fig = mp.gcf()\n",
    "fig.set_size_inches(6.5,6.5)\n",
    "fig.tight_layout()\n",
    "\n",
    "skewt_plot.ax.tick_params(labelsize=fontsize)\n",
    "skewt_plot.ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "skewt_plot.ax.set_xlabel('$\\degree$C', fontsize=fontsize)\n",
    "\n",
    "skewt_plot.plot_dry_adiabats(color='0.1',linewidth=1,linestyle='-')\n",
    "skewt_plot.plot_moist_adiabats(color='0.1',linestyle=':')\n",
    "skewt_plot.plot_mixing_lines(color='0.1',linestyle='--')\n",
    "\n",
    "handles, labels = skewt_plot.ax.get_legend_handles_labels()\n",
    "skewt_plot.ax.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "                     bbox_to_anchor=(1.03,1.0), \\\n",
    "                     bbox_transform=skewt_plot.ax.transAxes, loc='upper left', \\\n",
    "                     borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "                     edgecolor='0', framealpha=0, borderpad=0.25, \\\n",
    "                     columnspacing=0.5, handletextpad=0.5, markerscale=1)\n",
    "\n",
    "fig.savefig('both_cape_skewT_hour'+str(hour-4)+'_amazon_region.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T06:09:48.273083Z",
     "start_time": "2018-07-01T06:09:41.888743Z"
    }
   },
   "outputs": [],
   "source": [
    "fontsize=12\n",
    "hour = 20 # 20 for 3pm over Andes\n",
    "\n",
    "skewt_plot = metpy.plots.SkewT()\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., tk_1000_latlon_mean[hour,:]-273.15, lw=3, label='phys. Tenv', c='firebrick')\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., tk_400_latlon_mean[hour,:]-273.15, lw=3, label='cont. Tenv', c='coral', ls='--')\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., td_1000_latlon_mean[hour,:]-273.15, lw=3, label='phys. Td', c='dodgerblue')\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., td_400_latlon_mean[hour,:]-273.15, lw=3, label='cont. Td', c='skyblue', ls='--')\n",
    "\n",
    "pprofile_400 = metpy.calc.parcel_profile(ptot_latlon_subset_timemean/100.*metpy.units.units.hPa, \\\n",
    "                                         (tk_400_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC, \\\n",
    "                                         (td_400_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC).to('degC')\n",
    "pprofile_1000 = metpy.calc.parcel_profile(ptot_latlon_subset_timemean/100.*metpy.units.units.hPa, \\\n",
    "                                         (tk_1000_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC, \\\n",
    "                                         (td_1000_latlon_mean[hour,:][0]-273.15)*metpy.units.units.degC).to('degC')\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., pprofile_1000, label='phys. profile', c='0.1', lw=3, ls='-')\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., pprofile_400, label='cont. profile', c='0.5', lw=3, ls='--')\n",
    "\n",
    "skewt_plot.ax.set_ylim(1013.25,100)\n",
    "skewt_plot.ax.set_xlim(-45,35)\n",
    "\n",
    "fig = mp.gcf()\n",
    "fig.set_size_inches(6.5,6.5)\n",
    "fig.tight_layout()\n",
    "\n",
    "skewt_plot.ax.tick_params(labelsize=fontsize)\n",
    "skewt_plot.ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "skewt_plot.ax.set_xlabel('$\\degree$C', fontsize=fontsize)\n",
    "\n",
    "skewt_plot.plot_dry_adiabats(color='0.1',linewidth=1,linestyle='-')\n",
    "skewt_plot.plot_moist_adiabats(color='0.1',linestyle=':')\n",
    "skewt_plot.plot_mixing_lines(color='0.1',linestyle='--')\n",
    "\n",
    "handles, labels = skewt_plot.ax.get_legend_handles_labels()\n",
    "skewt_plot.ax.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "                     bbox_to_anchor=(1.03,1.0), \\\n",
    "                     bbox_transform=skewt_plot.ax.transAxes, loc='upper left', \\\n",
    "                     borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "                     edgecolor='0', framealpha=0, borderpad=0.25, \\\n",
    "                     columnspacing=0.5, handletextpad=0.5, markerscale=1)\n",
    "\n",
    "fig.savefig('both_cape_skewT_hour'+str(hour-5)+'_andes_region.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10 day mean skewT diagrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T06:10:25.144833Z",
     "start_time": "2018-07-01T06:10:25.131940Z"
    }
   },
   "outputs": [],
   "source": [
    "time_datetimes = pandas.to_datetime([i.decode('utf-8') for i in time], format = '%Y-%m-%d_%H:%M:%S')\n",
    "time_datetimes_andes = time_datetimes+datetime.timedelta(hours=-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T06:16:20.912877Z",
     "start_time": "2018-07-01T06:16:20.904062Z"
    }
   },
   "outputs": [],
   "source": [
    "hour_indices = [t.hour in [2,3,4] and t.day in range(2,11) for t in time_datetimes_andes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T06:16:24.188851Z",
     "start_time": "2018-07-01T06:16:23.614791Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_lo = 100\n",
    "ptot_hi = 1013.25\n",
    "\n",
    "tk_400_latlon_afternoon = numpy.nanmean(tk_400_latlon_mean[hour_indices,:], axis=0)\n",
    "td_400_latlon_afternoon = numpy.nanmean(td_400_latlon_mean[hour_indices,:], axis=0)\n",
    "\n",
    "tk_1000_latlon_afternoon = numpy.nanmean(tk_1000_latlon_mean[hour_indices,:], axis=0)\n",
    "td_1000_latlon_afternoon = numpy.nanmean(td_1000_latlon_mean[hour_indices,:], axis=0)\n",
    "\n",
    "ptot_indices = (ptot_latlon_subset_timemean/100.<=ptot_hi)&(ptot_latlon_subset_timemean/100.>=ptot_lo)\n",
    "\n",
    "cape_400 = metpy.calc.surface_based_cape_cin(ptot_latlon_subset_timemean[ptot_indices]/100.*metpy.units.units.hPa, \\\n",
    "                                  (tk_400_latlon_afternoon[ptot_indices]-273.15)*metpy.units.units.degC, \\\n",
    "                                  (td_400_latlon_afternoon[ptot_indices]-273.15)*metpy.units.units.degC)\n",
    "\n",
    "cape_1000 = metpy.calc.surface_based_cape_cin(ptot_latlon_subset_timemean[ptot_indices]/100.*metpy.units.units.hPa, \\\n",
    "                                  (tk_1000_latlon_afternoon[ptot_indices]-273.15)*metpy.units.units.degC, \\\n",
    "                                  (td_1000_latlon_afternoon[ptot_indices]-273.15)*metpy.units.units.degC)\n",
    "\n",
    "print(cape_400)\n",
    "print(cape_1000)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T06:08:48.681527Z",
     "start_time": "2018-06-29T06:08:44.084183Z"
    }
   },
   "source": [
    "fontsize=12\n",
    "hour = 19\n",
    "\n",
    "skewt_plot = metpy.plots.SkewT()\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., tk_1000_latlon_afternoon-273.15, lw=3, label='phys. T$_{env}$', c=color_list[0])\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., tk_400_latlon_afternoon-273.15, lw=3, label='cont. T$_{env}$', ls='--', c=color_list[1])\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., td_1000_latlon_afternoon-273.15, lw=3, label='phys. T$_d$', c=color_list[2])\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., td_400_latlon_afternoon-273.15, lw=3, label='cont. T$_d$', ls='--', c=color_list[3])\n",
    "\n",
    "pprofile_400 = metpy.calc.parcel_profile(ptot_latlon_subset_timemean/100.*metpy.units.units.hPa, \\\n",
    "                                         (tk_400_latlon_afternoon[0]-273.15)*metpy.units.units.degC, \\\n",
    "                                         (td_400_latlon_afternoon[0]-273.15)*metpy.units.units.degC).to('degC')\n",
    "pprofile_1000 = metpy.calc.parcel_profile(ptot_latlon_subset_timemean/100.*metpy.units.units.hPa, \\\n",
    "                                         (tk_1000_latlon_afternoon[0]-273.15)*metpy.units.units.degC, \\\n",
    "                                         (td_1000_latlon_afternoon[0]-273.15)*metpy.units.units.degC).to('degC')\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., pprofile_1000, label='phys. profile', c='0.1', lw=3, ls='-')\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., pprofile_400, label='cont. profile', c='0.5', lw=3, ls='--')\n",
    "\n",
    "\n",
    "# fill in control and phys CAPE\n",
    "skewt_plot.shade_cape(ptot_latlon_subset_timemean/100.,\\\n",
    "                      tk_1000_latlon_afternoon-273.15,\\\n",
    "                      numpy.array(pprofile_1000),\\\n",
    "                      edgecolor='None',facecolor=matplotlib.cm.tab20c(8/20),alpha=0.666,\\\n",
    "                      label='phys. CAPE')\n",
    "\n",
    "skewt_plot.shade_cape(ptot_latlon_subset_timemean/100.,\\\n",
    "                      tk_400_latlon_afternoon-273.15,\\\n",
    "                      numpy.array(pprofile_400),\\\n",
    "                      edgecolor='None',facecolor=matplotlib.cm.tab20c(8/20),alpha=0.333,\\\n",
    "                      label='cont. CAPE')\n",
    "\n",
    "skewt_plot.ax.set_ylim(1013.25,600)\n",
    "skewt_plot.ax.set_xlim(10,30)\n",
    "\n",
    "fig = mp.gcf()\n",
    "fig.set_size_inches(4.45,4.25)\n",
    "fig.tight_layout()\n",
    "\n",
    "skewt_plot.ax.tick_params(labelsize=fontsize)\n",
    "skewt_plot.ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "skewt_plot.ax.set_xlabel('$\\degree$C', fontsize=fontsize)\n",
    "\n",
    "skewt_plot.plot_dry_adiabats(color='0.5',linewidth=1,linestyle='-')\n",
    "skewt_plot.plot_moist_adiabats(color='0.5',linestyle=':')\n",
    "skewt_plot.plot_mixing_lines(color='0.5',linestyle='--',lw=1)\n",
    "\n",
    "handles, labels = skewt_plot.ax.get_legend_handles_labels()\n",
    "skewt_plot.ax.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "                     bbox_to_anchor=(1.03,1.0), \\\n",
    "                     bbox_transform=skewt_plot.ax.transAxes, loc='upper left', \\\n",
    "                     borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "                     edgecolor='0', framealpha=0, borderpad=0.25, \\\n",
    "                     columnspacing=0.5, handletextpad=0.5, markerscale=1)\n",
    "\n",
    "skewt_plot.ax.text(s='skew-T diagram at 15:00 on days 2–10',\\\n",
    "                  x=0,y=1.02,\\\n",
    "                  transform=skewt_plot.ax.transAxes,\\\n",
    "                  ha='left',va='bottom', fontsize=fontsize)\n",
    "\n",
    "skewt_plot.ax.annotate(\"\",xy=(11.6,700), \\\n",
    "                       xycoords='data',\\\n",
    "                       xytext=(13.6, 700), \\\n",
    "                       textcoords='data',\\\n",
    "                       arrowprops=dict(facecolor='0.1',ec='None',width=2,headwidth=6,headlength=10))\n",
    "\n",
    "skewt_plot.ax.annotate(\"\",xy=(10.9,700), \\\n",
    "                       xycoords='data',\\\n",
    "                       xytext=(8.9, 700), \\\n",
    "                       textcoords='data',\\\n",
    "                       arrowprops=dict(facecolor='0.1',ec='None',width=2,headwidth=6,headlength=10))\n",
    "\n",
    "CAPE_CIN_text = 'CAPE$_{\\,cont}$ = '+'{:.0f}'.format(numpy.array(cape_400[0]))+' J kg$^{\\,-1}$\\n'\\\n",
    "                'CAPE$_{\\,phys}$ = '+'{:.0f}'.format(numpy.array(cape_1000[0]))+' J kg$^{\\,-1}$\\n'\\\n",
    "                'CIN$_{\\,cont}$ = '+'{:.0f}'.format(numpy.array(cape_400[1]))+' J kg$^{\\,-1}$\\n'\\\n",
    "                'CIN$_{\\,phys}$ = '+'{:.0f}'.format(numpy.array(cape_1000[1]))+' J kg$^{\\,-1}$'\n",
    "skewt_plot.ax.text(s=CAPE_CIN_text,x=1.02,y=0,ha='left',va='bottom',transform=skewt_plot.ax.transAxes,fontsize=fontsize)\n",
    "\n",
    "fig.savefig('manuscript_both_cape_skewT_hour'+str(hour-4)+'_zoom_ALL_DAYS_amazon_region.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-01T06:16:33.854303Z",
     "start_time": "2018-07-01T06:16:29.760498Z"
    }
   },
   "outputs": [],
   "source": [
    "fontsize=12\n",
    "\n",
    "skewt_plot = metpy.plots.SkewT()\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., tk_1000_latlon_afternoon-273.15, lw=3, label='phys. T$_{env}$', c=color_list[0])\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., tk_400_latlon_afternoon-273.15, lw=3, label='cont. T$_{env}$', ls='--', c=color_list[1])\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., td_1000_latlon_afternoon-273.15, lw=3, label='phys. T$_d$', c=color_list[2])\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., td_400_latlon_afternoon-273.15, lw=3, label='cont. T$_d$', ls='--', c=color_list[3])\n",
    "\n",
    "pprofile_400 = metpy.calc.parcel_profile(ptot_latlon_subset_timemean/100.*metpy.units.units.hPa, \\\n",
    "                                         (tk_400_latlon_afternoon[0]-273.15)*metpy.units.units.degC, \\\n",
    "                                         (td_400_latlon_afternoon[0]-273.15)*metpy.units.units.degC).to('degC')\n",
    "pprofile_1000 = metpy.calc.parcel_profile(ptot_latlon_subset_timemean/100.*metpy.units.units.hPa, \\\n",
    "                                         (tk_1000_latlon_afternoon[0]-273.15)*metpy.units.units.degC, \\\n",
    "                                         (td_1000_latlon_afternoon[0]-273.15)*metpy.units.units.degC).to('degC')\n",
    "\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., pprofile_1000, label='phys. profile', c='0.1', lw=3, ls='-')\n",
    "skewt_plot.plot(ptot_latlon_subset_timemean/100., pprofile_400, label='cont. profile', c='0.5', lw=3, ls='--')\n",
    "\n",
    "\n",
    "# fill in control and phys CAPE\n",
    "skewt_plot.shade_cape(ptot_latlon_subset_timemean/100.,\\\n",
    "                      tk_1000_latlon_afternoon-273.15,\\\n",
    "                      numpy.array(pprofile_1000),\\\n",
    "                      edgecolor='None',facecolor=matplotlib.cm.tab20c(8/20),alpha=0.666,\\\n",
    "                      label='phys. CAPE')\n",
    "\n",
    "skewt_plot.shade_cape(ptot_latlon_subset_timemean/100.,\\\n",
    "                      tk_400_latlon_afternoon-273.15,\\\n",
    "                      numpy.array(pprofile_400),\\\n",
    "                      edgecolor='None',facecolor=matplotlib.cm.tab20c(8/20),alpha=0.333,\\\n",
    "                      label='cont. CAPE')\n",
    "\n",
    "skewt_plot.ax.set_ylim(800.,500.)\n",
    "skewt_plot.ax.set_xlim(0,17)\n",
    "\n",
    "fig = mp.gcf()\n",
    "fig.set_size_inches(4.45,4.25)\n",
    "fig.tight_layout()\n",
    "\n",
    "skewt_plot.ax.tick_params(labelsize=fontsize)\n",
    "skewt_plot.ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "skewt_plot.ax.set_xlabel('$\\degree$C', fontsize=fontsize)\n",
    "\n",
    "skewt_plot.plot_dry_adiabats(color='0.5',linewidth=1,linestyle='-')\n",
    "skewt_plot.plot_moist_adiabats(color='0.5',linestyle=':')\n",
    "skewt_plot.plot_mixing_lines(color='0.5',linestyle='--',lw=1)\n",
    "\n",
    "handles, labels = skewt_plot.ax.get_legend_handles_labels()\n",
    "skewt_plot.ax.legend(handles, labels, fontsize=fontsize, scatterpoints=1, \\\n",
    "                     bbox_to_anchor=(1.03,1.0), \\\n",
    "                     bbox_transform=skewt_plot.ax.transAxes, loc='upper left', \\\n",
    "                     borderaxespad=0, labelspacing=0.25, fancybox=False, \\\n",
    "                     edgecolor='0', framealpha=0, borderpad=0.25, \\\n",
    "                     columnspacing=0.5, handletextpad=0.5, markerscale=1)\n",
    "\n",
    "skewt_plot.ax.text(s='skew-T diagram at 15:00 on days 2–10',\\\n",
    "                  x=0,y=1.02,\\\n",
    "                  transform=skewt_plot.ax.transAxes,\\\n",
    "                  ha='left',va='bottom', fontsize=fontsize)\n",
    "\n",
    "# skewt_plot.ax.annotate(\"\",xy=(11.6,700), \\\n",
    "#                        xycoords='data',\\\n",
    "#                        xytext=(13.6, 700), \\\n",
    "#                        textcoords='data',\\\n",
    "#                        arrowprops=dict(facecolor='0.1',ec='None',width=2,headwidth=6,headlength=10))\n",
    "\n",
    "# skewt_plot.ax.annotate(\"\",xy=(10.9,700), \\\n",
    "#                        xycoords='data',\\\n",
    "#                        xytext=(8.9, 700), \\\n",
    "#                        textcoords='data',\\\n",
    "#                        arrowprops=dict(facecolor='0.1',ec='None',width=2,headwidth=6,headlength=10))\n",
    "\n",
    "CAPE_CIN_text = 'CAPE$_{\\,cont}$ = '+'{:.0f}'.format(numpy.array(cape_400[0]))+' J kg$^{\\,-1}$\\n'\\\n",
    "                'CAPE$_{\\,phys}$ = '+'{:.0f}'.format(numpy.array(cape_1000[0]))+' J kg$^{\\,-1}$\\n'\\\n",
    "                'CIN$_{\\,cont}$ = '+'{:.0f}'.format(numpy.array(cape_400[1]))+' J kg$^{\\,-1}$\\n'\\\n",
    "                'CIN$_{\\,phys}$ = '+'{:.0f}'.format(numpy.array(cape_1000[1]))+' J kg$^{\\,-1}$'\n",
    "skewt_plot.ax.text(s=CAPE_CIN_text,x=1.02,y=0,ha='left',va='bottom',transform=skewt_plot.ax.transAxes,fontsize=fontsize)\n",
    "\n",
    "fig.savefig('manuscript_both_cape_skewT_hour'+str(hour)+'_zoom_ALL_DAYS_andes_region.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot cape variables in 4x4 plot for first day and 2-10 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T02:09:56.378600Z",
     "start_time": "2018-06-27T02:09:50.833702Z"
    }
   },
   "outputs": [],
   "source": [
    "fig = mp.figure(figsize=(5.333,5.666))\n",
    "\n",
    "fontsize=12\n",
    "lon_offset=25.\n",
    "\n",
    "map_proj = cartopy.crs.PlateCarree()\n",
    "\n",
    "ax1 = fig.add_subplot(221, projection=map_proj)\n",
    "ax2 = fig.add_subplot(222, projection=map_proj)\n",
    "ax3 = fig.add_subplot(223, projection=map_proj)\n",
    "ax4 = fig.add_subplot(224, projection=map_proj)\n",
    "\n",
    "cp1 = ax1.pcolormesh(xlong,xlat,\\\n",
    "                     cape2d_mcape_diffs[:24,:,:].mean(axis=0),\\\n",
    "                     cmap='PuOr_r', vmin=-150,vmax=150)\n",
    "\n",
    "cp2 = ax2.pcolormesh(xlong,xlat,\\\n",
    "                     cape2d_mcin_diffs[:24,:,:].mean(axis=0),\\\n",
    "                     cmap='PuOr', vmin=-5,vmax=5)\n",
    "\n",
    "cp3 = ax3.pcolormesh(xlong,xlat,\\\n",
    "                     cape2d_lcl_diffs[:24,:,:].mean(axis=0),\\\n",
    "                     cmap=cmocean.cm.amp, vmin=0,vmax=150)\n",
    "\n",
    "cp4 = ax4.pcolormesh(xlong,xlat,\\\n",
    "                     cape2d_lfc_diffs[:24,:,:].mean(axis=0),\\\n",
    "                     cmap=cmocean.cm.amp, vmin=0,vmax=150)\n",
    "\n",
    "ax1.coastlines()\n",
    "ax2.coastlines()\n",
    "ax3.coastlines()\n",
    "ax4.coastlines()\n",
    "\n",
    "ax1.set_extent([273,329,-30,15], crs=map_proj)\n",
    "ax2.set_extent([273,329,-30,15], crs=map_proj)\n",
    "ax3.set_extent([273,329,-30,15], crs=map_proj)\n",
    "ax4.set_extent([273,329,-30,15], crs=map_proj)\n",
    "\n",
    "ax1.set_xticks([-360+280,-360+300,-360+320])\n",
    "ax1.set_yticks([-20,-10,0,10])\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "ax1.set_xticklabels(['$280$'+DEGREE_SIGN,'$300$'+DEGREE_SIGN,'$320$'+DEGREE_SIGN])\n",
    "ax1.set_yticklabels(['$-20$'+DEGREE_SIGN,'$-10$'+DEGREE_SIGN,'$0$'+DEGREE_SIGN,'$10$'+DEGREE_SIGN])\n",
    "\n",
    "ax2.set_xticks([-360+280,-360+300,-360+320])\n",
    "ax2.set_yticks([-20,-10,0,10])\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "ax2.set_xticklabels(['$280$'+DEGREE_SIGN,'$300$'+DEGREE_SIGN,'$320$'+DEGREE_SIGN])\n",
    "ax2.set_yticklabels(['$-20$'+DEGREE_SIGN,'$-10$'+DEGREE_SIGN,'$0$'+DEGREE_SIGN,'$10$'+DEGREE_SIGN])\n",
    "\n",
    "ax3.set_xticks([-360+280,-360+300,-360+320])\n",
    "ax3.set_yticks([-20,-10,0,10])\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "ax3.set_xticklabels(['$280$'+DEGREE_SIGN,'$300$'+DEGREE_SIGN,'$320$'+DEGREE_SIGN])\n",
    "ax3.set_yticklabels(['$-20$'+DEGREE_SIGN,'$-10$'+DEGREE_SIGN,'$0$'+DEGREE_SIGN,'$10$'+DEGREE_SIGN])\n",
    "\n",
    "ax4.set_xticks([-360+280,-360+300,-360+320])\n",
    "ax4.set_yticks([-20,-10,0,10])\n",
    "ax4.tick_params(labelsize=fontsize)\n",
    "ax4.set_xticklabels(['$280$'+DEGREE_SIGN,'$300$'+DEGREE_SIGN,'$320$'+DEGREE_SIGN])\n",
    "ax4.set_yticklabels(['$-20$'+DEGREE_SIGN,'$-10$'+DEGREE_SIGN,'$0$'+DEGREE_SIGN,'$10$'+DEGREE_SIGN])\n",
    "\n",
    "title = ax1.text(s='(a) $\\Delta$ CAPE', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "title = ax2.text(s='(b) $\\Delta$ CIN', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "title = ax3.text(s='(c) $\\Delta$ LCL', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "title = ax4.text(s='(d) $\\Delta$ LFC', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax4.transAxes)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "cbar_thickness=0.025\n",
    "cbar_yshift=0#-0.08\n",
    "\n",
    "bbox_position = ax1.get_position()\n",
    "cbar_axes = fig.add_axes([bbox_position.x0, bbox_position.y0+cbar_yshift, bbox_position.width, cbar_thickness])\n",
    "cbar = mp.colorbar(cp1, orientation='horizontal', cax=cbar_axes)\n",
    "#cbar.set_label(label='mm day$^{-1}$', fontsize=fontsize)\n",
    "cbar_axes.tick_params(labelsize=fontsize)\n",
    "cbar.set_ticks([-150,0,150])\n",
    "\n",
    "bbox_position = ax2.get_position()\n",
    "cbar_axes = fig.add_axes([bbox_position.x0, bbox_position.y0+cbar_yshift, bbox_position.width, cbar_thickness])\n",
    "cbar = mp.colorbar(cp2, orientation='horizontal', cax=cbar_axes)\n",
    "#cbar.set_label(label='mm day$^{-1}$', fontsize=fontsize)\n",
    "cbar_axes.tick_params(labelsize=fontsize)\n",
    "cbar.set_ticks([-5,-2.5,0,2.5,5])\n",
    "\n",
    "bbox_position = ax3.get_position()\n",
    "cbar_axes = fig.add_axes([bbox_position.x0, bbox_position.y0+cbar_yshift, bbox_position.width, cbar_thickness])\n",
    "cbar = mp.colorbar(cp3, orientation='horizontal', cax=cbar_axes)\n",
    "#cbar.set_label(label='W m$^{-2}$', fontsize=fontsize)\n",
    "cbar_axes.tick_params(labelsize=fontsize)\n",
    "cbar.set_ticks([0,50,100,150])\n",
    "\n",
    "bbox_position = ax4.get_position()\n",
    "cbar_axes = fig.add_axes([bbox_position.x0, bbox_position.y0+cbar_yshift, bbox_position.width, cbar_thickness])\n",
    "cbar = mp.colorbar(cp4, orientation='horizontal', cax=cbar_axes)\n",
    "#cbar.set_label(label=degree_sign+'C', fontsize=fontsize)\n",
    "cbar_axes.tick_params(labelsize=fontsize)\n",
    "cbar.set_ticks([0,50,100,150])\n",
    "\n",
    "ax1.text(s='J kg$^{\\,-1}$', x=0.95, y=0.95, \\\n",
    "        ha='right', va='top', \\\n",
    "        fontsize=fontsize, \\\n",
    "        transform=ax1.transAxes)\n",
    "ax2.text(s='J kg$^{\\,-1}$', x=0.95, y=0.95, \\\n",
    "        ha='right', va='top', \\\n",
    "        fontsize=fontsize, \\\n",
    "        transform=ax2.transAxes)\n",
    "ax3.text(s='m', x=0.95, y=0.95, \\\n",
    "        ha='right', va='top', \\\n",
    "        fontsize=fontsize, \\\n",
    "        transform=ax3.transAxes)\n",
    "ax4.text(s='m', x=0.95, y=0.93, \\\n",
    "        ha='right', va='top', \\\n",
    "        fontsize=fontsize, \\\n",
    "        transform=ax4.transAxes)\n",
    "\n",
    "#fig.savefig('cape_cin_lcl_lfc_days2to10.png', bbox_inches='tight', dpi=600, )\n",
    "fig.savefig('cape_cin_lcl_lfc_day1.png', bbox_inches='tight', dpi=600, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T02:09:56.415037Z",
     "start_time": "2018-06-27T02:09:56.386936Z"
    }
   },
   "outputs": [],
   "source": [
    "net_atm_diffs_1000_sw_latlon_subset = numpy.nanmean(net_atm_diffs_1000_sw[:,latlon_indices], axis=1)\n",
    "net_atm_diffs_400_sw_latlon_subset = numpy.nanmean(net_atm_diffs_400_sw[:,latlon_indices], axis=1)\n",
    "\n",
    "net_atm_diffs_1000_lw_latlon_subset = numpy.nanmean(net_atm_diffs_1000_lw[:,latlon_indices], axis=1)\n",
    "net_atm_diffs_400_lw_latlon_subset = numpy.nanmean(net_atm_diffs_400_lw[:,latlon_indices], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T02:10:00.416223Z",
     "start_time": "2018-06-27T02:10:00.392164Z"
    }
   },
   "outputs": [],
   "source": [
    "net_atm_diffs_1000_swc_latlon_subset = numpy.nanmean(net_atm_diffs_1000_swc[:,latlon_indices], axis=1)\n",
    "net_atm_diffs_400_swc_latlon_subset = numpy.nanmean(net_atm_diffs_400_swc[:,latlon_indices], axis=1)\n",
    "\n",
    "net_atm_diffs_1000_lwc_latlon_subset = numpy.nanmean(net_atm_diffs_1000_lwc[:,latlon_indices], axis=1)\n",
    "net_atm_diffs_400_lwc_latlon_subset = numpy.nanmean(net_atm_diffs_400_lwc[:,latlon_indices], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T02:10:02.462579Z",
     "start_time": "2018-06-27T02:10:02.458023Z"
    }
   },
   "outputs": [],
   "source": [
    "xvals = numpy.arange(net_atm_diffs_400_sw_latlon_subset.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T18:47:06.591858Z",
     "start_time": "2018-06-27T18:47:01.822942Z"
    }
   },
   "outputs": [],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig,axl = mp.subplots(3,2)\n",
    "fig.set_size_inches(8.5,5)\n",
    "\n",
    "axl[0,0].plot(xvals-4,net_atm_diffs_400_sw_latlon_subset/(60*60),c='firebrick')\n",
    "axl[1,0].plot(xvals-4,net_atm_diffs_400_lw_latlon_subset/(60*60),c='firebrick')\n",
    "#axl[1,0].plot(xvals-4,net_atm_diffs_1000_lw_latlon_subset/(60*60),c='coral')\n",
    "\n",
    "axl[2,0].plot(xvals-4,(net_atm_diffs_400_sw_latlon_subset+net_atm_diffs_400_lw_latlon_subset)/(60*60),c='firebrick')\n",
    "\n",
    "axl[0,1].plot(xvals-4,(net_atm_diffs_1000_sw_latlon_subset-net_atm_diffs_400_sw_latlon_subset)/(60*60),c='firebrick')\n",
    "axl[1,1].plot(xvals-4,(net_atm_diffs_1000_lw_latlon_subset-net_atm_diffs_400_lw_latlon_subset)/(60*60),c='firebrick')\n",
    "axl[2,1].plot(xvals-4,\\\n",
    "              ((net_atm_diffs_1000_sw_latlon_subset+net_atm_diffs_1000_lw_latlon_subset)-\\\n",
    "               (net_atm_diffs_400_sw_latlon_subset+net_atm_diffs_400_lw_latlon_subset))/(60*60),c='firebrick')\n",
    "\n",
    "axl[0,0].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "#axl[1,0].axhline(0, lw=1, c='0.1', ls='--')\n",
    "axl[2,0].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "axl[0,1].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "axl[1,1].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "axl[2,1].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "\n",
    "axl[0,0].set_ylim(0,400)\n",
    "axl[1,0].set_ylim(-300,-150)\n",
    "axl[2,0].set_ylim(-400,200)\n",
    "axl[0,1].set_ylim(-20,10)\n",
    "axl[1,1].set_ylim(-10,10)\n",
    "axl[2,1].set_ylim(-5,5)\n",
    "\n",
    "[ax.tick_params(labelsize=fontsize) for ax in axl.flatten()]\n",
    "[ax.set_xlim(0,240) for ax in axl.flatten()]\n",
    "[ax.set_ylabel('W m$^{\\,-2}$', fontsize=fontsize) for ax in axl.flatten()[0::2]]\n",
    "[ax.set_xlabel('hour', fontsize=fontsize) for ax in axl.flatten()[-2:]]\n",
    "[ax.get_yaxis().set_label_coords(-0.15,0.5) for ax in axl.flatten()]\n",
    "[ax.set_xticks(numpy.arange(0,241,24)) for ax in axl.flatten()]\n",
    "[ax.xaxis.grid() for ax in axl.flatten()]\n",
    "# [ax.set_axisbelow(True) for ax in axl.flatten()]\n",
    "\n",
    "plot_labels = [\\\n",
    "'net atmos. shortwave (control)',\\\n",
    "'phys.– cont. net atmos. shortwave',\\\n",
    "'net atmos. longwave (control)',\\\n",
    "'phys.– cont. net atmos. longwave',\\\n",
    "'net atmos. rad. (control)',\\\n",
    "'phys.– cont. net atmos. rad.']\n",
    "\n",
    "[axl.flatten()[i].text(s=plot_labels[i], ha='left',\\\n",
    "        va='bottom',\\\n",
    "        x=0,y=1.03,\\\n",
    "        transform=axl.flatten()[i].transAxes,\\\n",
    "        fontsize=fontsize)\\\n",
    "for i in range(axl.flatten().size)]\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "fig.savefig('radiation_changes_with_control.png',\\\n",
    "           dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T18:47:14.461318Z",
     "start_time": "2018-06-27T18:47:09.696742Z"
    }
   },
   "outputs": [],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig,axl = mp.subplots(3,2)\n",
    "fig.set_size_inches(8.5,5)\n",
    "\n",
    "axl[0,0].plot(xvals-4,net_atm_diffs_400_sw_latlon_subset/(60*60),c='firebrick')\n",
    "axl[1,0].plot(xvals-4,net_atm_diffs_400_lw_latlon_subset/(60*60),c='firebrick')\n",
    "#axl[1,0].plot(xvals-4,net_atm_diffs_1000_lw_latlon_subset/(60*60),c='coral')\n",
    "\n",
    "axl[2,0].plot(xvals-4,(net_atm_diffs_400_sw_latlon_subset+net_atm_diffs_400_lw_latlon_subset)/(60*60),c='firebrick')\n",
    "\n",
    "axl[0,1].plot(xvals-4,(net_atm_diffs_1000_sw_latlon_subset-net_atm_diffs_400_sw_latlon_subset)/(60*60),c='firebrick')\n",
    "axl[1,1].plot(xvals-4,(net_atm_diffs_1000_lw_latlon_subset-net_atm_diffs_400_lw_latlon_subset)/(60*60),c='firebrick')\n",
    "axl[2,1].plot(xvals-4,((net_atm_diffs_1000_sw_latlon_subset+net_atm_diffs_1000_lw_latlon_subset)-(net_atm_diffs_400_sw_latlon_subset+net_atm_diffs_400_lw_latlon_subset))/(60*60),c='firebrick')\n",
    "\n",
    "axl[0,0].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "#axl[1,0].axhline(0, lw=1, c='0.1', ls='--')\n",
    "axl[2,0].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "axl[0,1].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "axl[1,1].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "axl[2,1].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "\n",
    "axl[0,0].set_ylim(0,400)\n",
    "axl[1,0].set_ylim(-300,-150)\n",
    "axl[2,0].set_ylim(-400,200)\n",
    "axl[0,1].set_ylim(-20,10)\n",
    "axl[1,1].set_ylim(-10,10)\n",
    "axl[2,1].set_ylim(-5,5)\n",
    "\n",
    "[ax.tick_params(labelsize=fontsize) for ax in axl.flatten()]\n",
    "[ax.set_xlim(0,240) for ax in axl.flatten()]\n",
    "[ax.set_ylabel('W m$^{\\,-2}$', fontsize=fontsize) for ax in axl.flatten()[0::2]]\n",
    "[ax.set_xlabel('hour', fontsize=fontsize) for ax in axl.flatten()[-2:]]\n",
    "[ax.get_yaxis().set_label_coords(-0.15,0.5) for ax in axl.flatten()]\n",
    "[ax.set_xticks(numpy.arange(0,241,24)) for ax in axl.flatten()]\n",
    "[ax.xaxis.grid() for ax in axl.flatten()]\n",
    "# [ax.set_axisbelow(True) for ax in axl.flatten()]\n",
    "\n",
    "plot_labels = [\\\n",
    "'net atmos. shortwave (control)',\\\n",
    "'phys.– cont. net atmos. shortwave',\\\n",
    "'net atmos. longwave (control)',\\\n",
    "'phys.– cont. net atmos. longwave',\\\n",
    "'phys.– cont. net atmos. rad.',\\\n",
    "'net atmos. rad. (control)' ]\n",
    "\n",
    "[axl.flatten()[i].text(s=plot_labels[i], ha='left',\\\n",
    "        va='bottom',\\\n",
    "        x=0,y=1.03,\\\n",
    "        transform=axl.flatten()[i].transAxes,\\\n",
    "        fontsize=fontsize)\\\n",
    "for i in range(axl.flatten().size)]\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "fig.savefig('radiation_changes_with_control.png',\\\n",
    "           dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate cloud fraction changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T18:53:11.109677Z",
     "start_time": "2018-06-27T18:47:20.673241Z"
    }
   },
   "outputs": [],
   "source": [
    "cldfra_diffs = cldfra_1000-cldfra_400\n",
    "cldfra_diffs_latlon_subset = numpy.mean(cldfra_diffs.values[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T18:54:22.436508Z",
     "start_time": "2018-06-27T18:54:22.416940Z"
    }
   },
   "outputs": [],
   "source": [
    "cape2d_diffs_latlon_mean = numpy.nanmean(cape2d_diffs[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T19:07:40.729908Z",
     "start_time": "2018-06-27T19:07:40.287937Z"
    }
   },
   "outputs": [],
   "source": [
    "cape3d_400_latlon_mean = numpy.nanmean(cape3d_400[:,:,:,latlon_indices], axis=3)\n",
    "cape3d_1000_latlon_mean = numpy.nanmean(cape3d_1000[:,:,:,latlon_indices], axis=3)\n",
    "\n",
    "cape3d_diffs_latlon_mean = cape3d_1000_latlon_mean - cape3d_400_latlon_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot cloud changes and radiative changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T18:53:18.953639Z",
     "start_time": "2018-06-27T18:53:14.413810Z"
    }
   },
   "outputs": [],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig,axes = mp.subplots(5,1)\n",
    "fig.set_size_inches(4.25,8)\n",
    "\n",
    "axes[0].plot(xvals-4,(net_atm_diffs_1000_sw_latlon_subset-net_atm_diffs_400_sw_latlon_subset)/(60*60),c='firebrick')\n",
    "axes[1].plot(xvals-4,(net_atm_diffs_1000_lw_latlon_subset-net_atm_diffs_400_lw_latlon_subset)/(60*60),c='firebrick')\n",
    "axes[2].plot(xvals-4,\\\n",
    "            ((net_atm_diffs_1000_sw_latlon_subset+net_atm_diffs_1000_lw_latlon_subset)-\\\n",
    "             (net_atm_diffs_400_sw_latlon_subset+net_atm_diffs_400_lw_latlon_subset))/(60*60),c='firebrick')\n",
    "\n",
    "levels = numpy.arange(-.1,.11,0.02)*100.\n",
    "contour_plot = axes[3].contourf(numpy.arange(241)-4,\\\n",
    "                                ptot_latlon_subset_timemean/100.,\\\n",
    "                                cldfra_diffs_latlon_subset.T*100.,\\\n",
    "                                levels=levels, extend='both',\\\n",
    "                                cmap=cmocean.cm.balance_r)\n",
    "\n",
    "levels = numpy.arange(-.25,.251,0.05)\n",
    "contour_plot2 = axes[4].pcolormesh(numpy.arange(241)-4,\\\n",
    "                                ptot_latlon_subset_timemean/100.,\\\n",
    "                                cape3d_cape_diffs_latlon_mean.T,\\\n",
    "                                vmin=-200,vmax=200,\\\n",
    "                                #levels=levels, extend='both',\\\n",
    "                                cmap='PuOr')\n",
    "axes[3].set_ylim(1013.25,700)\n",
    "axes[4].set_ylim(1013.25,700)\n",
    "\n",
    "axes[0].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "axes[1].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "axes[2].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "\n",
    "axes[1].set_ylim(-10,10)\n",
    "axes[2].set_ylim(-5,5)\n",
    "\n",
    "[ax.tick_params(labelsize=fontsize) for ax in axes.flatten()]\n",
    "[ax.set_xlim(0,240) for ax in axes.flatten()]\n",
    "[ax.set_ylabel('W m$^{\\,-2}$', fontsize=fontsize) for ax in axes.flatten()[0:3]]\n",
    "[ax.set_xlabel('hour', fontsize=fontsize) for ax in [axes[-1]]]\n",
    "[ax.get_yaxis().set_label_coords(-0.15,0.5) for ax in axes.flatten()]\n",
    "[ax.xaxis.grid() for ax in axes.flatten()]\n",
    "\n",
    "axes[3].set_ylabel('hPa', fontsize=fontsize)\n",
    "axes[3].set_yticks([700,800,900,1000])\n",
    "\n",
    "axes[4].set_ylabel('hPa', fontsize=fontsize)\n",
    "axes[4].set_yticks([700,800,900,1000])\n",
    "\n",
    "[ax.set_xticks(numpy.arange(0,241,24)) for ax in axes.flatten()]\n",
    "[ax.set_xticklabels([]) for ax in axes[0:4]]\n",
    "# [ax.set_axisbelow(True) for ax in axes.flatten()]\n",
    "\n",
    "plot_labels = [\\\n",
    "'(a) $\\Delta$ net atmos. shortwave',\\\n",
    "'(b) $\\Delta$ net atmos. longwave',\\\n",
    "'(c) $\\Delta$ net atmos. rad.',\\\n",
    "'(d) $\\Delta$ cloud fraction', \\\n",
    "'(e) $\\Delta$ CAPE']\n",
    "\n",
    "[axes.flatten()[i].text(s=plot_labels[i], ha='left',\\\n",
    "        va='bottom',\\\n",
    "        x=0,y=1.03,\\\n",
    "        transform=axes.flatten()[i].transAxes,\\\n",
    "        fontsize=fontsize)\\\n",
    "for i in range(axes.flatten().size)]\n",
    "\n",
    "fig.tight_layout(h_pad=1.5)\n",
    "\n",
    "ax4pos = axes[3].get_position()\n",
    "cbar_ax = fig.add_axes([ax4pos.x1+0.025,ax4pos.y0,0.025,ax4pos.height])\n",
    "cbar = fig.colorbar(contour_plot, cax=cbar_ax)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_ticks([-10,-5,0,5,10])\n",
    "cbar.set_label('%', fontsize=fontsize)\n",
    "\n",
    "ax5pos = axes[4].get_position()\n",
    "cbar_ax = fig.add_axes([ax5pos.x1+0.025,ax5pos.y0,0.025,ax5pos.height])\n",
    "cbar = fig.colorbar(contour_plot2, cax=cbar_ax)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_ticks([-200,-100,0,100,200])\n",
    "cbar.set_label('kJ kg$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('radiation_cloud_cape_changes.png',\\\n",
    "           dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T00:46:57.411845Z",
     "start_time": "2018-06-27T00:46:57.391487Z"
    }
   },
   "source": [
    "# plot cape and cin background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T19:28:45.799792Z",
     "start_time": "2018-06-27T19:28:40.980482Z"
    }
   },
   "outputs": [],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig,axes = mp.subplots(2,2)\n",
    "fig.set_size_inches(8.5,4)\n",
    "\n",
    "cp1 = axes[0,0].pcolormesh(numpy.arange(241)-4,\\\n",
    "                         ptot_latlon_subset_timemean/100.,\\\n",
    "                         cape3d_400_latlon_mean[0,:].T,\\\n",
    "                         vmin=0,vmax=1500,\\\n",
    "                         #levels=levels, extend='both',\\\n",
    "                         cmap=cmocean.cm.amp)\n",
    "axes[0,0].set_ylim(1013.25,700)\n",
    "\n",
    "cp2 = axes[0,1].pcolormesh(numpy.arange(241)-4,\\\n",
    "                         ptot_latlon_subset_timemean/100.,\\\n",
    "                         cape3d_diffs_latlon_mean[0,:].T,\\\n",
    "                         vmin=-200,vmax=200,\\\n",
    "                         cmap=cmocean.cm.balance)\n",
    "axes[0,1].set_ylim(1013.25,700)\n",
    "\n",
    "\n",
    "cp3 = axes[1,0].pcolormesh(numpy.arange(241)-4,\\\n",
    "                         ptot_latlon_subset_timemean/100.,\\\n",
    "                         cape3d_400_latlon_mean[1,:].T,\\\n",
    "                         vmin=0,vmax=20,\\\n",
    "                         #levels=levels, extend='both',\\\n",
    "                         cmap=cmocean.cm.deep)\n",
    "axes[1,0].set_ylim(1013.25,700)\n",
    "\n",
    "\n",
    "\n",
    "cp4 = axes[1,1].pcolormesh(numpy.arange(241)-4,\\\n",
    "                         ptot_latlon_subset_timemean/100.,\\\n",
    "                         cape3d_diffs_latlon_mean[1,:].T,\\\n",
    "                         vmin=-10,vmax=10,\\\n",
    "                         cmap=cmocean.cm.balance_r)\n",
    "axes[1,1].set_ylim(1013.25,700)\n",
    "\n",
    "[ax.tick_params(labelsize=fontsize) for ax in axes.flatten()]\n",
    "[ax.set_xlim(0,240) for ax in axes.flatten()]\n",
    "[ax.set_xlabel('hour', fontsize=fontsize) for ax in [axes[1,0],axes[1,1]]]\n",
    "#[ax.get_yaxis().set_label_coords(-0.15,0.5) for ax in axes.flatten()]\n",
    "[ax.xaxis.grid() for ax in axes.flatten()]\n",
    "[ax.set_xticks(numpy.arange(0,241,24)) for ax in axes.flatten()]\n",
    "[ax.set_xticklabels([]) for ax in axes.flatten()[0:2]]\n",
    "[ax.set_ylabel('hPa', fontsize=fontsize) for ax in axes.flatten()[0::2]]\n",
    "[ax.set_yticks([700,800,900,1000]) for ax in axes.flatten()]\n",
    "\n",
    "fig.tight_layout(h_pad=3,w_pad=5)\n",
    "\n",
    "plot_labels = [\\\n",
    "'(a) background CAPE (J kg$^{\\,-1}$)',\\\n",
    "'(b) phys.– cont. CAPE (J kg$^{\\,-1}$)',\\\n",
    "'(c) background CIN (J kg$^{\\,-1}$)',\\\n",
    "'(d) phys.– cont. CIN (J kg$^{\\,-1}$)']\n",
    "\n",
    "[axes.flatten()[i].text(s=plot_labels[i], ha='left',\\\n",
    "        va='bottom',\\\n",
    "        x=0,y=1.02,\\\n",
    "        transform=axes.flatten()[i].transAxes,\\\n",
    "        fontsize=fontsize)\\\n",
    "for i in range(axes.flatten().size)]\n",
    "\n",
    "cbar_list = [cp1,cp2,cp3,cp4]\n",
    "pos_list = [ax.get_position() for ax in axes.flatten()]\n",
    "cbar_ax_list = [fig.add_axes([axpos.x1+0.02,axpos.y0,0.02,axpos.height]) for axpos in pos_list]\n",
    "#cbar_ax_list += [fig.add_axes([axpos.x0,axpos.y0-.1,axpos.width,0.025]) for axpos in pos_list[2:4]]\n",
    "\n",
    "cbar_list = [fig.colorbar(cbar_list[i], cax=cbar_ax_list[i], orientation='vertical') for i in range(4)]\n",
    "[cax.tick_params(labelsize=fontsize) for cax in cbar_ax_list]\n",
    "\n",
    "fig.savefig('background_and_change_CAPE_CIN.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T19:20:34.509275Z",
     "start_time": "2018-06-27T19:20:34.499228Z"
    }
   },
   "outputs": [],
   "source": [
    "pos_list[0::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-27T00:53:16.852206Z",
     "start_time": "2018-06-27T00:53:14.099018Z"
    }
   },
   "outputs": [],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig,axes = mp.subplots(3,1)\n",
    "fig.set_size_inches(4.25,5)\n",
    "\n",
    "levels = numpy.arange(-.1,.11,0.02)*100.\n",
    "contour_plot = axes[0].contourf(numpy.arange(241)-4,\\\n",
    "                                ptot_latlon_subset_timemean/100.,\\\n",
    "                                cldfra_diffs_latlon_subset.T*100.,\\\n",
    "                                levels=levels, extend='both',\\\n",
    "                                cmap=cmocean.cm.balance_r)\n",
    "\n",
    "axes[1].plot(numpy.arange(241)-4,\\\n",
    "             cape2d_diffs_latlon_mean[0,:], c='firebrick')\n",
    "\n",
    "axes[2].plot(numpy.arange(241)-4,\\\n",
    "             cape2d_diffs_latlon_mean[1,:], c='firebrick')\n",
    "# levels = numpy.arange(-.25,.251,0.05)\n",
    "# contour_plot2 = axes[1].pcolormesh(numpy.arange(241)-4,\\\n",
    "#                                 ptot_latlon_subset_timemean/100.,\\\n",
    "#                                 cape3d_cape_diffs_latlon_mean.T,\\\n",
    "#                                 vmin=-200,vmax=200,\\\n",
    "#                                 #levels=levels, extend='both',\\\n",
    "#                                 cmap='PuOr')\n",
    "axes[0].set_ylim(1013.25,700)\n",
    "\n",
    "axes[0].set_ylabel('W m$^{\\,-2}$', fontsize=fontsize)\n",
    "axes[1].set_ylabel('J kg$^{\\,-1}$', fontsize=fontsize)\n",
    "axes[2].set_ylabel('J kg$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "axes[1].axhline(0, lw='1', c='0.1', ls='--')\n",
    "axes[2].axhline(0, lw='1', c='0.1', ls='--')\n",
    "\n",
    "[ax.tick_params(labelsize=fontsize) for ax in axes.flatten()]\n",
    "[ax.set_xlim(0,240) for ax in axes.flatten()]\n",
    "[ax.set_xlabel('hour', fontsize=fontsize) for ax in [axes[-1]]]\n",
    "[ax.get_yaxis().set_label_coords(-0.15,0.5) for ax in axes.flatten()]\n",
    "[ax.xaxis.grid() for ax in axes.flatten()]\n",
    "\n",
    "axes[0].set_ylabel('hPa', fontsize=fontsize)\n",
    "axes[0].set_yticks([700,800,900,1000])\n",
    "\n",
    "# axes[1].set_ylabel('hPa', fontsize=fontsize)\n",
    "# axes[1].set_yticks([700,800,900,1000])\n",
    "\n",
    "[ax.set_xticks(numpy.arange(0,241,24)) for ax in axes.flatten()]\n",
    "[ax.set_xticklabels([]) for ax in axes[0:2]]\n",
    "# [ax.set_axisbelow(True) for ax in axes.flatten()]\n",
    "\n",
    "plot_labels = [\\\n",
    "'(a) $\\Delta$ cloud fraction', \\\n",
    "'(b) $\\Delta$ CAPE', \\\n",
    "'(c) $\\Delta$ CIN']\n",
    "\n",
    "[axes.flatten()[i].text(s=plot_labels[i], ha='left',\\\n",
    "        va='bottom',\\\n",
    "        x=0,y=1.03,\\\n",
    "        transform=axes.flatten()[i].transAxes,\\\n",
    "        fontsize=fontsize)\\\n",
    "for i in range(axes.flatten().size)]\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1pos = axes[0].get_position()\n",
    "cbar_ax = fig.add_axes([ax1pos.x1+0.025,ax1pos.y0,0.025,ax1pos.height])\n",
    "cbar = fig.colorbar(contour_plot, cax=cbar_ax)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_ticks([-10,-5,0,5,10])\n",
    "cbar.set_label('%', fontsize=fontsize)\n",
    "\n",
    "# ax2pos = axes[1].get_position()\n",
    "# cbar_ax = fig.add_axes([ax2pos.x1+0.025,ax2pos.y0,0.025,ax2pos.height])\n",
    "# cbar = fig.colorbar(contour_plot2, cax=cbar_ax)\n",
    "# cbar_ax.tick_params(labelsize=fontsize)\n",
    "# cbar.set_ticks([-200,-100,0,100,200])\n",
    "# cbar.set_label('kJ kg$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('cloud_cape_cin_timeseries_changes.png',\\\n",
    "           dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-25T06:05:03.117364Z",
     "start_time": "2018-06-25T06:04:59.675547Z"
    }
   },
   "outputs": [],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig,axes = mp.subplots(4,1)\n",
    "fig.set_size_inches(4.25,6)\n",
    "\n",
    "axes[0].plot(xvals-4,(net_atm_diffs_1000_sw_latlon_subset-net_atm_diffs_400_sw_latlon_subset)/(60*60),c='firebrick',label='full')\n",
    "axes[1].plot(xvals-4,(net_atm_diffs_1000_lw_latlon_subset-net_atm_diffs_400_lw_latlon_subset)/(60*60),c='firebrick')\n",
    "axes[2].plot(xvals-4,\\\n",
    "            ((net_atm_diffs_1000_sw_latlon_subset+net_atm_diffs_1000_lw_latlon_subset)-\\\n",
    "             (net_atm_diffs_400_sw_latlon_subset+net_atm_diffs_400_lw_latlon_subset))/(60*60),c='firebrick')\n",
    "\n",
    "\n",
    "axes[0].plot(xvals-4,(net_atm_diffs_1000_swc_latlon_subset-net_atm_diffs_400_swc_latlon_subset)/(60*60),c='coral',label='clear sky')\n",
    "axes[1].plot(xvals-4,(net_atm_diffs_1000_lwc_latlon_subset-net_atm_diffs_400_lwc_latlon_subset)/(60*60),c='coral')\n",
    "axes[2].plot(xvals-4,\\\n",
    "            ((net_atm_diffs_1000_swc_latlon_subset+net_atm_diffs_1000_lwc_latlon_subset)-\\\n",
    "             (net_atm_diffs_400_swc_latlon_subset+net_atm_diffs_400_lwc_latlon_subset))/(60*60),c='coral')\n",
    "\n",
    "contour_plot = axes[3].contourf(numpy.arange(241)-4,\\\n",
    "                                ptot_latlon_subset_timemean/100.,\\\n",
    "                                cldfra_diffs_latlon_subset.T,\\\n",
    "                                levels=levels, extend='both',\\\n",
    "                                cmap=cmocean.cm.balance_r)\n",
    "\n",
    "axes[3].set_ylim(1013.25,700)\n",
    "\n",
    "axes[0].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "axes[1].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "axes[2].axhline(0, lw=1, c='0.1', ls='--',zorder=0)\n",
    "\n",
    "axes[1].set_ylim(-10,10)\n",
    "axes[2].set_ylim(-5,5)\n",
    "\n",
    "[ax.tick_params(labelsize=fontsize) for ax in axes.flatten()]\n",
    "[ax.set_xlim(0,240) for ax in axes.flatten()]\n",
    "[ax.set_ylabel('W m$^{\\,-2}$', fontsize=fontsize) for ax in axes.flatten()[0:3]]\n",
    "[ax.set_xlabel('hour', fontsize=fontsize) for ax in [axes[-1]]]\n",
    "[ax.get_yaxis().set_label_coords(-0.15,0.5) for ax in axes.flatten()]\n",
    "[ax.xaxis.grid() for ax in axes.flatten()]\n",
    "\n",
    "axes[3].set_ylabel('hPa', fontsize=fontsize)\n",
    "axes[3].set_yticks([700,800,900,1000])\n",
    "\n",
    "[ax.set_xticks(numpy.arange(0,241,24)) for ax in axes.flatten()]\n",
    "[ax.set_xticklabels([]) for ax in axes[0:3]]\n",
    "# [ax.set_axisbelow(True) for ax in axes.flatten()]\n",
    "\n",
    "plot_labels = [\\\n",
    "'phys.– cont. net atmos. shortwave',\\\n",
    "'phys.– cont. net atmos. longwave',\\\n",
    "'phys.– cont. net atmos. rad.',\\\n",
    "'phys.– cont. cloud fraction change' ]\n",
    "\n",
    "[axes.flatten()[i].text(s=plot_labels[i], ha='left',\\\n",
    "        va='bottom',\\\n",
    "        x=0,y=1.03,\\\n",
    "        transform=axes.flatten()[i].transAxes,\\\n",
    "        fontsize=fontsize)\\\n",
    "for i in range(axes.flatten().size)]\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax4pos = axes[3].get_position()\n",
    "cbar_ax = fig.add_axes([ax4pos.x1+0.025,ax4pos.y0,0.025,ax4pos.height])\n",
    "cbar = fig.colorbar(contour_plot, cax=cbar_ax)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_ticks([-.1,-0.05,0,0.05,0.1])\n",
    "cbar.set_label('%', fontsize=fontsize)\n",
    "\n",
    "handles,labels=axes[0].get_legend_handles_labels()\n",
    "axes[0].legend(handles, labels, fontsize=fontsize, scatterpoints=1, ncol=1, \\\n",
    "               bbox_to_anchor=(1.03,1.0), bbox_transform=axes[0].transAxes, \\\n",
    "               loc='upper left', borderaxespad=0,\\\n",
    "               fancybox=False, edgecolor='0', framealpha=0, borderpad=0., \\\n",
    "               handletextpad=0.5)\n",
    "\n",
    "fig.savefig('radiation_AND_CLOUD_changes_with_control_full_and_clearsky.png',\\\n",
    "           dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get time data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-24T02:10:17.561197Z",
     "start_time": "2018-06-24T02:10:17.546577Z"
    }
   },
   "outputs": [],
   "source": [
    "time_datetimes = pandas.to_datetime([i.decode('utf-8') for i in time], format = '%Y-%m-%d_%H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-24T02:10:19.750505Z",
     "start_time": "2018-06-24T02:10:19.735549Z"
    }
   },
   "outputs": [],
   "source": [
    "skip_time = 24\n",
    "n_intervals = 8\n",
    "hour_strings = ['06','09','12','15','18','21','00','03']\n",
    "hour_strings_local = ['02', '05', '08', '11', '14', '17','20', '23']\n",
    "\n",
    "times_00 = [t.hour in [23,0,1] for t in time_datetimes[skip_time:]]\n",
    "times_03 = [t.hour in [2,3,4] for t in time_datetimes[skip_time:]]\n",
    "times_06 = [t.hour in [5,6,7] for t in time_datetimes[skip_time:]]\n",
    "times_09 = [t.hour in [8,9,10] for t in time_datetimes[skip_time:]]\n",
    "times_12 = [t.hour in [11,12,13] for t in time_datetimes[skip_time:]]\n",
    "times_15 = [t.hour in [14,15,16] for t in time_datetimes[skip_time:]]\n",
    "times_18 = [t.hour in [17,18,19] for t in time_datetimes[skip_time:]]\n",
    "times_21 = [t.hour in [20,21,22] for t in time_datetimes[skip_time:]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# lat indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:02:32.365226Z",
     "start_time": "2018-06-17T04:02:32.359084Z"
    }
   },
   "outputs": [],
   "source": [
    "lat_hi = 10.\n",
    "lat_lo = -10.\n",
    "\n",
    "# subset between lat_hi and lat_lo\n",
    "lat_indices = (xlat<=lat_hi)&(xlat>=lat_lo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:05:31.608852Z",
     "start_time": "2018-06-17T04:05:31.600230Z"
    }
   },
   "outputs": [],
   "source": [
    "lon_lo = 290-360\n",
    "lon_hi = 320-360\n",
    "lon_indices = (xlong[0,:]<lon_lo)|(xlong[0,:]>lon_hi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# cldfra"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-14T06:19:13.611597Z",
     "start_time": "2018-06-14T06:08:31.797511Z"
    }
   },
   "source": [
    "cldfra_diffs = cldfra_1000.values-cldfra_400.values\n",
    "\n",
    "cldfra_diffs_landmask = numpy.copy(cldfra_diffs)\n",
    "cldfra_diffs_landmask[:,:,landmask==1]=numpy.nan\n",
    "\n",
    "cldfra_diffs_lat_subset = cldfra_diffs[:,:,lat_indices[:,0],:]\n",
    "\n",
    "cldfra_400_landmask = numpy.copy(cldfra_400)\n",
    "cldfra_400_landmask[:,:,landmask==1]=numpy.nan"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-14T06:20:20.509608Z",
     "start_time": "2018-06-14T06:19:14.812532Z"
    }
   },
   "source": [
    "cldfra_400_lat_subset = cldfra_400[:,:,lat_indices[:,0],:]\n",
    "cldfra_diffs_lat_mean = numpy.nanmean(cldfra_diffs_lat_subset, axis=2)\n",
    "cldfra_400_lat_mean = numpy.nanmean(cldfra_400_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# geopotential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:07:57.679876Z",
     "start_time": "2018-06-17T04:07:42.668321Z"
    }
   },
   "outputs": [],
   "source": [
    "geopot_400_landmask = numpy.copy(geopot_400)\n",
    "geopot_400_landmask[:,:,landmask==1]=numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:08:07.257499Z",
     "start_time": "2018-06-17T04:07:58.613026Z"
    }
   },
   "outputs": [],
   "source": [
    "geopot_400_lat_subset = geopot_400[:,:,lat_indices[:,0],:]\n",
    "geopot_400_lat_mean = numpy.nanmean(geopot_400_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# net_bottom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:50:44.980744Z",
     "start_time": "2018-06-17T04:50:43.631144Z"
    }
   },
   "outputs": [],
   "source": [
    "net_bottom_diffs = net_bottom_diffs_1000-net_bottom_diffs_400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:50:52.750576Z",
     "start_time": "2018-06-17T04:50:44.989419Z"
    }
   },
   "outputs": [],
   "source": [
    "net_bottom_diffs_landmask = numpy.copy(net_bottom_diffs)\n",
    "net_bottom_diffs_landmask[:,landmask==0]=numpy.nan\n",
    "\n",
    "net_bottom_400_landmask = numpy.copy(net_bottom_400)\n",
    "net_bottom_400_landmask[:,landmask==0]=numpy.nan\n",
    "\n",
    "net_bottom_1000_landmask = numpy.copy(net_bottom_1000)\n",
    "net_bottom_1000_landmask[:,landmask==0]=numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:50:57.308961Z",
     "start_time": "2018-06-17T04:50:57.223712Z"
    }
   },
   "outputs": [],
   "source": [
    "net_bottom_diffs_lat_subset = net_bottom_diffs_landmask[:,lat_indices[:,0],:]\n",
    "net_bottom_400_lat_subset = net_bottom_400_landmask[:,lat_indices[:,0],:]\n",
    "net_bottom_1000_lat_subset = net_bottom_1000_landmask[:,lat_indices[:,0],:]\n",
    "\n",
    "net_bottom_400_lat_subset[:,:,lon_indices]=numpy.nan\n",
    "net_bottom_1000_lat_subset[:,:,lon_indices]=numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:50:59.683003Z",
     "start_time": "2018-06-17T04:50:59.473753Z"
    }
   },
   "outputs": [],
   "source": [
    "net_bottom_diffs_lat_mean = numpy.nanmean(net_bottom_diffs_lat_subset, axis=1)\n",
    "net_bottom_400_lat_mean = numpy.nanmean(net_bottom_400_lat_subset, axis=1)\n",
    "net_bottom_1000_lat_mean = numpy.nanmean(net_bottom_1000_lat_subset, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# fira"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:04:32.767647Z",
     "start_time": "2018-06-17T04:04:07.332756Z"
    }
   },
   "outputs": [],
   "source": [
    "fira_diffs = fira_1000.values-fira_400.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:04:33.528212Z",
     "start_time": "2018-06-17T04:04:33.398230Z"
    }
   },
   "outputs": [],
   "source": [
    "fira_diffs_landmask = numpy.copy(fira_diffs)\n",
    "fira_diffs_landmask[:,landmask==0]=numpy.nan\n",
    "\n",
    "fira_400_landmask = numpy.copy(fira_400)\n",
    "fira_400_landmask[:,landmask==0]=numpy.nan\n",
    "\n",
    "fira_1000_landmask = numpy.copy(fira_1000)\n",
    "fira_1000_landmask[:,landmask==0]=numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:13:19.234532Z",
     "start_time": "2018-06-17T04:13:15.257165Z"
    }
   },
   "outputs": [],
   "source": [
    "fira_diffs_lat_subset = fira_diffs_landmask[:,lat_indices[:,0],:]\n",
    "fira_400_lat_subset = fira_400_landmask[:,lat_indices[:,0],:]\n",
    "fira_1000_lat_subset = fira_1000_landmask[:,lat_indices[:,0],:]\n",
    "\n",
    "fira_400_lat_subset[:,:,lon_indices]=numpy.nan\n",
    "fira_1000_lat_subset[:,:,lon_indices]=numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:13:21.582302Z",
     "start_time": "2018-06-17T04:13:20.257743Z"
    }
   },
   "outputs": [],
   "source": [
    "fira_diffs_lat_mean = numpy.nanmean(fira_diffs_lat_subset, axis=1)\n",
    "fira_400_lat_mean = numpy.nanmean(fira_400_lat_subset, axis=1)\n",
    "fira_1000_lat_mean = numpy.nanmean(fira_1000_lat_subset, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# fsa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:04:53.875147Z",
     "start_time": "2018-06-17T04:04:40.844402Z"
    }
   },
   "outputs": [],
   "source": [
    "fsa_diffs = fsa_1000.values-fsa_400.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:04:54.669112Z",
     "start_time": "2018-06-17T04:04:54.539566Z"
    }
   },
   "outputs": [],
   "source": [
    "fsa_diffs_landmask = numpy.copy(fsa_diffs)\n",
    "fsa_diffs_landmask[:,landmask==0]=numpy.nan\n",
    "\n",
    "fsa_400_landmask = numpy.copy(fsa_400)\n",
    "fsa_400_landmask[:,landmask==0]=numpy.nan\n",
    "\n",
    "fsa_1000_landmask = numpy.copy(fsa_1000)\n",
    "fsa_1000_landmask[:,landmask==0]=numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:13:19.234532Z",
     "start_time": "2018-06-17T04:13:15.257165Z"
    }
   },
   "outputs": [],
   "source": [
    "fsa_diffs_lat_subset = fsa_diffs_landmask[:,lat_indices[:,0],:]\n",
    "fsa_400_lat_subset = fsa_400_landmask[:,lat_indices[:,0],:]\n",
    "fsa_1000_lat_subset = fsa_1000_landmask[:,lat_indices[:,0],:]\n",
    "\n",
    "fsa_400_lat_subset[:,:,lon_indices]=numpy.nan\n",
    "fsa_1000_lat_subset[:,:,lon_indices]=numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:13:21.582302Z",
     "start_time": "2018-06-17T04:13:20.257743Z"
    }
   },
   "outputs": [],
   "source": [
    "fsa_diffs_lat_mean = numpy.nanmean(fsa_diffs_lat_subset, axis=1)\n",
    "fsa_400_lat_mean = numpy.nanmean(fsa_400_lat_subset, axis=1)\n",
    "fsa_1000_lat_mean = numpy.nanmean(fsa_1000_lat_subset, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# glw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:35:13.048463Z",
     "start_time": "2018-06-17T04:35:01.271278Z"
    }
   },
   "outputs": [],
   "source": [
    "glw_diffs = glw_1000.values-glw_400.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:35:17.243103Z",
     "start_time": "2018-06-17T04:35:14.428058Z"
    }
   },
   "outputs": [],
   "source": [
    "glw_diffs_landmask = numpy.copy(glw_diffs)\n",
    "glw_diffs_landmask[:,landmask==0]=numpy.nan\n",
    "\n",
    "glw_400_landmask = numpy.copy(glw_400)\n",
    "glw_400_landmask[:,landmask==0]=numpy.nan\n",
    "\n",
    "glw_1000_landmask = numpy.copy(glw_1000)\n",
    "glw_1000_landmask[:,landmask==0]=numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:35:19.784794Z",
     "start_time": "2018-06-17T04:35:18.652049Z"
    }
   },
   "outputs": [],
   "source": [
    "glw_diffs_lat_subset = glw_diffs_landmask[:,lat_indices[:,0],:]\n",
    "glw_400_lat_subset = glw_400_landmask[:,lat_indices[:,0],:]\n",
    "glw_1000_lat_subset = glw_1000_landmask[:,lat_indices[:,0],:]\n",
    "\n",
    "glw_400_lat_subset[:,:,lon_indices]=numpy.nan\n",
    "glw_1000_lat_subset[:,:,lon_indices]=numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:35:21.412573Z",
     "start_time": "2018-06-17T04:35:21.232332Z"
    }
   },
   "outputs": [],
   "source": [
    "glw_diffs_lat_mean = numpy.nanmean(glw_diffs_lat_subset, axis=1)\n",
    "glw_400_lat_mean = numpy.nanmean(glw_400_lat_subset, axis=1)\n",
    "glw_1000_lat_mean = numpy.nanmean(glw_1000_lat_subset, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# swdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:35:13.048463Z",
     "start_time": "2018-06-17T04:35:01.271278Z"
    }
   },
   "outputs": [],
   "source": [
    "swdown_diffs = swdown_1000.values-swdown_400.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:35:17.243103Z",
     "start_time": "2018-06-17T04:35:14.428058Z"
    }
   },
   "outputs": [],
   "source": [
    "swdown_diffs_landmask = numpy.copy(swdown_diffs)\n",
    "swdown_diffs_landmask[:,landmask==0]=numpy.nan\n",
    "\n",
    "swdown_400_landmask = numpy.copy(swdown_400)\n",
    "swdown_400_landmask[:,landmask==0]=numpy.nan\n",
    "\n",
    "swdown_1000_landmask = numpy.copy(swdown_1000)\n",
    "swdown_1000_landmask[:,landmask==0]=numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:35:19.784794Z",
     "start_time": "2018-06-17T04:35:18.652049Z"
    }
   },
   "outputs": [],
   "source": [
    "swdown_diffs_lat_subset = swdown_diffs_landmask[:,lat_indices[:,0],:]\n",
    "swdown_400_lat_subset = swdown_400_landmask[:,lat_indices[:,0],:]\n",
    "swdown_1000_lat_subset = swdown_1000_landmask[:,lat_indices[:,0],:]\n",
    "\n",
    "swdown_400_lat_subset[:,:,lon_indices]=numpy.nan\n",
    "swdown_1000_lat_subset[:,:,lon_indices]=numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:35:21.412573Z",
     "start_time": "2018-06-17T04:35:21.232332Z"
    }
   },
   "outputs": [],
   "source": [
    "swdown_diffs_lat_mean = numpy.nanmean(swdown_diffs_lat_subset, axis=1)\n",
    "swdown_400_lat_mean = numpy.nanmean(swdown_400_lat_subset, axis=1)\n",
    "swdown_1000_lat_mean = numpy.nanmean(swdown_1000_lat_subset, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 hourly means of net_bottom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:51:11.475177Z",
     "start_time": "2018-06-17T04:51:11.447771Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate 3 hourly means for 400\n",
    "net_bottom_400_t00mean = numpy.mean(net_bottom_400_lat_mean[skip_time:][times_00,:], axis=0)\n",
    "net_bottom_400_t03mean = numpy.mean(net_bottom_400_lat_mean[skip_time:][times_03,:], axis=0)\n",
    "net_bottom_400_t06mean = numpy.mean(net_bottom_400_lat_mean[skip_time:][times_06,:], axis=0)\n",
    "net_bottom_400_t09mean = numpy.mean(net_bottom_400_lat_mean[skip_time:][times_09,:], axis=0)\n",
    "net_bottom_400_t12mean = numpy.mean(net_bottom_400_lat_mean[skip_time:][times_12,:], axis=0)\n",
    "net_bottom_400_t15mean = numpy.mean(net_bottom_400_lat_mean[skip_time:][times_15,:], axis=0)\n",
    "net_bottom_400_t18mean = numpy.mean(net_bottom_400_lat_mean[skip_time:][times_18,:], axis=0)\n",
    "net_bottom_400_t21mean = numpy.mean(net_bottom_400_lat_mean[skip_time:][times_21,:], axis=0)\n",
    "\n",
    "net_bottom_1000_t00mean = numpy.nanmean(net_bottom_1000_lat_mean[skip_time:][times_00,:], axis=0)\n",
    "net_bottom_1000_t03mean = numpy.nanmean(net_bottom_1000_lat_mean[skip_time:][times_03,:], axis=0)\n",
    "net_bottom_1000_t06mean = numpy.nanmean(net_bottom_1000_lat_mean[skip_time:][times_06,:], axis=0)\n",
    "net_bottom_1000_t09mean = numpy.nanmean(net_bottom_1000_lat_mean[skip_time:][times_09,:], axis=0)\n",
    "net_bottom_1000_t12mean = numpy.nanmean(net_bottom_1000_lat_mean[skip_time:][times_12,:], axis=0)\n",
    "net_bottom_1000_t15mean = numpy.nanmean(net_bottom_1000_lat_mean[skip_time:][times_15,:], axis=0)\n",
    "net_bottom_1000_t18mean = numpy.nanmean(net_bottom_1000_lat_mean[skip_time:][times_18,:], axis=0)\n",
    "net_bottom_1000_t21mean = numpy.nanmean(net_bottom_1000_lat_mean[skip_time:][times_21,:], axis=0)\n",
    "\n",
    "net_bottom_400_array = numpy.array([net_bottom_400_t06mean, net_bottom_400_t09mean, net_bottom_400_t12mean, net_bottom_400_t15mean, net_bottom_400_t18mean, net_bottom_400_t21mean, net_bottom_400_t00mean, net_bottom_400_t03mean])\n",
    "net_bottom_1000_array = numpy.array([net_bottom_1000_t06mean, net_bottom_1000_t09mean, net_bottom_1000_t12mean, net_bottom_1000_t15mean, net_bottom_1000_t18mean, net_bottom_1000_t21mean, net_bottom_1000_t00mean, net_bottom_1000_t03mean])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:51:14.396961Z",
     "start_time": "2018-06-17T04:51:14.377935Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate 3 hourly means for 400\n",
    "net_bottom_diffs_t00mean = numpy.nanmean(net_bottom_diffs_lat_mean[skip_time:,:][times_00,:], axis=0)\n",
    "net_bottom_diffs_t03mean = numpy.nanmean(net_bottom_diffs_lat_mean[skip_time:,:][times_03,:], axis=0)\n",
    "net_bottom_diffs_t06mean = numpy.nanmean(net_bottom_diffs_lat_mean[skip_time:,:][times_06,:], axis=0)\n",
    "net_bottom_diffs_t09mean = numpy.nanmean(net_bottom_diffs_lat_mean[skip_time:,:][times_09,:], axis=0)\n",
    "net_bottom_diffs_t12mean = numpy.nanmean(net_bottom_diffs_lat_mean[skip_time:,:][times_12,:], axis=0)\n",
    "net_bottom_diffs_t15mean = numpy.nanmean(net_bottom_diffs_lat_mean[skip_time:,:][times_15,:], axis=0)\n",
    "net_bottom_diffs_t18mean = numpy.nanmean(net_bottom_diffs_lat_mean[skip_time:,:][times_18,:], axis=0)\n",
    "net_bottom_diffs_t21mean = numpy.nanmean(net_bottom_diffs_lat_mean[skip_time:,:][times_21,:], axis=0)\n",
    "\n",
    "net_bottom_diffs_array = numpy.array([net_bottom_diffs_t06mean, net_bottom_diffs_t09mean, net_bottom_diffs_t12mean, net_bottom_diffs_t15mean, net_bottom_diffs_t18mean, net_bottom_diffs_t21mean, net_bottom_diffs_t00mean, net_bottom_diffs_t03mean])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 hourly means of fira"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:13:25.550317Z",
     "start_time": "2018-06-17T04:13:25.532569Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate 3 hourly means for 400\n",
    "fira_400_t00mean = numpy.mean(fira_400_lat_mean[skip_time:][times_00,:], axis=0)\n",
    "fira_400_t03mean = numpy.mean(fira_400_lat_mean[skip_time:][times_03,:], axis=0)\n",
    "fira_400_t06mean = numpy.mean(fira_400_lat_mean[skip_time:][times_06,:], axis=0)\n",
    "fira_400_t09mean = numpy.mean(fira_400_lat_mean[skip_time:][times_09,:], axis=0)\n",
    "fira_400_t12mean = numpy.mean(fira_400_lat_mean[skip_time:][times_12,:], axis=0)\n",
    "fira_400_t15mean = numpy.mean(fira_400_lat_mean[skip_time:][times_15,:], axis=0)\n",
    "fira_400_t18mean = numpy.mean(fira_400_lat_mean[skip_time:][times_18,:], axis=0)\n",
    "fira_400_t21mean = numpy.mean(fira_400_lat_mean[skip_time:][times_21,:], axis=0)\n",
    "\n",
    "fira_1000_t00mean = numpy.mean(fira_1000_lat_mean[skip_time:][times_00,:], axis=0)\n",
    "fira_1000_t03mean = numpy.mean(fira_1000_lat_mean[skip_time:][times_03,:], axis=0)\n",
    "fira_1000_t06mean = numpy.mean(fira_1000_lat_mean[skip_time:][times_06,:], axis=0)\n",
    "fira_1000_t09mean = numpy.mean(fira_1000_lat_mean[skip_time:][times_09,:], axis=0)\n",
    "fira_1000_t12mean = numpy.mean(fira_1000_lat_mean[skip_time:][times_12,:], axis=0)\n",
    "fira_1000_t15mean = numpy.mean(fira_1000_lat_mean[skip_time:][times_15,:], axis=0)\n",
    "fira_1000_t18mean = numpy.mean(fira_1000_lat_mean[skip_time:][times_18,:], axis=0)\n",
    "fira_1000_t21mean = numpy.mean(fira_1000_lat_mean[skip_time:][times_21,:], axis=0)\n",
    "\n",
    "fira_400_array = numpy.array([fira_400_t06mean, fira_400_t09mean, fira_400_t12mean, fira_400_t15mean, fira_400_t18mean, fira_400_t21mean, fira_400_t00mean, fira_400_t03mean])\n",
    "fira_1000_array = numpy.array([fira_1000_t06mean, fira_1000_t09mean, fira_1000_t12mean, fira_1000_t15mean, fira_1000_t18mean, fira_1000_t21mean, fira_1000_t00mean, fira_1000_t03mean])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:24:07.089984Z",
     "start_time": "2018-06-17T04:24:07.075151Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate 3 hourly means for 400\n",
    "fira_diffs_t00mean = numpy.mean(fira_diffs_lat_mean[skip_time:,:][times_00,:], axis=0)\n",
    "fira_diffs_t03mean = numpy.mean(fira_diffs_lat_mean[skip_time:,:][times_03,:], axis=0)\n",
    "fira_diffs_t06mean = numpy.mean(fira_diffs_lat_mean[skip_time:,:][times_06,:], axis=0)\n",
    "fira_diffs_t09mean = numpy.mean(fira_diffs_lat_mean[skip_time:,:][times_09,:], axis=0)\n",
    "fira_diffs_t12mean = numpy.mean(fira_diffs_lat_mean[skip_time:,:][times_12,:], axis=0)\n",
    "fira_diffs_t15mean = numpy.mean(fira_diffs_lat_mean[skip_time:,:][times_15,:], axis=0)\n",
    "fira_diffs_t18mean = numpy.mean(fira_diffs_lat_mean[skip_time:,:][times_18,:], axis=0)\n",
    "fira_diffs_t21mean = numpy.mean(fira_diffs_lat_mean[skip_time:,:][times_21,:], axis=0)\n",
    "\n",
    "fira_diffs_array = numpy.array([fira_diffs_t06mean, fira_diffs_t09mean, fira_diffs_t12mean, fira_diffs_t15mean, fira_diffs_t18mean, fira_diffs_t21mean, fira_diffs_t00mean, fira_diffs_t03mean])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 hourly means of fsa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:17:28.167904Z",
     "start_time": "2018-06-17T04:17:28.149999Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate 3 hourly means for 400\n",
    "fsa_400_t00mean = numpy.mean(fsa_400_lat_mean[skip_time:][times_00,:], axis=0)\n",
    "fsa_400_t03mean = numpy.mean(fsa_400_lat_mean[skip_time:][times_03,:], axis=0)\n",
    "fsa_400_t06mean = numpy.mean(fsa_400_lat_mean[skip_time:][times_06,:], axis=0)\n",
    "fsa_400_t09mean = numpy.mean(fsa_400_lat_mean[skip_time:][times_09,:], axis=0)\n",
    "fsa_400_t12mean = numpy.mean(fsa_400_lat_mean[skip_time:][times_12,:], axis=0)\n",
    "fsa_400_t15mean = numpy.mean(fsa_400_lat_mean[skip_time:][times_15,:], axis=0)\n",
    "fsa_400_t18mean = numpy.mean(fsa_400_lat_mean[skip_time:][times_18,:], axis=0)\n",
    "fsa_400_t21mean = numpy.mean(fsa_400_lat_mean[skip_time:][times_21,:], axis=0)\n",
    "\n",
    "fsa_1000_t00mean = numpy.mean(fsa_1000_lat_mean[skip_time:][times_00,:], axis=0)\n",
    "fsa_1000_t03mean = numpy.mean(fsa_1000_lat_mean[skip_time:][times_03,:], axis=0)\n",
    "fsa_1000_t06mean = numpy.mean(fsa_1000_lat_mean[skip_time:][times_06,:], axis=0)\n",
    "fsa_1000_t09mean = numpy.mean(fsa_1000_lat_mean[skip_time:][times_09,:], axis=0)\n",
    "fsa_1000_t12mean = numpy.mean(fsa_1000_lat_mean[skip_time:][times_12,:], axis=0)\n",
    "fsa_1000_t15mean = numpy.mean(fsa_1000_lat_mean[skip_time:][times_15,:], axis=0)\n",
    "fsa_1000_t18mean = numpy.mean(fsa_1000_lat_mean[skip_time:][times_18,:], axis=0)\n",
    "fsa_1000_t21mean = numpy.mean(fsa_1000_lat_mean[skip_time:][times_21,:], axis=0)\n",
    "\n",
    "fsa_400_array = numpy.array([fsa_400_t06mean, fsa_400_t09mean, fsa_400_t12mean, fsa_400_t15mean, fsa_400_t18mean, fsa_400_t21mean, fsa_400_t00mean, fsa_400_t03mean])\n",
    "fsa_1000_array = numpy.array([fsa_1000_t06mean, fsa_1000_t09mean, fsa_1000_t12mean, fsa_1000_t15mean, fsa_1000_t18mean, fsa_1000_t21mean, fsa_1000_t00mean, fsa_1000_t03mean])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:23:53.603889Z",
     "start_time": "2018-06-17T04:23:53.589110Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate 3 hourly means for 400\n",
    "fsa_diffs_t00mean = numpy.mean(fsa_diffs_lat_mean[skip_time:,:][times_00,:], axis=0)\n",
    "fsa_diffs_t03mean = numpy.mean(fsa_diffs_lat_mean[skip_time:,:][times_03,:], axis=0)\n",
    "fsa_diffs_t06mean = numpy.mean(fsa_diffs_lat_mean[skip_time:,:][times_06,:], axis=0)\n",
    "fsa_diffs_t09mean = numpy.mean(fsa_diffs_lat_mean[skip_time:,:][times_09,:], axis=0)\n",
    "fsa_diffs_t12mean = numpy.mean(fsa_diffs_lat_mean[skip_time:,:][times_12,:], axis=0)\n",
    "fsa_diffs_t15mean = numpy.mean(fsa_diffs_lat_mean[skip_time:,:][times_15,:], axis=0)\n",
    "fsa_diffs_t18mean = numpy.mean(fsa_diffs_lat_mean[skip_time:,:][times_18,:], axis=0)\n",
    "fsa_diffs_t21mean = numpy.mean(fsa_diffs_lat_mean[skip_time:,:][times_21,:], axis=0)\n",
    "\n",
    "fsa_diffs_array = numpy.array([fsa_diffs_t06mean, fsa_diffs_t09mean, fsa_diffs_t12mean, fsa_diffs_t15mean, fsa_diffs_t18mean, fsa_diffs_t21mean, fsa_diffs_t00mean, fsa_diffs_t03mean])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 hourly means of glw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:35:34.295636Z",
     "start_time": "2018-06-17T04:35:34.274525Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate 3 hourly means for 400\n",
    "glw_400_t00mean = numpy.mean(glw_400_lat_mean[skip_time:][times_00,:], axis=0)\n",
    "glw_400_t03mean = numpy.mean(glw_400_lat_mean[skip_time:][times_03,:], axis=0)\n",
    "glw_400_t06mean = numpy.mean(glw_400_lat_mean[skip_time:][times_06,:], axis=0)\n",
    "glw_400_t09mean = numpy.mean(glw_400_lat_mean[skip_time:][times_09,:], axis=0)\n",
    "glw_400_t12mean = numpy.mean(glw_400_lat_mean[skip_time:][times_12,:], axis=0)\n",
    "glw_400_t15mean = numpy.mean(glw_400_lat_mean[skip_time:][times_15,:], axis=0)\n",
    "glw_400_t18mean = numpy.mean(glw_400_lat_mean[skip_time:][times_18,:], axis=0)\n",
    "glw_400_t21mean = numpy.mean(glw_400_lat_mean[skip_time:][times_21,:], axis=0)\n",
    "\n",
    "glw_1000_t00mean = numpy.mean(glw_1000_lat_mean[skip_time:][times_00,:], axis=0)\n",
    "glw_1000_t03mean = numpy.mean(glw_1000_lat_mean[skip_time:][times_03,:], axis=0)\n",
    "glw_1000_t06mean = numpy.mean(glw_1000_lat_mean[skip_time:][times_06,:], axis=0)\n",
    "glw_1000_t09mean = numpy.mean(glw_1000_lat_mean[skip_time:][times_09,:], axis=0)\n",
    "glw_1000_t12mean = numpy.mean(glw_1000_lat_mean[skip_time:][times_12,:], axis=0)\n",
    "glw_1000_t15mean = numpy.mean(glw_1000_lat_mean[skip_time:][times_15,:], axis=0)\n",
    "glw_1000_t18mean = numpy.mean(glw_1000_lat_mean[skip_time:][times_18,:], axis=0)\n",
    "glw_1000_t21mean = numpy.mean(glw_1000_lat_mean[skip_time:][times_21,:], axis=0)\n",
    "\n",
    "glw_400_array = numpy.array([glw_400_t06mean, glw_400_t09mean, glw_400_t12mean, glw_400_t15mean, glw_400_t18mean, glw_400_t21mean, glw_400_t00mean, glw_400_t03mean])\n",
    "glw_1000_array = numpy.array([glw_1000_t06mean, glw_1000_t09mean, glw_1000_t12mean, glw_1000_t15mean, glw_1000_t18mean, glw_1000_t21mean, glw_1000_t00mean, glw_1000_t03mean])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:35:36.465829Z",
     "start_time": "2018-06-17T04:35:36.453249Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate 3 hourly means for 400\n",
    "glw_diffs_t00mean = numpy.mean(glw_diffs_lat_mean[skip_time:,:][times_00,:], axis=0)\n",
    "glw_diffs_t03mean = numpy.mean(glw_diffs_lat_mean[skip_time:,:][times_03,:], axis=0)\n",
    "glw_diffs_t06mean = numpy.mean(glw_diffs_lat_mean[skip_time:,:][times_06,:], axis=0)\n",
    "glw_diffs_t09mean = numpy.mean(glw_diffs_lat_mean[skip_time:,:][times_09,:], axis=0)\n",
    "glw_diffs_t12mean = numpy.mean(glw_diffs_lat_mean[skip_time:,:][times_12,:], axis=0)\n",
    "glw_diffs_t15mean = numpy.mean(glw_diffs_lat_mean[skip_time:,:][times_15,:], axis=0)\n",
    "glw_diffs_t18mean = numpy.mean(glw_diffs_lat_mean[skip_time:,:][times_18,:], axis=0)\n",
    "glw_diffs_t21mean = numpy.mean(glw_diffs_lat_mean[skip_time:,:][times_21,:], axis=0)\n",
    "\n",
    "glw_diffs_array = numpy.array([glw_diffs_t06mean, glw_diffs_t09mean, glw_diffs_t12mean, glw_diffs_t15mean, glw_diffs_t18mean, glw_diffs_t21mean, glw_diffs_t00mean, glw_diffs_t03mean])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 hourly means of swdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:35:34.295636Z",
     "start_time": "2018-06-17T04:35:34.274525Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate 3 hourly means for 400\n",
    "swdown_400_t00mean = numpy.mean(swdown_400_lat_mean[skip_time:][times_00,:], axis=0)\n",
    "swdown_400_t03mean = numpy.mean(swdown_400_lat_mean[skip_time:][times_03,:], axis=0)\n",
    "swdown_400_t06mean = numpy.mean(swdown_400_lat_mean[skip_time:][times_06,:], axis=0)\n",
    "swdown_400_t09mean = numpy.mean(swdown_400_lat_mean[skip_time:][times_09,:], axis=0)\n",
    "swdown_400_t12mean = numpy.mean(swdown_400_lat_mean[skip_time:][times_12,:], axis=0)\n",
    "swdown_400_t15mean = numpy.mean(swdown_400_lat_mean[skip_time:][times_15,:], axis=0)\n",
    "swdown_400_t18mean = numpy.mean(swdown_400_lat_mean[skip_time:][times_18,:], axis=0)\n",
    "swdown_400_t21mean = numpy.mean(swdown_400_lat_mean[skip_time:][times_21,:], axis=0)\n",
    "\n",
    "swdown_1000_t00mean = numpy.mean(swdown_1000_lat_mean[skip_time:][times_00,:], axis=0)\n",
    "swdown_1000_t03mean = numpy.mean(swdown_1000_lat_mean[skip_time:][times_03,:], axis=0)\n",
    "swdown_1000_t06mean = numpy.mean(swdown_1000_lat_mean[skip_time:][times_06,:], axis=0)\n",
    "swdown_1000_t09mean = numpy.mean(swdown_1000_lat_mean[skip_time:][times_09,:], axis=0)\n",
    "swdown_1000_t12mean = numpy.mean(swdown_1000_lat_mean[skip_time:][times_12,:], axis=0)\n",
    "swdown_1000_t15mean = numpy.mean(swdown_1000_lat_mean[skip_time:][times_15,:], axis=0)\n",
    "swdown_1000_t18mean = numpy.mean(swdown_1000_lat_mean[skip_time:][times_18,:], axis=0)\n",
    "swdown_1000_t21mean = numpy.mean(swdown_1000_lat_mean[skip_time:][times_21,:], axis=0)\n",
    "\n",
    "swdown_400_array = numpy.array([swdown_400_t06mean, swdown_400_t09mean, swdown_400_t12mean, swdown_400_t15mean, swdown_400_t18mean, swdown_400_t21mean, swdown_400_t00mean, swdown_400_t03mean])\n",
    "swdown_1000_array = numpy.array([swdown_1000_t06mean, swdown_1000_t09mean, swdown_1000_t12mean, swdown_1000_t15mean, swdown_1000_t18mean, swdown_1000_t21mean, swdown_1000_t00mean, swdown_1000_t03mean])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:35:36.465829Z",
     "start_time": "2018-06-17T04:35:36.453249Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate 3 hourly means for 400\n",
    "swdown_diffs_t00mean = numpy.mean(swdown_diffs_lat_mean[skip_time:,:][times_00,:], axis=0)\n",
    "swdown_diffs_t03mean = numpy.mean(swdown_diffs_lat_mean[skip_time:,:][times_03,:], axis=0)\n",
    "swdown_diffs_t06mean = numpy.mean(swdown_diffs_lat_mean[skip_time:,:][times_06,:], axis=0)\n",
    "swdown_diffs_t09mean = numpy.mean(swdown_diffs_lat_mean[skip_time:,:][times_09,:], axis=0)\n",
    "swdown_diffs_t12mean = numpy.mean(swdown_diffs_lat_mean[skip_time:,:][times_12,:], axis=0)\n",
    "swdown_diffs_t15mean = numpy.mean(swdown_diffs_lat_mean[skip_time:,:][times_15,:], axis=0)\n",
    "swdown_diffs_t18mean = numpy.mean(swdown_diffs_lat_mean[skip_time:,:][times_18,:], axis=0)\n",
    "swdown_diffs_t21mean = numpy.mean(swdown_diffs_lat_mean[skip_time:,:][times_21,:], axis=0)\n",
    "\n",
    "swdown_diffs_array = numpy.array([swdown_diffs_t06mean, swdown_diffs_t09mean, swdown_diffs_t12mean, swdown_diffs_t15mean, swdown_diffs_t18mean, swdown_diffs_t21mean, swdown_diffs_t00mean, swdown_diffs_t03mean])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## create grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-17T04:15:03.622146Z",
     "start_time": "2018-06-17T04:15:03.613490Z"
    }
   },
   "outputs": [],
   "source": [
    "xx,zz=numpy.meshgrid(xlong[0,:],geopot_400_lat_mean[0,:,:].mean(axis=1)/10000.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot fira minus fsa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-21T15:18:07.619869Z",
     "start_time": "2018-06-21T15:18:01.283623Z"
    }
   },
   "outputs": [],
   "source": [
    "#fig,ax_array = mp.subplots(4,2)\n",
    "#fig.set_size_inches(7.5,6)\n",
    "fontsize=12\n",
    "\n",
    "fig = mp.figure(figsize=(7.5,6))\n",
    "\n",
    "ax_list = []\n",
    "for t in range(8):\n",
    "    ax=fig.add_subplot(4,2,t+1)\n",
    "    radiation_smoothed = pandas.Series(fira_diffs_array[t]-fsa_diffs_array[t]).rolling(window=3, center=True, min_periods=1).mean()\n",
    "    ax.plot(xx[0,:]+360., radiation_smoothed, c='firebrick')\n",
    "    ax.text(s='('+string.ascii_lowercase[t]+') hour '+hour_strings_local[t], x=0, y=1.02,\\\n",
    "            transform=ax.transAxes,fontsize=fontsize,ha='left',va='bottom')\n",
    "    ax.tick_params(labelsize=fontsize)\n",
    "    ax_list.append(ax)\n",
    "    ax.axhline(0, lw=1, c='0.1', ls='--', zorder=0)\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig('net_radiation_atmosphere_3hour.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot swdown minus glw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot net bottom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-21T15:17:03.323651Z",
     "start_time": "2018-06-21T15:16:56.787093Z"
    }
   },
   "outputs": [],
   "source": [
    "#fig,ax_array = mp.subplots(4,2)\n",
    "#fig.set_size_inches(7.5,6)\n",
    "fontsize=12\n",
    "\n",
    "fig = mp.figure(figsize=(7.5,6))\n",
    "\n",
    "ax_list = []\n",
    "for t in range(8):\n",
    "    ax=fig.add_subplot(4,2,t+1)\n",
    "    radiation_smoothed = pandas.Series(net_bottom_diffs_array[t]).rolling(window=3, center=True, min_periods=1).mean()\n",
    "    ax.plot(xx[0,:]+360., radiation_smoothed/3600., c='firebrick')\n",
    "    ax.text(s='('+string.ascii_lowercase[t]+') hour '+hour_strings_local[t], x=0, y=1.02,\\\n",
    "            transform=ax.transAxes,fontsize=fontsize,ha='left',va='bottom')\n",
    "    ax.tick_params(labelsize=fontsize)\n",
    "    ax_list.append(ax)\n",
    "    ax.axhline(0, lw=1, c='0.1', ls='--', zorder=0)\n",
    "    if t in [0,2,4,6]:\n",
    "        ax.set_ylabel('W m$^{\\,-2}$', fontsize=fontsize)\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig('net_radiation_bottom_3hour.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  }
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