gp1_calculations_for_different_PBL_schemes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-18T01:01:45.675285Z",
     "start_time": "2018-08-18T01:01:45.650786Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import xarray\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as mp\n",
    "import datetime\n",
    "import matplotlib.colors\n",
    "import scipy.stats\n",
    "import itertools\n",
    "import string\n",
    "import matplotlib.patches\n",
    "import glob\n",
    "import matplotlib.gridspec\n",
    "import scipy.ndimage\n",
    "\n",
    "import wrf\n",
    "import netCDF4\n",
    "import cmocean\n",
    "\n",
    "import pandas\n",
    "\n",
    "import matplotlib.ticker\n",
    "import cartopy.mpl.gridliner #import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "\n",
    "import mpl_toolkits\n",
    "import cartopy\n",
    "import cartopy.io.shapereader\n",
    "\n",
    "DEGREE_SIGN = u'\\u00B0'\n",
    "mp.rcParams.update({'mathtext.default': 'regular'})\n",
    "\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:10:15.984186Z",
     "start_time": "2018-08-17T19:10:15.978551Z"
    }
   },
   "outputs": [],
   "source": [
    "run_info = 'FMAASO'\n",
    "skip_time=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# opening mean fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## control data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:10:16.192078Z",
     "start_time": "2018-08-17T19:10:15.992560Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "001\t\t\t001_MYNN25_scheme\t  001_UW_scheme\r\n",
      "001_ACM2_scheme\t\t001_MYNN30_scheme\t  001_UW_scheme_10days\r\n",
      "001_ACM2_scheme_10days\t001_MYNN30_scheme_10days  001_with_extras\r\n",
      "001_MYJ_scheme\t\t001_TEMF_scheme\t\t  001_with_xkzs\r\n",
      "001_MYJ_scheme_10days\t001_TEMF_scheme_10days\t  001_with_xkzs_and_fxpbls\r\n"
     ]
    }
   ],
   "source": [
    "!ls /beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/400_sensitivity/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "YSU, ACM2, MYJ, and UW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:11:15.906414Z",
     "start_time": "2018-08-17T19:10:16.194981Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/400_sensitivity/001_with_extras'\n",
    "ysu_file_list = sorted(glob.glob(ysu_root_dir+'/'+'wrfout*'))\n",
    "ysu_ds_list = [netCDF4.Dataset(file) for file in ysu_file_list]\n",
    "\n",
    "ysu_ptot_400 = numpy.load('DATA_ptot_400_sensitivity.npy')\n",
    "ysu_geopot_400 = numpy.load('DATA_geopot_400_sensitivity.npy')\n",
    "ysu_tk_400 = numpy.load('DATA_tk_400_sensitivity.npy')\n",
    "\n",
    "ysu_cont_mfds = xarray.open_mfdataset(ysu_root_dir + '/' + 'wrfout*')\n",
    "\n",
    "ysu_time = ysu_cont_mfds['Times'].values\n",
    "ysu_xlat = ysu_cont_mfds['XLAT'].values[0,:,:]\n",
    "ysu_xlong = ysu_cont_mfds['XLONG'].values[0,:,:]\n",
    "ysu_landmask = ysu_cont_mfds['LANDMASK'].values[0,:,:]\n",
    "\n",
    "ysu_qvapor_400 = ysu_cont_mfds['QVAPOR'].values\n",
    "ysu_t_400 = ysu_cont_mfds['T'].values\n",
    "ysu_pblh_400 = ysu_cont_mfds['PBLH'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:11:20.791533Z",
     "start_time": "2018-08-17T19:11:15.914849Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_cldfra_400 = ysu_cont_mfds['CLDFRA'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:41:25.746894Z",
     "start_time": "2018-08-17T19:41:23.001210Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_rainc_400 = ysu_cont_mfds['RAINC'].values\n",
    "ysu_rainnc_400 = ysu_cont_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:12:15.136514Z",
     "start_time": "2018-08-17T19:11:20.800069Z"
    }
   },
   "outputs": [],
   "source": [
    "acm2_root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/400_sensitivity/001_ACM2_scheme'\n",
    "acm2_file_list = sorted(glob.glob(acm2_root_dir+'/'+'wrfout*'))\n",
    "acm2_ds_list = [netCDF4.Dataset(file) for file in acm2_file_list]\n",
    "\n",
    "acm2_ptot_400 = wrf.getvar(acm2_ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "acm2_geopot_400 = wrf.getvar(acm2_ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "acm2_tk_400 = wrf.getvar(acm2_ds_list, 'tk', timeidx=wrf.ALL_TIMES).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:12:47.388002Z",
     "start_time": "2018-08-17T19:12:15.145019Z"
    }
   },
   "outputs": [],
   "source": [
    "acm2_cont_mfds = xarray.open_mfdataset(acm2_root_dir + '/' + 'wrfout*')\n",
    "acm2_qvapor_400 = acm2_cont_mfds['QVAPOR'].values\n",
    "acm2_t_400 = acm2_cont_mfds['T'].values\n",
    "acm2_pblh_400 = acm2_cont_mfds['PBLH'].values\n",
    "acm2_cldfra_400 = acm2_cont_mfds['CLDFRA'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:41:09.803888Z",
     "start_time": "2018-08-17T19:41:08.793095Z"
    }
   },
   "outputs": [],
   "source": [
    "acm2_rainc_400 = acm2_cont_mfds['RAINC'].values\n",
    "acm2_rainnc_400 = acm2_cont_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:13:46.005173Z",
     "start_time": "2018-08-17T19:12:47.396472Z"
    }
   },
   "outputs": [],
   "source": [
    "myj_root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/400_sensitivity/001_MYJ_scheme'\n",
    "myj_file_list = sorted(glob.glob(myj_root_dir+'/'+'wrfout*'))\n",
    "myj_ds_list = [netCDF4.Dataset(file) for file in myj_file_list]\n",
    "\n",
    "myj_ptot_400 = wrf.getvar(myj_ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "myj_geopot_400 = wrf.getvar(myj_ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "myj_tk_400 = wrf.getvar(myj_ds_list, 'tk', timeidx=wrf.ALL_TIMES).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:14:15.535103Z",
     "start_time": "2018-08-17T19:13:46.013849Z"
    }
   },
   "outputs": [],
   "source": [
    "myj_cont_mfds = xarray.open_mfdataset(myj_root_dir + '/' + 'wrfout*')\n",
    "myj_qvapor_400 = myj_cont_mfds['QVAPOR'].values\n",
    "myj_t_400 = myj_cont_mfds['T'].values\n",
    "myj_pblh_400 = myj_cont_mfds['PBLH'].values\n",
    "myj_cldfra_400 = myj_cont_mfds['CLDFRA'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:41:38.633748Z",
     "start_time": "2018-08-17T19:41:35.107537Z"
    }
   },
   "outputs": [],
   "source": [
    "myj_rainc_400 = myj_cont_mfds['RAINC'].values\n",
    "myj_rainnc_400 = myj_cont_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:15:10.107807Z",
     "start_time": "2018-08-17T19:14:15.543598Z"
    }
   },
   "outputs": [],
   "source": [
    "uw_root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/400_sensitivity/001_UW_scheme'\n",
    "uw_file_list = sorted(glob.glob(uw_root_dir+'/'+'wrfout*'))\n",
    "uw_ds_list = [netCDF4.Dataset(file) for file in uw_file_list]\n",
    "\n",
    "uw_ptot_400 = wrf.getvar(uw_ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "uw_geopot_400 = wrf.getvar(uw_ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "uw_tk_400 = wrf.getvar(uw_ds_list, 'tk', timeidx=wrf.ALL_TIMES).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:15:36.823470Z",
     "start_time": "2018-08-17T19:15:10.119894Z"
    }
   },
   "outputs": [],
   "source": [
    "uw_cont_mfds = xarray.open_mfdataset(uw_root_dir + '/' + 'wrfout*')\n",
    "uw_qvapor_400 = uw_cont_mfds['QVAPOR'].values\n",
    "uw_t_400 = uw_cont_mfds['T'].values\n",
    "uw_pblh_400 = uw_cont_mfds['PBLH'].values\n",
    "uw_cldfra_400 = uw_cont_mfds['CLDFRA'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:41:46.072890Z",
     "start_time": "2018-08-17T19:41:43.863885Z"
    }
   },
   "outputs": [],
   "source": [
    "uw_rainc_400 = uw_cont_mfds['RAINC'].values\n",
    "uw_rainnc_400 = uw_cont_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:16:33.491358Z",
     "start_time": "2018-08-17T19:15:36.832078Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn25_root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/400_sensitivity/001_MYNN25_scheme'\n",
    "mynn25_file_list = sorted(glob.glob(mynn25_root_dir+'/'+'wrfout*'))\n",
    "mynn25_ds_list = [netCDF4.Dataset(file) for file in mynn25_file_list]\n",
    "\n",
    "mynn25_ptot_400 = wrf.getvar(mynn25_ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "mynn25_geopot_400 = wrf.getvar(mynn25_ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "mynn25_tk_400 = wrf.getvar(mynn25_ds_list, 'tk', timeidx=wrf.ALL_TIMES).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:17:07.468872Z",
     "start_time": "2018-08-17T19:16:33.499787Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn25_cont_mfds = xarray.open_mfdataset(mynn25_root_dir + '/' + 'wrfout*')\n",
    "mynn25_qvapor_400 = mynn25_cont_mfds['QVAPOR'].values\n",
    "mynn25_t_400 = mynn25_cont_mfds['T'].values\n",
    "mynn25_pblh_400 = mynn25_cont_mfds['PBLH'].values\n",
    "mynn25_cldfra_400 = mynn25_cont_mfds['CLDFRA'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:41:56.845815Z",
     "start_time": "2018-08-17T19:41:53.555411Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn25_rainc_400 = mynn25_cont_mfds['RAINC'].values\n",
    "mynn25_rainnc_400 = mynn25_cont_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:18:10.563876Z",
     "start_time": "2018-08-17T19:17:07.477311Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn30_root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/400_sensitivity/001_MYNN30_scheme'\n",
    "mynn30_file_list = sorted(glob.glob(mynn30_root_dir+'/'+'wrfout*'))\n",
    "mynn30_ds_list = [netCDF4.Dataset(file) for file in mynn30_file_list]\n",
    "\n",
    "mynn30_ptot_400 = wrf.getvar(mynn30_ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "mynn30_geopot_400 = wrf.getvar(mynn30_ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "mynn30_tk_400 = wrf.getvar(mynn30_ds_list, 'tk', timeidx=wrf.ALL_TIMES).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:18:45.695946Z",
     "start_time": "2018-08-17T19:18:10.572324Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn30_cont_mfds = xarray.open_mfdataset(mynn30_root_dir + '/' + 'wrfout*')\n",
    "mynn30_qvapor_400 = mynn30_cont_mfds['QVAPOR'].values\n",
    "mynn30_t_400 = mynn30_cont_mfds['T'].values\n",
    "mynn30_pblh_400 = mynn30_cont_mfds['PBLH'].values\n",
    "mynn30_cldfra_400 = mynn30_cont_mfds['CLDFRA'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:42:06.324141Z",
     "start_time": "2018-08-17T19:42:02.532756Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn30_rainc_400 = mynn30_cont_mfds['RAINC'].values\n",
    "mynn30_rainnc_400 = mynn30_cont_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:19:52.886283Z",
     "start_time": "2018-08-17T19:18:45.704686Z"
    }
   },
   "outputs": [],
   "source": [
    "temf_root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/400_sensitivity/001_TEMF_scheme'\n",
    "temf_file_list = sorted(glob.glob(temf_root_dir+'/'+'wrfout*'))\n",
    "temf_ds_list = [netCDF4.Dataset(file) for file in temf_file_list]\n",
    "\n",
    "temf_ptot_400 = wrf.getvar(temf_ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "temf_geopot_400 = wrf.getvar(temf_ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "temf_tk_400 = wrf.getvar(temf_ds_list, 'tk', timeidx=wrf.ALL_TIMES).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:20:25.632254Z",
     "start_time": "2018-08-17T19:19:52.894625Z"
    }
   },
   "outputs": [],
   "source": [
    "temf_cont_mfds = xarray.open_mfdataset(temf_root_dir + '/' + 'wrfout*')\n",
    "temf_qvapor_400 = temf_cont_mfds['QVAPOR'].values\n",
    "temf_t_400 = temf_cont_mfds['T'].values\n",
    "temf_pblh_400 = temf_cont_mfds['PBLH'].values\n",
    "temf_cldfra_400 = temf_cont_mfds['CLDFRA'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:42:13.179566Z",
     "start_time": "2018-08-17T19:42:10.587780Z"
    }
   },
   "outputs": [],
   "source": [
    "temf_rainc_400 = temf_cont_mfds['RAINC'].values\n",
    "temf_rainnc_400 = temf_cont_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## phys data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:21:31.674018Z",
     "start_time": "2018-08-17T19:20:25.640902Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/1000_sensitivity/001_with_extras'\n",
    "ysu_file_list = sorted(glob.glob(ysu_root_dir+'/'+'wrfout*'))\n",
    "ysu_ds_list = [netCDF4.Dataset(file) for file in ysu_file_list]\n",
    "\n",
    "ysu_ptot_1000 = numpy.load('DATA_ptot_1000_sensitivity.npy')\n",
    "ysu_geopot_1000 = numpy.load('DATA_geopot_1000_sensitivity.npy')\n",
    "ysu_tk_1000 = numpy.load('DATA_tk_1000_sensitivity.npy')\n",
    "\n",
    "ysu_phys_mfds = xarray.open_mfdataset(ysu_root_dir + '/' + 'wrfout*')\n",
    "ysu_qvapor_1000 = ysu_phys_mfds['QVAPOR'].values\n",
    "ysu_t_1000 = ysu_phys_mfds['T'].values\n",
    "ysu_pblh_1000 = ysu_phys_mfds['PBLH'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:21:36.524994Z",
     "start_time": "2018-08-17T19:21:31.682784Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_cldfra_1000 = ysu_phys_mfds['CLDFRA'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:42:34.950754Z",
     "start_time": "2018-08-17T19:42:32.356341Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_rainc_1000 = ysu_phys_mfds['RAINC'].values\n",
    "ysu_rainnc_1000 = ysu_phys_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:22:42.180675Z",
     "start_time": "2018-08-17T19:21:36.533580Z"
    }
   },
   "outputs": [],
   "source": [
    "acm2_root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/1000_sensitivity/001_ACM2_scheme'\n",
    "acm2_file_list = sorted(glob.glob(acm2_root_dir+'/'+'wrfout*'))\n",
    "acm2_ds_list = [netCDF4.Dataset(file) for file in acm2_file_list]\n",
    "\n",
    "acm2_ptot_1000 = wrf.getvar(acm2_ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "acm2_geopot_1000 = wrf.getvar(acm2_ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "acm2_tk_1000 = wrf.getvar(acm2_ds_list, 'tk', timeidx=wrf.ALL_TIMES).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:23:10.856730Z",
     "start_time": "2018-08-17T19:22:42.189179Z"
    }
   },
   "outputs": [],
   "source": [
    "acm2_phys_mfds = xarray.open_mfdataset(acm2_root_dir + '/' + 'wrfout*')\n",
    "acm2_qvapor_1000 = acm2_phys_mfds['QVAPOR'].values\n",
    "acm2_t_1000 = acm2_phys_mfds['T'].values\n",
    "acm2_pblh_1000 = acm2_phys_mfds['PBLH'].values\n",
    "acm2_cldfra_1000 = acm2_phys_mfds['CLDFRA'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:42:45.055219Z",
     "start_time": "2018-08-17T19:42:42.537082Z"
    }
   },
   "outputs": [],
   "source": [
    "acm2_rainc_1000 = acm2_phys_mfds['RAINC'].values\n",
    "acm2_rainnc_1000 = acm2_phys_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:24:10.957543Z",
     "start_time": "2018-08-17T19:23:10.865149Z"
    }
   },
   "outputs": [],
   "source": [
    "myj_root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/1000_sensitivity/001_MYJ_scheme'\n",
    "myj_file_list = sorted(glob.glob(myj_root_dir+'/'+'wrfout*'))\n",
    "myj_ds_list = [netCDF4.Dataset(file) for file in myj_file_list]\n",
    "\n",
    "myj_ptot_1000 = wrf.getvar(myj_ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "myj_geopot_1000 = wrf.getvar(myj_ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "myj_tk_1000 = wrf.getvar(myj_ds_list, 'tk', timeidx=wrf.ALL_TIMES).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:24:40.989267Z",
     "start_time": "2018-08-17T19:24:10.966202Z"
    }
   },
   "outputs": [],
   "source": [
    "myj_phys_mfds = xarray.open_mfdataset(myj_root_dir + '/' + 'wrfout*')\n",
    "myj_qvapor_1000 = myj_phys_mfds['QVAPOR'].values\n",
    "myj_t_1000 = myj_phys_mfds['T'].values\n",
    "myj_pblh_1000 = myj_phys_mfds['PBLH'].values\n",
    "myj_cldfra_1000 = myj_phys_mfds['CLDFRA'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:42:53.615548Z",
     "start_time": "2018-08-17T19:42:51.022196Z"
    }
   },
   "outputs": [],
   "source": [
    "myj_rainc_1000 = myj_phys_mfds['RAINC'].values\n",
    "myj_rainnc_1000 = myj_phys_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:25:37.402088Z",
     "start_time": "2018-08-17T19:24:40.997842Z"
    }
   },
   "outputs": [],
   "source": [
    "uw_root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/1000_sensitivity/001_UW_scheme'\n",
    "uw_file_list = sorted(glob.glob(uw_root_dir+'/'+'wrfout*'))\n",
    "uw_ds_list = [netCDF4.Dataset(file) for file in uw_file_list]\n",
    "\n",
    "uw_ptot_1000 = wrf.getvar(uw_ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "uw_geopot_1000 = wrf.getvar(uw_ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "uw_tk_1000 = wrf.getvar(uw_ds_list, 'tk', timeidx=wrf.ALL_TIMES).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:26:04.454229Z",
     "start_time": "2018-08-17T19:25:37.410683Z"
    }
   },
   "outputs": [],
   "source": [
    "uw_phys_mfds = xarray.open_mfdataset(uw_root_dir + '/' + 'wrfout*')\n",
    "uw_qvapor_1000 = uw_phys_mfds['QVAPOR'].values\n",
    "uw_t_1000 = uw_phys_mfds['T'].values\n",
    "uw_pblh_1000 = uw_phys_mfds['PBLH'].values\n",
    "uw_cldfra_1000 = uw_phys_mfds['CLDFRA'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:43:02.578229Z",
     "start_time": "2018-08-17T19:42:59.717851Z"
    }
   },
   "outputs": [],
   "source": [
    "uw_rainc_1000 = uw_phys_mfds['RAINC'].values\n",
    "uw_rainnc_1000 = uw_phys_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:27:11.236615Z",
     "start_time": "2018-08-17T19:26:04.462715Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn25_root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/1000_sensitivity/001_MYNN25_scheme'\n",
    "mynn25_file_list = sorted(glob.glob(mynn25_root_dir+'/'+'wrfout*'))\n",
    "mynn25_ds_list = [netCDF4.Dataset(file) for file in mynn25_file_list]\n",
    "\n",
    "mynn25_ptot_1000 = wrf.getvar(mynn25_ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "mynn25_geopot_1000 = wrf.getvar(mynn25_ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "mynn25_tk_1000 = wrf.getvar(mynn25_ds_list, 'tk', timeidx=wrf.ALL_TIMES).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:27:44.839035Z",
     "start_time": "2018-08-17T19:27:11.245067Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn25_phys_mfds = xarray.open_mfdataset(mynn25_root_dir + '/' + 'wrfout*')\n",
    "mynn25_qvapor_1000 = mynn25_phys_mfds['QVAPOR'].values\n",
    "mynn25_t_1000 = mynn25_phys_mfds['T'].values\n",
    "mynn25_pblh_1000 = mynn25_phys_mfds['PBLH'].values\n",
    "mynn25_cldfra_1000 = mynn25_phys_mfds['CLDFRA'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:43:12.753610Z",
     "start_time": "2018-08-17T19:43:09.379735Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn25_rainc_1000 = mynn25_phys_mfds['RAINC'].values\n",
    "mynn25_rainnc_1000 = mynn25_phys_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:29:16.311496Z",
     "start_time": "2018-08-17T19:27:44.847468Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn30_root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/1000_sensitivity/001_MYNN30_scheme'\n",
    "mynn30_file_list = sorted(glob.glob(mynn30_root_dir+'/'+'wrfout*'))\n",
    "mynn30_ds_list = [netCDF4.Dataset(file) for file in mynn30_file_list]\n",
    "\n",
    "mynn30_ptot_1000 = wrf.getvar(mynn30_ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "mynn30_geopot_1000 = wrf.getvar(mynn30_ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "mynn30_tk_1000 = wrf.getvar(mynn30_ds_list, 'tk', timeidx=wrf.ALL_TIMES).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:30:07.638195Z",
     "start_time": "2018-08-17T19:29:16.320168Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn30_phys_mfds = xarray.open_mfdataset(mynn30_root_dir + '/' + 'wrfout*')\n",
    "mynn30_qvapor_1000 = mynn30_phys_mfds['QVAPOR'].values\n",
    "mynn30_t_1000 = mynn30_phys_mfds['T'].values\n",
    "mynn30_pblh_1000 = mynn30_phys_mfds['PBLH'].values\n",
    "mynn30_cldfra_1000 = mynn30_phys_mfds['CLDFRA'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:43:28.185925Z",
     "start_time": "2018-08-17T19:43:25.053454Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn30_rainc_1000 = mynn30_phys_mfds['RAINC'].values\n",
    "mynn30_rainnc_1000 = mynn30_phys_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:31:36.570309Z",
     "start_time": "2018-08-17T19:30:07.646876Z"
    }
   },
   "outputs": [],
   "source": [
    "temf_root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/1000_sensitivity/001_TEMF_scheme'\n",
    "temf_file_list = sorted(glob.glob(temf_root_dir+'/'+'wrfout*'))\n",
    "temf_ds_list = [netCDF4.Dataset(file) for file in temf_file_list]\n",
    "\n",
    "temf_ptot_1000 = wrf.getvar(temf_ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "temf_geopot_1000 = wrf.getvar(temf_ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "temf_tk_1000 = wrf.getvar(temf_ds_list, 'tk', timeidx=wrf.ALL_TIMES).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:11.942605Z",
     "start_time": "2018-08-17T19:31:36.578977Z"
    }
   },
   "outputs": [],
   "source": [
    "temf_phys_mfds = xarray.open_mfdataset(temf_root_dir + '/' + 'wrfout*')\n",
    "temf_qvapor_1000 = temf_phys_mfds['QVAPOR'].values\n",
    "temf_t_1000 = temf_phys_mfds['T'].values\n",
    "temf_pblh_1000 = temf_phys_mfds['PBLH'].values\n",
    "temf_cldfra_1000 = temf_phys_mfds['CLDFRA'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:43:35.669772Z",
     "start_time": "2018-08-17T19:43:32.168747Z"
    }
   },
   "outputs": [],
   "source": [
    "temf_rainc_1000 = temf_phys_mfds['RAINC'].values\n",
    "temf_rainnc_1000 = temf_phys_mfds['RAINNC'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define dx, dy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:11.956567Z",
     "start_time": "2018-08-17T19:32:11.951287Z"
    }
   },
   "outputs": [],
   "source": [
    "dx = 50000. # 50,000 m or 50 km\n",
    "dy = 50000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:12.043841Z",
     "start_time": "2018-08-17T19:32:11.965027Z"
    }
   },
   "outputs": [],
   "source": [
    "L_v = 2264705.0 # J/kg\n",
    "c_p = 1005.7 # J/kgK\n",
    "g = 9.80665 # m/s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get landmask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:12.184083Z",
     "start_time": "2018-08-17T19:32:12.052633Z"
    }
   },
   "outputs": [],
   "source": [
    "landmask = ysu_cont_mfds['LANDMASK'].sel(Time=0).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:12.548727Z",
     "start_time": "2018-08-17T19:32:12.192542Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2b6204bcf668>"
      ]
     },
     "execution_count": 35,
     "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(landmask);mp.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:13.150045Z",
     "start_time": "2018-08-17T19:32:12.557075Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_mse_400 = ysu_geopot_400 + L_v*ysu_qvapor_400 + c_p*ysu_tk_400\n",
    "ysu_mse_1000 = ysu_geopot_1000 + L_v*ysu_qvapor_1000 + c_p*ysu_tk_1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "set up time/lev numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:13.163908Z",
     "start_time": "2018-08-17T19:32:13.158476Z"
    }
   },
   "outputs": [],
   "source": [
    "ntime = 25\n",
    "nlev = 29"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculate lat lon stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:13.228876Z",
     "start_time": "2018-08-17T19:32:13.172248Z"
    }
   },
   "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": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:13.295137Z",
     "start_time": "2018-08-17T19:32:13.237641Z"
    }
   },
   "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": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:13.354450Z",
     "start_time": "2018-08-17T19:32:13.303865Z"
    }
   },
   "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": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:13.416069Z",
     "start_time": "2018-08-17T19:32:13.362816Z"
    }
   },
   "outputs": [],
   "source": [
    "lon_indices = (ysu_xlong>=lon_lo)&(ysu_xlong<=lon_hi)\n",
    "lat_indices = (ysu_xlat>=lat_lo)&(ysu_xlat<=lat_hi)\n",
    "latlon_indices = (lon_indices)&(lat_indices)&(landmask_indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# take latlon means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pblh, deltaysu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:13.656637Z",
     "start_time": "2018-08-17T19:32:13.418865Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_pblh_400_latlon_mean = numpy.nanmean(ysu_pblh_400[:,latlon_indices], axis=1)\n",
    "ysu_pblh_1000_latlon_mean = numpy.nanmean(ysu_pblh_1000[:,latlon_indices], axis=1)\n",
    "\n",
    "ysu_qvapor_400_latlon_mean = numpy.nanmean(ysu_qvapor_400[:,:,latlon_indices], axis=2)\n",
    "ysu_qvapor_1000_latlon_mean = numpy.nanmean(ysu_qvapor_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "ysu_tk_400_latlon_mean = numpy.nanmean(ysu_tk_400[:,:,latlon_indices], axis=2)\n",
    "ysu_tk_1000_latlon_mean = numpy.nanmean(ysu_tk_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "ysu_cldfra_400_latlon_mean = numpy.nanmean(ysu_cldfra_400[:,:,latlon_indices], axis=2)\n",
    "ysu_cldfra_1000_latlon_mean = numpy.nanmean(ysu_cldfra_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:45:02.575358Z",
     "start_time": "2018-08-17T19:45:02.559639Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_raintot_400 = ysu_rainc_400 + ysu_rainnc_400\n",
    "ysu_raintot_1000 = ysu_rainc_1000 + ysu_rainnc_1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:13.723894Z",
     "start_time": "2018-08-17T19:32:13.665094Z"
    }
   },
   "outputs": [],
   "source": [
    "myj_pblh_400_latlon_mean = numpy.nanmean(myj_pblh_400[:,latlon_indices], axis=1)\n",
    "myj_pblh_1000_latlon_mean = numpy.nanmean(myj_pblh_1000[:,latlon_indices], axis=1)\n",
    "\n",
    "myj_qvapor_400_latlon_mean = numpy.nanmean(myj_qvapor_400[:,:,latlon_indices], axis=2)\n",
    "myj_qvapor_1000_latlon_mean = numpy.nanmean(myj_qvapor_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "myj_tk_400_latlon_mean = numpy.nanmean(myj_tk_400[:,:,latlon_indices], axis=2)\n",
    "myj_tk_1000_latlon_mean = numpy.nanmean(myj_tk_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "myj_cldfra_400_latlon_mean = numpy.nanmean(myj_cldfra_400[:,:,latlon_indices], axis=2)\n",
    "myj_cldfra_1000_latlon_mean = numpy.nanmean(myj_cldfra_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:45:10.068178Z",
     "start_time": "2018-08-17T19:45:10.049592Z"
    }
   },
   "outputs": [],
   "source": [
    "myj_raintot_400 = myj_rainc_400 + myj_rainnc_400\n",
    "myj_raintot_1000 = myj_rainc_1000 + myj_rainnc_1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:13.809174Z",
     "start_time": "2018-08-17T19:32:13.732447Z"
    }
   },
   "outputs": [],
   "source": [
    "acm2_pblh_400_latlon_mean = numpy.nanmean(acm2_pblh_400[:,latlon_indices], axis=1)\n",
    "acm2_pblh_1000_latlon_mean = numpy.nanmean(acm2_pblh_1000[:,latlon_indices], axis=1)\n",
    "\n",
    "acm2_qvapor_400_latlon_mean = numpy.nanmean(acm2_qvapor_400[:,:,latlon_indices], axis=2)\n",
    "acm2_qvapor_1000_latlon_mean = numpy.nanmean(acm2_qvapor_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "acm2_tk_400_latlon_mean = numpy.nanmean(acm2_tk_400[:,:,latlon_indices], axis=2)\n",
    "acm2_tk_1000_latlon_mean = numpy.nanmean(acm2_tk_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "acm2_cldfra_400_latlon_mean = numpy.nanmean(acm2_cldfra_400[:,:,latlon_indices], axis=2)\n",
    "acm2_cldfra_1000_latlon_mean = numpy.nanmean(acm2_cldfra_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:45:16.662517Z",
     "start_time": "2018-08-17T19:45:16.644812Z"
    }
   },
   "outputs": [],
   "source": [
    "acm2_raintot_400 = acm2_rainc_400 + acm2_rainnc_400\n",
    "acm2_raintot_1000 = acm2_rainc_1000 + acm2_rainnc_1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:13.892024Z",
     "start_time": "2018-08-17T19:32:13.817970Z"
    }
   },
   "outputs": [],
   "source": [
    "uw_pblh_400_latlon_mean = numpy.nanmean(uw_pblh_400[:,latlon_indices], axis=1)\n",
    "uw_pblh_1000_latlon_mean = numpy.nanmean(uw_pblh_1000[:,latlon_indices], axis=1)\n",
    "\n",
    "uw_qvapor_400_latlon_mean = numpy.nanmean(uw_qvapor_400[:,:,latlon_indices], axis=2)\n",
    "uw_qvapor_1000_latlon_mean = numpy.nanmean(uw_qvapor_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "uw_tk_400_latlon_mean = numpy.nanmean(uw_tk_400[:,:,latlon_indices], axis=2)\n",
    "uw_tk_1000_latlon_mean = numpy.nanmean(uw_tk_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "uw_cldfra_400_latlon_mean = numpy.nanmean(uw_cldfra_400[:,:,latlon_indices], axis=2)\n",
    "uw_cldfra_1000_latlon_mean = numpy.nanmean(uw_cldfra_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:45:23.419095Z",
     "start_time": "2018-08-17T19:45:23.400626Z"
    }
   },
   "outputs": [],
   "source": [
    "uw_raintot_400 = uw_rainc_400 + uw_rainnc_400\n",
    "uw_raintot_1000 = uw_rainc_1000 + uw_rainnc_1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:13.968030Z",
     "start_time": "2018-08-17T19:32:13.900619Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn25_pblh_400_latlon_mean = numpy.nanmean(mynn25_pblh_400[:,latlon_indices], axis=1)\n",
    "mynn25_pblh_1000_latlon_mean = numpy.nanmean(mynn25_pblh_1000[:,latlon_indices], axis=1)\n",
    "\n",
    "mynn25_qvapor_400_latlon_mean = numpy.nanmean(mynn25_qvapor_400[:,:,latlon_indices], axis=2)\n",
    "mynn25_qvapor_1000_latlon_mean = numpy.nanmean(mynn25_qvapor_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "mynn25_tk_400_latlon_mean = numpy.nanmean(mynn25_tk_400[:,:,latlon_indices], axis=2)\n",
    "mynn25_tk_1000_latlon_mean = numpy.nanmean(mynn25_tk_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "mynn25_cldfra_400_latlon_mean = numpy.nanmean(mynn25_cldfra_400[:,:,latlon_indices], axis=2)\n",
    "mynn25_cldfra_1000_latlon_mean = numpy.nanmean(mynn25_cldfra_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:45:30.759565Z",
     "start_time": "2018-08-17T19:45:30.742249Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn25_raintot_400 = mynn25_rainc_400 + mynn25_rainnc_400\n",
    "mynn25_raintot_1000 = mynn25_rainc_1000 + mynn25_rainnc_1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:14.043458Z",
     "start_time": "2018-08-17T19:32:13.976670Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn30_pblh_400_latlon_mean = numpy.nanmean(mynn30_pblh_400[:,latlon_indices], axis=1)\n",
    "mynn30_pblh_1000_latlon_mean = numpy.nanmean(mynn30_pblh_1000[:,latlon_indices], axis=1)\n",
    "\n",
    "mynn30_qvapor_400_latlon_mean = numpy.nanmean(mynn30_qvapor_400[:,:,latlon_indices], axis=2)\n",
    "mynn30_qvapor_1000_latlon_mean = numpy.nanmean(mynn30_qvapor_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "mynn30_tk_400_latlon_mean = numpy.nanmean(mynn30_tk_400[:,:,latlon_indices], axis=2)\n",
    "mynn30_tk_1000_latlon_mean = numpy.nanmean(mynn30_tk_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "mynn30_cldfra_400_latlon_mean = numpy.nanmean(mynn30_cldfra_400[:,:,latlon_indices], axis=2)\n",
    "mynn30_cldfra_1000_latlon_mean = numpy.nanmean(mynn30_cldfra_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:45:39.318839Z",
     "start_time": "2018-08-17T19:45:39.301336Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn30_raintot_400 = mynn30_rainc_400 + mynn30_rainnc_400\n",
    "mynn30_raintot_1000 = mynn30_rainc_1000 + mynn30_rainnc_1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:14.160572Z",
     "start_time": "2018-08-17T19:32:14.051881Z"
    }
   },
   "outputs": [],
   "source": [
    "temf_pblh_400_latlon_mean = numpy.nanmean(temf_pblh_400[:,latlon_indices], axis=1)\n",
    "temf_pblh_1000_latlon_mean = numpy.nanmean(temf_pblh_1000[:,latlon_indices], axis=1)\n",
    "\n",
    "temf_qvapor_400_latlon_mean = numpy.nanmean(temf_qvapor_400[:,:,latlon_indices], axis=2)\n",
    "temf_qvapor_1000_latlon_mean = numpy.nanmean(temf_qvapor_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "temf_tk_400_latlon_mean = numpy.nanmean(temf_tk_400[:,:,latlon_indices], axis=2)\n",
    "temf_tk_1000_latlon_mean = numpy.nanmean(temf_tk_1000[:,:,latlon_indices], axis=2)\n",
    "\n",
    "temf_cldfra_400_latlon_mean = numpy.nanmean(temf_cldfra_400[:,:,latlon_indices], axis=2)\n",
    "temf_cldfra_1000_latlon_mean = numpy.nanmean(temf_cldfra_1000[:,:,latlon_indices], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:45:54.768328Z",
     "start_time": "2018-08-17T19:45:54.750137Z"
    }
   },
   "outputs": [],
   "source": [
    "temf_raintot_400 = temf_rainc_400 + temf_rainnc_400\n",
    "temf_raintot_1000 = temf_rainc_1000 + temf_rainnc_1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "raintot time means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-18T01:12:05.022120Z",
     "start_time": "2018-08-18T01:12:04.992288Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_raintot_400_hourly = numpy.diff(ysu_raintot_400, axis=0)\n",
    "ysu_raintot_1000_hourly = numpy.diff(ysu_raintot_1000, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-18T01:12:17.845578Z",
     "start_time": "2018-08-18T01:12:17.822531Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_raintot_hourly_diffs = ysu_raintot_1000_hourly-ysu_raintot_400_hourly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-18T01:12:36.010558Z",
     "start_time": "2018-08-18T01:12:35.971496Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_raintot_hourly_diffs_timemean = numpy.nanmean(ysu_raintot_hourly_diffs, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-18T01:14:07.775354Z",
     "start_time": "2018-08-18T01:14:07.360424Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2b629ebe4860>"
      ]
     },
     "execution_count": 123,
     "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(ysu_raintot_hourly_diffs_timemean*24., cmap=cmocean.cm.balance);mp.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:51:11.490456Z",
     "start_time": "2018-08-17T19:51:11.482363Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_raintot_diffs = ysu_raintot_1000[-1,:,:]-ysu_raintot_400[-1,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:51:52.464217Z",
     "start_time": "2018-08-17T19:51:51.982637Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2b62bd364278>"
      ]
     },
     "execution_count": 108,
     "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.pcolormesh(ysu_raintot_diffs, vmin=-.001,vmax=.001);mp.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:49:24.865741Z",
     "start_time": "2018-08-17T19:49:24.835180Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_raintot_400_time_mean = numpy.nanmean(ysu_raintot_400, axis=0)\n",
    "ysu_raintot_1000_time_mean = numpy.nanmean(ysu_raintot_1000, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:49:45.603914Z",
     "start_time": "2018-08-17T19:49:45.141989Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2b62bc2327f0>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0jsiGzItm0U9rgVuAO0rvfwFCjUHgCuBrLJwAv4mkK4ok+tnhqf59EsdxnEUwU1fHoNDz7SwWkv02cL2ZfZGFE+A3MbNrzGyHme1I10z2ennHcZwl0ed88KuCnrxoJL0O+L+AS83stthcJKi/s0MCfMdxnOOLBTv8MLFkDV5ShVBe6mUl4Q4LJ8B3HMc57gybF00vGvxZBG+Zfwr7rQB8mOBZMycBfl9W6DiO0wdsCDdZuxbwZnZt6e34PMPaE+A7juOsGIbNROORrI7jDA2D5CHTDcP1vOI4ztBi1j83ycUi9yW9WdJDMdr/wti2SdJXJO2SdF3cz0TSb8W2eyT9+9h2hqRHYts9kt7ey2d2Ae84ztDQRzfJeSP3JZ0EXAmcB7ySsEcJcBVwg5ltAw4Ar49j3x7H7gB+W9LWOO/HzOzceHygl8/rAt5xnKHBrLujCy4mRu4DNwEvK/VdCNxqZgfN7CFgj6RzgIuAT8Qx1xOE/xnA/zCz/WZ2GPg+8BxC4Ogjx/p5XcA7jjMUGCLPk64OYHMRcR+PK9qmWyhyv9kXeQzYAmw0swPlNjP7FzP7fQBJLwR+GriLIPjfJOluSX8Xtfol45usjuMMDUtwotlrZjsWmWq+yP32PgMyoN6hDQBJbwP+D+DNZrZP0v3A+4C/J6SD+Uvgl7tffsAFvOM4w4H11Ytmocj93cALSu+3AruAKUkTZnak1IakawjxRS8xs93xnH8ws0Ox/68JNv0l4yYaZ3gxhSO+Tmqa2+YMHtblsTgLRe5/DniVpPFoe19jZruAzwCXxjFvBG6V9CLgxcD/UhLuAJ+W9Evx9SVAOWtA17gG7wwvir/kKNDzkVJbt5iWfo5z3OijBn8zbZH7kt4LfNPMbpL0EeBOYBZ4Uzzn3cANkt5JuCHcSAgOPRO4q5QZ4A3AO4CPSfoz4OHSHEvCBbzjyI5Nay+EfFnYLyb4lzLW6QsG5Hl/BHz0nmmP3P+DUv/VwNVt5zwFXNB2zl/FoxMvOcZluoB3nCZLEbIloWyJobwLgd7pGuWbw1LX4CwNY+jMby7gHccZGoYtF41vsjoO9Kw5JzMiH89BhiVg8RelBi3b/qiF+Uua+toHRbYmQ1kYVzmsYzcVOYvTv03WVYFr8M7wciy273hePmakRxIwBWFdmG1Kv6xkOqE9xfih7UZ6sIKlMLpXzJyUY6M5yYzrXMvHYJXj6wb/a3KGlz7au5Nay93yqPkTY+PZT8/V4mWQhP7aZmN8TwJVQ7NzXTfnMGTCaVkYMg2+awEv6RJJ74uvXxJDaB+S9LuxbcHsao4zcJSeALJopmkK9babx777N7V87Dt4z8ycaCSH0qD5l001Zb9834A9NgwsV1fHoLCogI+C+4PANaXmjwKvBZ4HvE7SdhbIruY4K45jDWgqhPOJNaA72Vs92KaZd7oZJEZSE/lI2w0Dgq3ftfhjRF0eg0G3NvgvFC8knQLIzO6I728GXgG8iJhdTdJNwJ/2e7GO4zjHxJA9BC2qwVvgFuCO2DRfprSFsqs1kXRFkaEtOzx1rOt3nN5o0457IheWdf8kUNs2G7xl4OhzSmvJxiz8MuMYZcE7RzNJ8M7JPZ1Cz7gNflHmy5S2UHa11mCza8xsh5ntSNdM9nB5x+kPttS//jl2ccjHcvJaSl6xo8d0ODc5UKGxprONvoyM4E0TbfGWQDotklmRjxgIxp4SebrA9ZyjKQKdujkGhF4EfJFFraDIitZs75BdzXFWFPmIoWyJP+RS5CpG8IIRJNGXfV7vl9K5Cz45mFBdVA4n5OMZasR5EqOxPsOqRlIXqsPsOkganuJgqfSx4MeqYMkCPmY8q0g6W9IkYXP18yycXc1xVhSqd5laoP01oEbweU+OpKSHKuTVNq28Q8BS82khW1hTzEeM5NxDVPZXgrYerz+6pxLOzUPgVGOy9HA8QBrnspOru2NA6DXQ6bcJmdAmgfeY2eNxs3VOdrU+rdFx+ouJtBazR0JnQd+WabLwmsmrhhLIN2RoJENPjzDyTEp9fY7qCrK9+FWV3CCVh5tKWhONyZI/fJw/m8xIp1JkUNs9SSpDOZADIY6KpBHWbKmRziSoLqwyN6jKWZhhe+Dp+k/DzK4tvf4GcxPaz5ddzXEcZ2UwYBuo3eCRrM5Q0sz9voBKl4/lJLOQT2SMPq2gRc8mKBOVJ6vo6RHSIwl5BTAYOeMwVjGyNRnp9NH2eKtAY9LI1zWCfb24voyxxyuoHsYndZFXS+caNNZac81JPQmeNRXDqn3wBhoautxgHSCTlwt4Z/joJBDbbeYC6gnZmDH6RIXtv7AT5Wp6zGQTOdlkRmMyJx/PSepi9pE1jD2ZBNt8bf5rV5+oko2GtAR5Gprr63OsCpw4E2z6CoFR1cNqpjQo2/gbE+ammV4YMjdJ/xNxhoP2xGLtQl5GngaPmDyFtCYsDdkilcFd951OkhhJFpKDVafE9ElBP6qvzSE10hlR2xQ8bGY3tGnW8fojTyfMbgrzJHWRj1roiht7+VQ1yHMZsxutleNGRj5qzbKCSTY33UGxdmcRjnLcHmxcg3cGHwveJ/M9flsCZOL5P/YwIXVvQl41LA2a9MyWjPRQ2hw3u9E4tD2nMZmTjRuVIwouixA2Ya103YLY+NJf+i7ZmgZJTTTWBGlTmGuyNQ0YLaUj2DBLtjajeiD8TCe3HCbfUG/N3Z77ZoBMC8uC+8E7juMMLqVtjwWPRedZJLmipDfHZIz3Sbowtm2S9BVJuyRdJ6my1LFLxQW8M/gohv7P8+tVFtwbf/CdM0lmRDaRh6GZwkZmxUgakMyKbG2GVYIZRrlQA7JxwxIjWxPOWyia9QsPnE0yndJYl4fz83DtbCKnsq8CUyn5xlkwkc9UIBf1dYbqYuaBdST7Ro42/Zg86Klb+meDnze5oqSTgCuB84BXAh+OXVcBN5jZNuAA8PqljO3h07qAd5xkNvw/r+bkY9a0h2PB5JJOpeSjwS89mU5QIwr+xEJgUk3YWA4GlYNtP6ko7C0BO6HGz571YPBvH8lRPdjzs/FQ6MMqMLYnZfThUfKKhUIhSbgp5SOGpa05k9mWaadg5JlkoMwLK5yLickVgZuAl5X6LgRuNbODZvYQsEfSOcBFwCfimOsJAn0pY5eMC3hnOJjPc8ZEY10eNlTrSbS7B3t2svVIEPYWBXTVmlo9ucLr9Q0sgWQqBQuJwoqN2dGn1bxM5XAC+0b4zt++gLNurJHsr5A0xOx6CzeMNDwVTJ9eZ+1DBmsazRKASS1cM6+0bAh5laMiaGdOyVyLX4QlmGg2F0kR43FF21QLJVecLyHjRjM70Na2lLFLxr1onOGk2KQ0kUwHwa5MVA8kNNbmQTt/bIJ8fYPkUBrMOFlrE1V5PA5WqEyHTdzaupy8oaChA7UToinHoL4uQ7mYPgkefM1o2FTdMkN+YCQ8FeSCBNKDFfa+yBh5dITGhJFPBJfI0b0hWraVS77leRM+DyS10mcDF/btGEtJQ7DXzHYsMtt8yRXb+4yQkLHeoW0pY5eMa/DO8FJ41yja4XNorAkatXKFRGIHK82i2dmmOjKo7g8C2dIg5Otrc2Y35mg62FBCel+LRTtAsyI9kjSDn5SLpCa0e4zKoQSrWPMc5TD6dEJjIrxO9wVNv74+yo4O+wgj+8Xo02rVc20fM4DeIT3TPxv8QskV50vIOCVpoq1tKWOXjAt4x3GGhn550bBwcsXPAa+SNB7t6WvMbBfwGeDSOOaNcY6ljF0yLuCd4SNqsoVtu/h/Y114CraUOb/2pmY8FSyatc0ZnDSDGiGdgaXBF16xyp7FVO7EgKXGpgYkIQtkNh6kh1VaWSHXb9/ftKdnJ80Gr5k8eu2syahMhTzwppAnXvWWCcYSY2Z7jSNn1Wmsy5rBUM3i3UXRkOKBf9i1+P5p8DcD9Zhc8deB/yjpvZJ+xcyeAj4C3EnYgP2teM67gcsl7SSYZW5cythePq7b4J2hJR8xLIHG2gxSo3IwDel4x4x0WmRjFj1VoHogob7eUB0SEzw5FvKzT6fRywUQwfNmJCeZTUKBjtGc9FAF1YtiHUZ6JG7mjgRPmP2PrCfJReWQqG8KEbHF2s79v/dz/6+fQDKdkI+EaFZL4rqS4GKpZ0bIx3JGnwp2+iJLZl4xkoaoTIlzfu5B7vnn7WSTQxbK2U6ftiXmSa74B6X+q4Gr2855Crigw1xdj10qrsE7w4tCuoBi401Z8GkPQprmxqelxuzGoKlnawo1ndaGXaboFz937nwiI5lNsMSon9AgmQneMo01OTZiLS07J2j663LsYLXpEpnOwn1XnBByzsSnBBTdOFMjr+YoC+eqLmpbGs0bQCHILAnXu+dr26mfMjv38w+ZNt+teWaQ9qZdwDvDRWGyaEDl1CmS2WBaSY6kNCZzkqL+aSP4xRcCtQh8GnsiDflgRLhBzAJpSGuQ1EQ2mZHU488qD8K7esqR4ApZDa6YIwcSsomQXKwyJUhbsjaZCWkSVA8JyJAx9lQw1eSj0byTGNlkRuVwiqUx1YKBZhKq+9LoYROPWGAkGzd0qEJ6JPrK52JkfwcBP+hCf8gKfvQk4EthuvdIukvSyyVtl/TtGFr7gX4v1HH6QlTRsrUZ2cOTIWo1mmEKYZhkkI3nTRdK1VtBRTOnZE2vGhT84U2ENMFjRnIkbV4qmUmwqpHvnCSpJU3VcHZTyG2DjMakMfFISjqdkE2GKFkZnPrjjzP2REK+rsH0yTmVZyrk4zmVwwlJTaQHYyWp0pNDERGruko+8sFUo4xmTdeg9ROeSkqyLC/2HhYqPbjKGTYNvlcb/C8AW8zsXEnPBz4F/BB4F3ALcIukl5vZl/q0TsdxnGNngIR3N/RqosmB8ej/uRE4DLwYuCVuPnyaHkNrHWfZMTVTAyj6wVsSNOe8EjZBk9mEJIO0BhY9WMiCd0o+ljfT/OZxIzadToL211CwgRNs90UAVZHJMB/JSWoJSSPMZ4nRGIPGZMhFk5wU3Kl333EKs+uBmZRkVow+E54UstGw0Uoas1YWpp168LU3Qbau0WxXPRTntsKLZzIPmTKTYLdX3spJX4wrUhM3heGgaPJug++aLxEE+5PAV4D/AuyLwh2OIbTWcZaVpttg3LBshIRhiul+bSyjviVsRuYVo74hD3brhOavRbNJM/oVgukDoi08gXQmXiNuxJqAxEiPtOy7+WgQ0MmJNWZPzKhMhWvYY+OoHjxfssm8aVaZOdmo7E/JxvNmBGs2lpduJmGDWA2RHEmjDV7kMXUCMYjKUsNGjKTeEuZJFr6XZu55IDmShJvfoAj3gv65Sa4KejXRXEEwyfwMcC7wTeDeUr8xT2htzOlwBUC6cWOPl3ecHlHhhx7s7WqEZF+2tgH1BNWC9m6pkcwmoCA4g8eKGNkfokytajCeQ0PkY1A5FHPUAI31GenhlGzcSGqtG0E2EYWpQl6ZBLC9oyRZeCooCn9kI8FVstD6m+X5xmht4EJz8xfCEwgCWciREzaBg1DPx1vuPcUma7HWyqGkeXNoTIbKVNm6BppJmjnuBwkNmZdorxr8+YRUlg0zuwv4fmwrmDe01syuMbMdZrYjXTPZ4+Ud5xgoqiCNZ4wciBr3TEp6KA0CNApLRDP5WLopaPX19SExGSYsU/RvD943hXaeHkpRQ4zsSyEJGjNZ1NyrwTRCEsYXrpPZmiy4bFp4IpjdGiKT8pE8aPZrGyQntqLhLYmbwpQ8cArvnqRlgsnHciqHktbnbttAbUyGzWFLjclHwmdXbTCF+zDSq4C/k5DOEkmnAuuAW6M3TQq8jh5Dax3HcZaNITPR9Crg/4qQHvNe4B+B3wTeDvw58ADwNTP7Vn+W6Dh9Jto1kpmEmZOMxvqMZDqhsT4k9VP0jW+6IJqwJ0eDG+Jk1oxYLaJL1RCzG0JhEEuD+SakIbCWbbuw19djkrKoXedjsX0qDWaT6HKpw2nrKSIx0r1VRu6aaAY2FWkQmmkRFCJzmx8xLyJnixTGCdmaBmNPKaQtsLCGbG0juFDWxNRZGfmGRjDjQHPzdWAYwk3WnmzwZlbn6DBdCJ40jrOqSKbTUDT7cCV6kNBMCWwoCtlgEkkPh59MOqOQebKUwTcjU7RXAAAgAElEQVSZUdPjJqkHX/rK4ZR8LAyqTIVzktlQj7XpG2+lVMRYyyc/BiqpDgimT8uYeDhlemvLzFPOQtleA1YGmk5aN6tawpGtGel0yGyZjRsjT1aBcBNIpxJ0MNjk8xFrbh4PFAMkvLvBI1mdwaRb7w/FKNSZpBm4FGzhCgJOMP5YsXFpTa+VbCIPmrkp2PBl4ddk0fXw5NqczUwIaYVDf9wslbHuviQmLIvJxSbysDFaCa6QKNrw4xPDzEll98UQcdv8vGXhVdjbc5GPhopQlamEpJ6Q1oKHDgobv5Vpgp2/EnLqWEpwhB5E3ETjOANClznQlUP9hHp0naRZc9XSINCPnJaTj7aiPvOxPLg/xufffCRcK6+GHPDKQHtHj/LYKNwbq4eD1p3UxMGz85CThiBY00Mhb0FSZIy0lltlUfUp1HIFLLSHG1GUSqVIVGXhScJi6oJ81BjZH54eyt9LUoububGtMiUmHk3J1mQD5SYpaKVwWOQYFFzAO4PJUgyqJtKpNAjsQkAmoSxeUguafDKTNKdKp1quj+VryAiulYQiHPlEFm4U0WbfTO+7OQh38uCFIyuuSzPYqunaGNMVK491YKtRq69acy8gmwyBVyQt4V7Y57MxI5/IUCPY5Gc35nPGIGP61JzKoRQ1oHpQ1NfmzJxspIcHzAjvNnjHcZwBZoCEdze4Bu8MLPnaLspYxg1OrCh83TJ3BI+YaJMmBAlZApXDUZsua8wQ7N3VHKsYs+tD4rF8JGx2Wmoh+nU0Z2J32IzNR4Imbs1N3Gjbz2KkbTQxFVkkk3oIzGqV4GttxiYzYW1A8ylkbG94OkgPVWhsyFqbpjnNvQTFdMmNLbWmySmUK5z7XQwMboN3nMEgOdSFiUEh9/rIPmHr6yGdACW5HTdh1QjmDhvNqa8LUa75ZBYyTcYAJauEjUzNttwim9kci7QBR1KOnGZUDidzIkyVtdwnVRc22laDtXzTKdITVIy8as0N26LYCCYqh0VtQyGkYeTpWC82pkGoHkpaNw9Bsm+E6kGRnTM1eEK9xLCZaFzAO8NN9FOvrzVGHxoNQjOl6aqY1MKGZ2HjLpfLS6ZSZNDY2GDdD4VVY9GOKi2tuxY8cIoiHJYa44+JRswbg0UbfE5wezSwqpEeSOdsmBbIWhq2LLhrWmJUDgY7fTqdBLt89K/Px8NTTGPSgrdOBRrrchqTrcIgScydU19j5I9PzLXRDxquwTvOEBEzJ2ZjRmPcaKyNJpIiy2Qajmau+NlW2H8h+NNDFQ6fTgiQimaQ0SeD8E/qYVzh+SKD6VODGUexkhMEoU4eg4tKQr1IOTC27dDRgtdCWcHK4SQUB4FmsFU2FjTz6jPRt78a3CJH94bPUT0Y19QITxZNN89BU2HL2PJ60UgalXRjrInxWUnrO4y5KvbfKem82Naxloak90h6RNLdkn45tv2spIdiLY57JF3afo0yLuAdB0Ilp5GQHKxs4y782IOdPtquo4kkr0a7emxTFoV5AxpnHwFCEi9TOL/IYaNG9LAhvp+NfvZJWAeAhfijkHxszJh+ZO1cwRvt40lWkslR47fRcCdJ6iKLaY2TRiheUts8t1pUIdSbxT4GneXV4N8K7DSzbcAXCdH9TSSdD7wK2E4IFP1Q7Ho/oZbGGcA5MeXLiwkp158DvBz4gKSR+P6PzOzceFy/0IJcwDuOMzQssw3+YuDa+Pp6jq6JcTHwyZik8TbgdEmTdK6lcTrwcTObNbM9wDPAycA24JFuF+QC3nFKJo85v/DCS6bkd65cYdO0AeN7Qm3V6oGEvErLpFM18qfGmucGL5p4nTx4zBQpD/JKKMLRrjU2PXqgqaW3m28gPEU0Jkq2+ug3n9STUEA8tWaAVfPznTYTgpigGQk7SME9C9K9Br9Z0u2l44ouZt8GPBpfd6qJUe4H2ANspkMtDTP7n2b2FwCS/jWh/sZugpb/h5J+IOlvJK1baEEu4B2noNjULARhkYs9piBopjWIlY/qk0EQz55RI50uCofEOKlZNT1aCjfL5oZq3NyEILzT6ZB3Hmim9i0EriUGmTjx9uCZM7rtUJDl8SaU1NVMD1yQV4x0uk2ol9IEb7/sO6SHKmFtldA2sr9kmhpUuhXu4avcW6Q1j8c1XV6hUXrd7qdb7i+vqL0tA5BUkfRu4GrgUjPLgW8D/yfwo4SU7H+00II80MlxOtEU6DEtQB7s4kVOmnzEmrln0qdGgrZsNDVmS6FyMKWxNiu5MQb3xcaalm1/zg0gh8Yam+ODrlxUD4mnXhTs/bVda0P6gVLmSExYNUczCaTBbTKbyEMO+qjCjT8hsrFQaPu+v3oJ6VTr+k2b/CD6vZco3RePfS7pKqB9g/N5hFoY99K5Jsbu2F6wiaDFn1Bq2wrskpQAfw/sA15oZvtj/9+Y2cG4hr+hZcfviGvwjlOm3RDbNNfQMqPEhGHFJmc2njeLhBTatbKYkbGeBA+WyPhT0FiXBXNL1noqIHrcTDxa8pSJ2nR9fWljtxJuLHl1rqQqe/c0P0oWx5uYPsmYOWs23JAOl/S6ZjKzwRbuBf2ywZvZH5vZC8oHcCVwWRxyOUfXxLgVuFRSIukC4H4zqwHf7VBL45eBWTO7rCTcAW6Lm7UAlwC3LbRO1+AdxxkelvcedjXwKUkPEIoivRZA0ieAD5vZNyV9lVDudD/wb+N5VwKfImjy15rZtyT9KvCvJN1Tmv9CgqfOJ+PN4C7gzQstyAW843SirNEWtvg0BA4lUyk2asEiczghyYNfeX190IY1kzRT7loSbPaFZn3g7LxZF5U0FuxohDQJjVNmObwhRbUQtJRPZCRHov0mE/l4yCGfVy2UB7Q2bT9trTcfz0ksDdeWhSjap6thTHmfgZAdM5kZkof5ZRTwZjZN0Krb299Qev0ugktkuf9+2mppmNkfAn/Y4TK7gRd0u6ae/1UlXSFpp6T7JF0s6SXRIf8hSb/b67yOs+KI6QyUh+IgRaWkyoE0+KsrRIqm0zHNQEwFbFWbk3VSjShTy5u49ZBmWAbpnpGYlz6ckkwnqE6zoEdSCz/XpCaqB5OWPaFcZ9UU0i4oesjE/mQ2aQr3ZJbWeBgq4T5sqQp60uAlnQX8NvBjwEnAZ4EDhEeSu4FvSrrJzB7s10Id51ml06+8FFxU2ZdS35C3Cl+nkJs1hUg2HvLHVw+mIdgpMajaHOFePShq22ZJ9oeoppEDYnY9MBKCkSwNScnUCCmLs7gxa1XIRsL86YyCi2bhRWOhfGBRdm/O5ylyy1fmvh8qBkh4d0Ovt+5XE2xFh6MQfwMgM7sjlvO7GXhFvxbpOMedsmpnodydVYJfex5rqRa5ZIK3TELSCBWakpmQFmCOy6JCagRmW5p47cS8mdHR4lNC8LkMOd/LWFL45NNKWlYI8JG8mQueXIw/Ntf9cWh83jswbAU/erXBPxfIJd0GjAJ/y1wH/seAUzqdGAMGrgBIN27s8fKOcxyRYZWgVVvFSGutAtcQTCPZRNDaLYF8TR5SBcyIbMKa7o82YlT3VqhvbrRs7SVNHIVEYPmYHXVzsDSkRrCqNfPfAKRHRJLGNMUKfvrTW49ee/F62Bi2j9yrgB8n+GteQAipvQP4x1J/Jyf/0BECBq4BGN12+pB93Y7jHDdaQUxDQ68Cfh/wLTObAe6XtBt4Ual/K7DzWBfnOCudIg1B0xafGPlkTjqVQJWQSMyCSSWbyFsaZNTGswlDtYSy+ae8eZqP2dEXJEawKmakLHnT1NdZM2HZktL+Dokf/LAJ+F5t8F8ELpKUStpK0Nb3Sjo7Js95NfD5fi3ScVYKaswjNKNQDoU0aNrSSa2VkbLRqs5E3hLkc2y+bfMmMyHlgZoB8EdvjOZjeSgEbgpukeXp8u42Ukf2lypFDShFJKt70SyCmf2jpJ8Evg9MA/8bUANuBCaB95jZ431bpeOsEJopBsqUbOO1k+tUn64GQW6KLo4h8jWvGiNPpaEQ9pg10x7Mqz3LyE6tseZ7Yxw5tYPXSzwvnQrl+vKqhQIgbTb2fGOdZF+1eVpSE/koc8bMbhygncUFUD5A0rsLeg506uSwzxIc8B1nNbD2wYStN+zk3t87Y/5BhYDOQhqAfCT4xNfXtfLQNDZkpAcqNNaENAZFcFMQ8nQW8iZGHxjjyGnz5ImJN4cil83oMwm1kzKq+9KQi6Y4ZzqdM2co09d2wxgGE80Q2uCHJMLBcXrj0Pace3/vDJJaEIaVgx1+MuV8NTLykZzGZMtHHiA9UIn9hNTDSQhuytc35uS4mSN0ZcxuWEAixbEjz4T/n/2yB5u1V8vzzAlkKgvx+V4PMG6icRzHGVQGSHh3g2vwjtMFxebootpd3OisHkxaY0tpe4sxzTQBh9KW73tUH/NY+DuvdjCllOZQA/J1DdJaOPehm7aTVy2YZxZTReOcJjrPP6AMmwbvAt5xlkBR3LojJenQWFOSEuncnDTN/5cEa15s3maicigkCUunkuBtA1QOlbxcLNjvrWokBytMPb8GJqbOzMlHWNwbprRO5UNgey9jXR4Dggt4x3k2MFqujh02OJMs/F85wcOmEZ4alMHo06K+IW+dC3P83zfcNjp3zqUI7CET7sOWqsAFvOO0kU2UfuEdNOFzXriLkWcW+elorsauLOSlyYviGgbkBPNN3jqnmYsmDdr1xPMOMPoMzQIj7SkLkHHw7DYvGxPVA3M1/nbydKgsM4D7wTuO4ww2NkDSuwtcg3ecMqZmQQ5LYlBQW3DTvd/dxuymJT7HK+RzT2pJsMcLlBF+gUVR77KJJb6cenA9+5+fBf/1UvHu9jXPeWKQUTsxC/PPt5xCUx0yNX7YNHgX8I5TpvTrVhaKYzRT/R6rMCzkd1GLtcjL3nbdOfloEmvWW00aYuypOElhg28zzZSvZSnzSiuVzULFaYMu7LvdYHUB7zhDgIzGmpBLJp0uVU2K5BvqS5sv2tFVD9WarCif17TT09K6TSQzCaaQaqBg5sQwduKxDsK4JMyT2UXSIBRLSkv55AdJdZ2H5dxklTQq6UZJuyR9VtL6DmOuiv13Sjovtm2X9O3Y/oHS2Jsl3ROPr8a2TZK+EsdeJ2lBM7sLeMeZh3w8p3pIaG2dbNyoHJ4rVItKTF2TBm05G5/rU696KLFnCWCEm4nB2ofCGqwRnyRKJpfKdNvcJkaeSeYq4Xb0Tan9nNGnk5BPftC198gye9G8FdhpZtsICRnfPufa0vnAq4DtwFuAD8Wu9xPSvpwBnCPp5bF9g5mdG4+fi21XATfEaxwAXr/QglzAO04nZIw+UaFy3gGSPSEz1xzf9h7mUz0GN9XVTEZW9pwpTDiNNTlJHQ6cE0rvpYdDgrLKVMt0s/9HcsaenJvWoLY5b2aPHN+dHPVE0GlNtS2NZr6cgccIm6zdHL1xMXBtfH098MoO/Z80s4aZ3QacHrPvvhi4xcwM+DTwSknxdn8UFwGfWOAac3AB7zjO0LCETdbNkm4vHVd0Mf02WpXtHgO2LNAPsAfYDOyLwr183lbCDeB2SXdIuiT2bzSzAwtcYw7uJuk481Bfn1N/aF0oi1d4nHSr6WaCtDTWxMh+MX1Kzsj+hMa4NQtqWxqmzas5aT1hZOsU9Ucni9PIJzJUNWZVwUZykpmEJIPZ9SEIqnYCIGP9vQn7fyTjzHOe4NHaVipTRZpiaEx2tscnM0OivRd0/1H3mtmOHmZvlF63+zGV+4v3ndoK/6pPAH8KnAR8JZZIrXcYOy+uwTvOfJTqny6Z9nNiZkjlor4+x6ohPYEpuGIm0wlJPcFGjPquNcFNU8GDZ3JnlWR/EO5jjxeRUMHMU5+kub6DzzWShtj1/VPIR8INanadcdLt2dEFQ8r/Hxb7O/1zk4ybpXeVD+B5BM2b+P9dbaftLvUDbCJo8SeU2orz9gDvNbNpM3sY+CahFvaUpIkFrjGHYxLwkhJJ35B00Xw7wY6zaoleKEk9WZr23gkTVombeLOFLT6JPvAhPUExf7YmCzZ7C2uYOSkn39AgmUpD+uCSQK5MBwGfjxh5NW8K/uZaE3jsgqTlktn2GRa00Q8aZijv7lh8KvtjM3tB+QCuBC6LQy4Hbm077Vbg0ig3LwDuN7Ma8F1JL5eUAq+L434FuE6BzcD5hAJLnwEujfO9scM15nCsGvzbCHctmH8n2HFWL/2KfCn5qhfCNpmFUN7P0JZpsjUZJkin0lCCr8BE+lS1mW++fMxsyUhmE9LD4SaUzEK6odbc0J33MyhkrFTdk4310Q/+auCFkh4Afhz4SwBJn5D0E2b2XeCrwA+BD9LysrkS+HPgAeBrZvYtwmbrwdj2ReD3zWwf8G7gckk7CaadGxdaUM82eElnAa8AbgFSwk7wr5qZSfo0YXf3S73O7zgrgqjZTjwqjpwW23oRiDG9r1WC62NSU8wUaWTjOckT4yRFhackFuIuri/DqjTNKWoABtn6jPRQSnpENNYaNpqTZwnsGYMKVA8kIfvlPEnIkoxWUe8hEfLL+THNbBq4pEP7G0qv30VbJTwzu58gP8ttGfDrHeZ6Crig2zUdiwb/F8DvEO538+0EH4WkK4qd6ezw1DFc3nEcZwkYkFt3x4DQk4CX9OvAbWZ2b2yabyf4KMzsGjPbYWY70jWTvVzecZ4diqLWRzQ3rUCv08UCHoX5JB8xUEhBgObWZ1UWtPVyFGtxfauGsZYaE4+J2pbw01NeyoQpa+aur5zWUqRs2N0qltdEs+Lo9Z/7ZcDrJd1D2Ax4N8GVp2DR3V3HWfFEgZqfNc30KccYzt+W4waABFSnoyeLpcaa7fup7InRsmUzi4nG+oz0cMrU6caZN0M+mpNMpaSHSjVZ4zmzz4yFuq0mkhkdPd8Q4cnGusDM3mBmZ5vZucBNwG8A3+qwE+w4q5coBKs/GEeN7hOOzdngLM1VzNH0XJG1PF4K7Tw1sBDtenjn+qDFF7liSsnFJrYcJh/PGX9cPPILKUkteuSkRj6ek4/kjD4dhfqRlNkTcvJNs9iIHZUdc5jolxfNaqGfD2yddoIdZ/USMy3WTsxZu7N7N0KrdhYQyuKcKc1kYnNuHLLgRpnBCT+6l7waKjppJiEfz5o3gg13J0w/spbkcMpzf/GB5lqbbp1HUpLZhNrmfG77vipFzdiBUlO7pVvzzAB9NcccyWpml5fevni+cY7jOMeTEOg0QNK7C4Z9y8VxFiWdSvj2H370mA20heaOWjVXiwRjyJrpgdUQz9v4FMpE/YQGVjEYyUmmE7I1GQeea+iEGlYx7vz2WXPXVDYktxcRcULK5m6OAcFz0TjOAoyffog1Y7Nsv/mKYwz7NiwtbXAWgj1pCeJ8NKQyyMeMr992LgJGd1eYPSGHeoVsIqeyrwKCvJGQWLD3N01CHeq1zuFYo3EHANfgHWfYKbTeTBzZvZZzNj4ZknL1I5w/Ct5QfDtco2hTRmiTYfFy9bXWvK6y4K5plZiLvoiKLdYcz1vwMw0zboN3HKfQgq1ijD6d8NVvnUvSz3QF0IpYrQtTyPrYLLFnwkaCG2Q+GjJOkpS0/g7ztVIh0FlzL78eWi1+sDxkusE1eMeZh8qWaU76doM1D6X90YDn1ExtBS1RmMqLtsSo7KuETJOzaiYdawpmtbT6duGtXK0KTSUbfNk1c6hZ3oIfKw7X4B3HGQ6MYynHtypxDd5x5iF7fJy0Zhw+K+u75muCOR4uqZHHzVIZNNZmwWxTDe3JdKve6pwiHR08aGzk6NBMS/u6/NXLkGnwLuAdp51opx7Zl3DyOx+kunmmf5uURUbIvMgK2Zo3qbdMKkk9wVKoHExIaqKxsRHqrebMTSXcPnf5/+0Mu3kGfJPVcZxAXoVv3v68sMmZltwPe6V8rgX3RjXaPGGKvsRIYmEQq0AylUISEpQl8xVpa7fRd9qEHXKUD5eNxjV4x2knCsgTX/IE1a1HyNY1Wu3HQnvgkelo00nhMmmAoLE2uk+moT3JOsw132dw5mIMXaCTC3jHKVOYSGqi/jcnY/ev4byzH1mea5U9Y3KRHtFcLb4S7eednrPbA5ncz31RhCHr7hgU3ETjOM7wMEDCuxtcg3ecMkUO+FHjqR1GYzLn+98+69jnnU/DLtIUTGRkE0U0q+b0NTdju0xX3PW1h5Fl9KKRNCrpRkm7JH1W0voOY66K/XdKOi+2bZf07dj+gdj2Vkn3lI7dkv5XSWdIeqTU/vb2a5RxAe847ZiaOdNVpNZdDiFZ9qA5krbcGduCmprVpBayq8/X58K9xfLb4N8K7DSzbYRC2XOEr6TzgVcB24G3AB+KXe8n1Gk9AzhH0svN7L+Y2bmx5sZLgZ2E+tdnAh8r+szsAwstyAW845SJ3idbvi6SWrSDQ/82LTvNI5uTVbK5jvZ1lciLzJTtaQg6nLPx+8eg/Q8YyvOujh65GLg2vr4eeGWH/k+aWcPMbgNOlzRJSLN+S6xp/ekO570H+NNY1Hsb0PWmUK81WRNJH4uPFHdI+hlJL5F0t6SHJP1uL/M6zorAxBM/nZNN5lQPJSEx2LHSzQ2iPZXBAu6Oc1wlOwnu2DbyjNj3/D7fpFYtXZpnerfTbwMeja8fA7Ys0A+wB9gM7IvC/ajzJJ0JvAj4+9h0BvCmKGv/TtLWhRbUqwb/K8CmeLHXAh+Nx2uB5wGvk7S9x7kdx3H6j7EUAb9Z0u2l44our9AovW6PWCj3N1fUoa183puBj5RuAPcD7wOeTyiL+pcLLahXL5otwMfjRe+W9GPAt83sDgBJNwOvAK7ucX7HOT5ELVeZSI8kbP+TO3nszT/G4TMWsYEvYe6jWGgDtji1AVYttXWyybfngjcxu6kP6x4kure+7DWzHfN1SroKuLSt+XnAVuDe+P9dbf27Y3vBJoIWf0Kprf281wHldfyDmR2Ka/hrQqnUeem16PZHzOzGeJHfAGaY++jR6fHEcVYN2lwjm8h54rpTOfjC2vIISZUKYM8nsGNdWKvQ2vzNadnUs3ns622C3gn0yw/ezP7YzF5QPgjC9rI45HKChl3mVuDSaOK+ALjfzGrAdyW9XFJKEOi3Akg6G3jczJ4uzfFpSb8UX18C3LbQOnveZJU0KemvgLcBF7DwY0b5vCuKx57s8FSvl3ec5SEKw4nvjGMb6iR/vykU1yj19ZNkIS+dItVvXioKkkPSCBp9kaQsDApzpNMdhL1r8C2W1wZ/NfBCSQ8AP040n0j6hKSfMLPvAl8Ffgh8kJaXzZXAnwMPAF8zs2/F9p8CvsVc3gG8U9I9hJvIghp8TyYaSRNxoV8hPD6cwNxHj60Et56jMLNrgGsARred7n95zopk6vQcm03Y/yNGnsZNzWXS4hdNDlYyvcggH4n97fVWZWTjy7TOQcAMsuXLQxC9XC7p0P6G0ut3EVwiy/33Ezxp2s/7OPDxtra7gJd0u6ZeNfi3Av9sZr9jZjNmthuoSDo7uv28Gvh8j3M7znFn9OmEDd+LmnvSR4HZSZh3EsidtPBuznXhvjBDli64103WFwMvlVT217wcuBGYBN5jZo8f49ocx3H6ywAJ727oScCb2b+bp+sFx7AWxzn+RE05G4MDZ+ekNYU87f2cv0x5M7TLtc07l7MwBnhNVscZYqIAbUzmJBmk26aW1wllPiHd6UawkIuk0wUGlnd3DAgu4B2nTCE8N81CLtZ9ZhJb21j8vF7p5N5owpIO/S7Ujw0jbLJ2cwwIni7Ycdoxob2jkBjPnAfJwWX8mcyzSapsEdPNsVaXGlbcBu84jjOgDJmAdxON4xRE88iZfzdLXs1JasuQhbGLtASYQl74Mp02WD1D5BJZ9mRjKw4X8I5TEE0ib7n6JsaeTFsBRf00hbQL6kJIF+kH4hgrpwPuZIdfiveNEzAgz7s7BgQX8I5TxsR/+u+/xsxJWVOA5hvqfZu7Y/pf2dxf4jwpgjt61jhLY8g0eLfBO04ZGUfOrqPDrZ9GMxdNH+ZeMC1B+QbQnoZgvnOcJbC8qQpWIi7gHaeMiWR/Fdtcg2dGFh+/hHkXFcgLVXPylATHjoENkI97N7iAdxxnePBIVscZbqqHxPjdY/3zUilr4IsV/ShSBDfgOb/zdcg7bMg6veM2eMcZYmTU1xv19VDkV88mrDsTywJzLkgn90fgwT//qVYxbg9sOnbMBspDphtcg3ecdkqbndn4Ipr3Uuft1NZue5dh1egqedJMq9B2TYzuTTq7Tzrd4Rq84ww5y6Upl+ddrKRebLO9o9iGBhiM3zPC9Ck5o3sTaie4D/zSMSzrWGhuYHEB7zgL8WwI+07XKHLSGGh/+JlObwk3AxfuPTKE6YJdwDuOMzwMmZtkX23wCnxU0sOSviHptH7O7zhDj2vuPWOA5dbV0QuSRiXdKGmXpM9KWt9hzFWx/05J57X13STp3IXGStou6dux/QOLranfm6yvBk4EziRUDX93n+d3nOFlMVdLZ2Fs2Qt+vBXYaWbbgC8Cby93SjofeBWwHXgL8KHY/iJJ1wO/vNhY4P2Eot1nAOdIevlCC+q3gL8Y+LiZGXAT8LI+z+84jtMzlmVdHT1yMXBtfH098MoO/Z80s4aZ3QacLmkCeAr4a+CuRcZOEuph3xJl7Kc7XGMO/bbBbwMeBTCzWUmppMRK8cGSrgCuiG9rO9/2jrs6zLPS2QzsPd6L6JHVunZf97PPSlv7Gcdy8iH2ffaf7IbNXQ4fk3R76f01ZnbNIuc05R/wGLClQ//nSu/3ACea2cPAI5LescjYzcC+KNyLa1yw0IL6LeANKNc3a1hb8of4JV0DIOl2M9vR5zUsO6t13bB61+7rfvZZzWvvhJldtNyXoCX/DGh/FGiXj53GLDR2KecD/Rfwu4GtwJ2SqsBMn+d3HMc57ki6Cri0rfl5BPl3b/z/rrb+Qj4WbAKenOcSncbuAU4otXW6xhz6bYO/Fbgsvr4M+Hyf53ccx12AOGwAAANySURBVDnumNkfm9kLygdwJS35dzlBHpa5FbhUUiLpAuB+M5ud5xKdxtaA70p6uaQUeF2Ha8yh3xr8zcAvSnoQeAR4zSLjF7NprVRW67ph9a7d1/3ss5rXfjy4GviUpAeAO4HXAkj6BPBhM/umpK8CPwT2A/92vonM7LvzjL0S+BRBk7/WzL610IJkA5R3wXEcx2nhycYcx3EGFBfwjuM4A8qzLuBXYzoDSbdLuicen1pquPDxQNIlkt4XX79E0t2SHpL0u7Ftxf47tK39MkkPlr7/n43t84Z8P8trTSR9LK7lDkk/s1q+73nWvqK/b2eJmNmzegCXADcQShlcSoh8fdbXsYT1psAX29puAn4pfoZbgZcf73WW1iZCmog9wPti2+3A+UAV+A4h/HnF/TvMs/Z3AT/fNu584BsEJ4F/BXz5OK75NQTnAgE/CnxvFX3fnda+or9vP5Z2HA8TzWpLZ7AVeLx4E92TlhQufBz4AnAdgKRTCJvpd5hZnfCDfgUr99+hufbINoJHVpn5Qr6PB1uI36OZ3Q38GKvn+25f+4ms/O/bWQLHQ8DPSWcApJJW8l7AGcD58RH2NuBnOTpcuD0k+bgRf6y3AHfEpnL4NLTWu+L+HTqsHcL3/5eSfiDpL2IAXftn2kMQTs86ZvYRM7sRQNJvEIL7Vsv33b72+1nh37ezNI7HH9ii6QxWGFPAfwV2ELLFfYYlhgsfZ+YLb14t/w5fBt5G0Iw3AL9JDyHby4mkSUl/RVjnBayi77tt7ZezCr5vp3uOh4BvhuCuknQG9xCCFOpm9h1CxrfzS/2LhgsfZ9pDnov1rvh/B0kC/rOZ3W1mDeBvgRewtJDvZSWaKr4KHCYoAavm++6w9p2s8O/bWRrHQ8CvtnQGbwP+BEDSc4B1wD8sJVz4eGJmu4GKpLMV0o2+mvCdr4Z/hxS4T1IhXF4N3MbSQr6Xm7cC/2xmv2NmM6vs+56zdlbH9+0sgeNRsm+p6QyONx8GrpP0Q+AA8GaCDbLrcOEVwG8DNwKTwHvM7HFJK/7fwcwakt4GfFlSg6BtXmtm9W5Dvp8FXgy8VFJ5o/1yVsf33WntK/37dpaApypwHMcZUFay94rjOI5zDLiAdxzHGVBcwDuO4wwoLuAdx3EGFBfwjuM4A4oLeMdxnAHFBbzjOM6A8v8D1AHa9R/2gBkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mp.pcolormesh((ysu_raintot_1000_time_mean-ysu_raintot_400_time_mean), vmin=-.01,vmax=.01)\n",
    "mp.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:14.296274Z",
     "start_time": "2018-08-17T19:32:14.169083Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_tk_diffs_latlon_mean = ysu_tk_1000_latlon_mean - ysu_tk_400_latlon_mean\n",
    "ysu_qvapor_diffs_latlon_mean = ysu_qvapor_1000_latlon_mean - ysu_qvapor_400_latlon_mean\n",
    "ysu_cldfra_diffs_latlon_mean = ysu_cldfra_1000_latlon_mean - ysu_cldfra_400_latlon_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ysu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:14.390167Z",
     "start_time": "2018-08-17T19:32:14.304915Z"
    }
   },
   "outputs": [],
   "source": [
    "myj_tk_diffs_latlon_mean = myj_tk_1000_latlon_mean - myj_tk_400_latlon_mean\n",
    "myj_qvapor_diffs_latlon_mean = myj_qvapor_1000_latlon_mean - myj_qvapor_400_latlon_mean\n",
    "myj_cldfra_diffs_latlon_mean = myj_cldfra_1000_latlon_mean - myj_cldfra_400_latlon_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:14.489734Z",
     "start_time": "2018-08-17T19:32:14.399081Z"
    }
   },
   "outputs": [],
   "source": [
    "acm2_tk_diffs_latlon_mean = acm2_tk_1000_latlon_mean - acm2_tk_400_latlon_mean\n",
    "acm2_qvapor_diffs_latlon_mean = acm2_qvapor_1000_latlon_mean - acm2_qvapor_400_latlon_mean\n",
    "acm2_cldfra_diffs_latlon_mean = acm2_cldfra_1000_latlon_mean - acm2_cldfra_400_latlon_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:14.589877Z",
     "start_time": "2018-08-17T19:32:14.498530Z"
    }
   },
   "outputs": [],
   "source": [
    "uw_tk_diffs_latlon_mean = uw_tk_1000_latlon_mean - uw_tk_400_latlon_mean\n",
    "uw_qvapor_diffs_latlon_mean = uw_qvapor_1000_latlon_mean - uw_qvapor_400_latlon_mean\n",
    "uw_cldfra_diffs_latlon_mean = uw_cldfra_1000_latlon_mean - uw_cldfra_400_latlon_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:14.689750Z",
     "start_time": "2018-08-17T19:32:14.595221Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn25_tk_diffs_latlon_mean = mynn25_tk_1000_latlon_mean - mynn25_tk_400_latlon_mean\n",
    "mynn25_qvapor_diffs_latlon_mean = mynn25_qvapor_1000_latlon_mean - mynn25_qvapor_400_latlon_mean\n",
    "mynn25_cldfra_diffs_latlon_mean = mynn25_cldfra_1000_latlon_mean - mynn25_cldfra_400_latlon_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:14.795825Z",
     "start_time": "2018-08-17T19:32:14.692617Z"
    }
   },
   "outputs": [],
   "source": [
    "mynn30_tk_diffs_latlon_mean = mynn30_tk_1000_latlon_mean - mynn30_tk_400_latlon_mean\n",
    "mynn30_qvapor_diffs_latlon_mean = mynn30_qvapor_1000_latlon_mean - mynn30_qvapor_400_latlon_mean\n",
    "mynn30_cldfra_diffs_latlon_mean = mynn30_cldfra_1000_latlon_mean - mynn30_cldfra_400_latlon_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:15.004530Z",
     "start_time": "2018-08-17T19:32:14.798242Z"
    }
   },
   "outputs": [],
   "source": [
    "temf_tk_diffs_latlon_mean = temf_tk_1000_latlon_mean - temf_tk_400_latlon_mean\n",
    "temf_qvapor_diffs_latlon_mean = temf_qvapor_1000_latlon_mean - temf_qvapor_400_latlon_mean\n",
    "temf_cldfra_diffs_latlon_mean = temf_cldfra_1000_latlon_mean - temf_cldfra_400_latlon_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:15.198223Z",
     "start_time": "2018-08-17T19:32:15.007022Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_ptot_400_latlon_time_mean = numpy.nanmean(ysu_ptot_400[:,:,latlon_indices], axis=(0,2))\n",
    "ysu_geopot_400_latlon_time_mean = numpy.nanmean(ysu_geopot_400[:,:,latlon_indices], axis=(0,2))\n",
    "\n",
    "myj_ptot_400_latlon_time_mean = numpy.nanmean(myj_ptot_400[:,:,latlon_indices], axis=(0,2))\n",
    "myj_geopot_400_latlon_time_mean = numpy.nanmean(myj_geopot_400[:,:,latlon_indices], axis=(0,2))\n",
    "\n",
    "acm2_ptot_400_latlon_time_mean = numpy.nanmean(acm2_ptot_400[:,:,latlon_indices], axis=(0,2))\n",
    "acm2_geopot_400_latlon_time_mean = numpy.nanmean(acm2_geopot_400[:,:,latlon_indices], axis=(0,2))\n",
    "\n",
    "uw_ptot_400_latlon_time_mean = numpy.nanmean(uw_ptot_400[:,:,latlon_indices], axis=(0,2))\n",
    "uw_geopot_400_latlon_time_mean = numpy.nanmean(uw_geopot_400[:,:,latlon_indices], axis=(0,2))\n",
    "\n",
    "mynn25_ptot_400_latlon_time_mean = numpy.nanmean(mynn25_ptot_400[:,:,latlon_indices], axis=(0,2))\n",
    "mynn25_geopot_400_latlon_time_mean = numpy.nanmean(mynn25_geopot_400[:,:,latlon_indices], axis=(0,2))\n",
    "\n",
    "mynn30_ptot_400_latlon_time_mean = numpy.nanmean(mynn30_ptot_400[:,:,latlon_indices], axis=(0,2))\n",
    "mynn30_geopot_400_latlon_time_mean = numpy.nanmean(mynn30_geopot_400[:,:,latlon_indices], axis=(0,2))\n",
    "\n",
    "temf_ptot_400_latlon_time_mean = numpy.nanmean(temf_ptot_400[:,:,latlon_indices], axis=(0,2))\n",
    "temf_geopot_400_latlon_time_mean = numpy.nanmean(temf_geopot_400[:,:,latlon_indices], axis=(0,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:15.221854Z",
     "start_time": "2018-08-17T19:32:15.206843Z"
    }
   },
   "outputs": [],
   "source": [
    "ysu_xx, ysu_pp = numpy.meshgrid(numpy.arange(ntime), ysu_ptot_400_latlon_time_mean)\n",
    "ysu_xx_geopot, ysu_gg = numpy.meshgrid(numpy.arange(ntime), ysu_geopot_400_latlon_time_mean)\n",
    "\n",
    "myj_xx, myj_pp = numpy.meshgrid(numpy.arange(ntime), myj_ptot_400_latlon_time_mean)\n",
    "myj_xx_geopot, myj_gg = numpy.meshgrid(numpy.arange(ntime), myj_geopot_400_latlon_time_mean)\n",
    "\n",
    "acm2_xx, acm2_pp = numpy.meshgrid(numpy.arange(ntime), acm2_ptot_400_latlon_time_mean)\n",
    "acm2_xx_geopot, acm2_gg = numpy.meshgrid(numpy.arange(ntime), acm2_geopot_400_latlon_time_mean)\n",
    "\n",
    "uw_xx, uw_pp = numpy.meshgrid(numpy.arange(ntime), uw_ptot_400_latlon_time_mean)\n",
    "uw_xx_geopot, uw_gg = numpy.meshgrid(numpy.arange(ntime), uw_geopot_400_latlon_time_mean)\n",
    "\n",
    "mynn25_xx, mynn25_pp = numpy.meshgrid(numpy.arange(ntime), mynn25_ptot_400_latlon_time_mean)\n",
    "mynn25_xx_geopot, mynn25_gg = numpy.meshgrid(numpy.arange(ntime), mynn25_geopot_400_latlon_time_mean)\n",
    "\n",
    "mynn30_xx, mynn30_pp = numpy.meshgrid(numpy.arange(ntime), mynn30_ptot_400_latlon_time_mean)\n",
    "mynn30_xx_geopot, mynn30_gg = numpy.meshgrid(numpy.arange(ntime), mynn30_geopot_400_latlon_time_mean)\n",
    "\n",
    "temf_xx, temf_pp = numpy.meshgrid(numpy.arange(ntime), temf_ptot_400_latlon_time_mean)\n",
    "temf_xx_geopot, temf_gg = numpy.meshgrid(numpy.arange(ntime), temf_geopot_400_latlon_time_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for qvapor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:20.970002Z",
     "start_time": "2018-08-17T19:32:15.230320Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,10))\n",
    "levels = numpy.arange(-0.5,0.51,0.05)\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(511)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "contour_plot1 = ax1.contourf(ysu_xx_geopot-4, ysu_gg/9.81/1000., \\\n",
    "                             ysu_qvapor_diffs_latlon_mean.T*1000., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "\n",
    "ax1.plot(ysu_xx_geopot[0,:]-4, ysu_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax1.plot(ysu_xx_geopot[0,:]-4, ysu_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "ax1.set_ylim(0,3.25)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_ylabel('km', fontsize=fontsize)\n",
    "\n",
    "ax2 = fig.add_subplot(512)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax2.contourf(temf_xx_geopot-4, temf_gg/9.81/1000., \\\n",
    "                             temf_qvapor_diffs_latlon_mean.T*1000., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "ax2.set_ylim(0,3.25)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_ylabel('km', fontsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.plot(temf_xx_geopot[0,:]-4, temf_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax2.plot(temf_xx_geopot[0,:]-4, temf_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(513)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax3.contourf(acm2_xx_geopot-4, acm2_gg/9.81/1000., \\\n",
    "                             acm2_qvapor_diffs_latlon_mean.T*1000., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "ax3.set_ylim(0,3.25)\n",
    "ax3.set_xlim(6,18)\n",
    "ax3.set_ylabel('km', fontsize=fontsize)\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "ax3.plot(acm2_xx_geopot[0,:]-4, acm2_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax3.plot(acm2_xx_geopot[0,:]-4, acm2_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "\n",
    "ax4 = fig.add_subplot(514)\n",
    "ax4.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax4.contourf(uw_xx_geopot-4, uw_gg/9.81/1000., \\\n",
    "                             uw_qvapor_diffs_latlon_mean.T*1000., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "ax4.set_ylim(0,3.25)\n",
    "ax4.set_xlim(6,18)\n",
    "ax4.set_ylabel('km', fontsize=fontsize)\n",
    "ax4.set_xticks([i for i in range(6,19,2)])\n",
    "ax4.plot(uw_xx_geopot[0,:]-4, uw_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax4.plot(uw_xx_geopot[0,:]-4, uw_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax5 = fig.add_subplot(515)\n",
    "ax5.tick_params(labelsize=fontsize)\n",
    "contour_plot5 = ax5.contourf(mynn30_xx_geopot-4, uw_gg/9.81/1000., \\\n",
    "                             mynn30_qvapor_diffs_latlon_mean.T*1000., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "ax5.set_ylim(0,3.25)\n",
    "ax5.set_xlim(6,18)\n",
    "ax5.set_ylabel('km', fontsize=fontsize)\n",
    "ax5.set_xticks([i for i in range(6,19,2)])\n",
    "ax5.plot(uw_xx_geopot[0,:]-4, mynn30_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax5.plot(uw_xx_geopot[0,:]-4, mynn30_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax1.text(s='(a) $\\Delta$ moisture (YSU)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s='(b) $\\Delta$ moisture (TEMF)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s='(c) $\\Delta$ moisture (ACM2)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "ax4.text(s='(d) $\\Delta$ moisture (UW)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax4.transAxes)\n",
    "ax5.text(s='(e) $\\Delta$ moisture (MYNN)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax5.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "ax2pos = ax5.get_position()\n",
    "cbar_ax = fig.add_axes([ax2pos.x1+0.05, ax2pos.y0, 0.045, ax1pos.y1-ax2pos.y0])\n",
    "cbar = fig.colorbar(contour_plot1, cax=cbar_ax)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_label('g kg$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('figures/qvapor_diffs_PBL_schemes.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:26.552419Z",
     "start_time": "2018-08-17T19:32:20.979579Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,10))\n",
    "levels = numpy.arange(-1,1.1,0.1)\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(511)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "contour_plot1 = ax1.contourf(ysu_xx_geopot-4, ysu_gg/9.81/1000., \\\n",
    "                             ysu_tk_diffs_latlon_mean.T, \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "\n",
    "ax1.plot(ysu_xx_geopot[0,:]-4, ysu_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax1.plot(ysu_xx_geopot[0,:]-4, ysu_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "ax1.set_ylim(0,3.25)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_ylabel('km', fontsize=fontsize)\n",
    "\n",
    "ax2 = fig.add_subplot(512)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax2.contourf(temf_xx_geopot-4, temf_gg/9.81/1000., \\\n",
    "                             temf_tk_diffs_latlon_mean.T, \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "ax2.set_ylim(0,3.25)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_ylabel('km', fontsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.plot(temf_xx_geopot[0,:]-4, temf_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax2.plot(temf_xx_geopot[0,:]-4, temf_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(513)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax3.contourf(acm2_xx_geopot-4, acm2_gg/9.81/1000., \\\n",
    "                             acm2_tk_diffs_latlon_mean.T, \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "ax3.set_ylim(0,3.25)\n",
    "ax3.set_xlim(6,18)\n",
    "ax3.set_ylabel('km', fontsize=fontsize)\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "ax3.plot(acm2_xx_geopot[0,:]-4, acm2_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax3.plot(acm2_xx_geopot[0,:]-4, acm2_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "\n",
    "ax4 = fig.add_subplot(514)\n",
    "ax4.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax4.contourf(uw_xx_geopot-4, uw_gg/9.81/1000., \\\n",
    "                             uw_tk_diffs_latlon_mean.T, \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "ax4.set_ylim(0,3.25)\n",
    "ax4.set_xlim(6,18)\n",
    "ax4.set_ylabel('km', fontsize=fontsize)\n",
    "ax4.set_xticks([i for i in range(6,19,2)])\n",
    "ax4.plot(uw_xx_geopot[0,:]-4, uw_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax4.plot(uw_xx_geopot[0,:]-4, uw_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax5 = fig.add_subplot(515)\n",
    "ax5.tick_params(labelsize=fontsize)\n",
    "contour_plot5 = ax5.contourf(mynn30_xx_geopot-4, uw_gg/9.81/1000., \\\n",
    "                             mynn30_tk_diffs_latlon_mean.T, \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "ax5.set_ylim(0,3.25)\n",
    "ax5.set_xlim(6,18)\n",
    "ax5.set_ylabel('km', fontsize=fontsize)\n",
    "ax5.set_xticks([i for i in range(6,19,2)])\n",
    "ax5.plot(uw_xx_geopot[0,:]-4, mynn30_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax5.plot(uw_xx_geopot[0,:]-4, mynn30_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax1.text(s='(a) $\\Delta$ temperature (YSU)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s='(b) $\\Delta$ temperature (TEMF)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s='(c) $\\Delta$ temperature (ACM2)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "ax4.text(s='(d) $\\Delta$ temperature (UW)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax4.transAxes)\n",
    "ax5.text(s='(e) $\\Delta$ temperature (MYNN)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax5.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "ax2pos = ax5.get_position()\n",
    "cbar_ax = fig.add_axes([ax2pos.x1+0.05, ax2pos.y0, 0.045, ax1pos.y1-ax2pos.y0])\n",
    "cbar = fig.colorbar(contour_plot1, cax=cbar_ax)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_label('$\\degree$C', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('figures/tk_diffs_PBL_schemes.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:38:28.150474Z",
     "start_time": "2018-08-17T19:38:20.800412Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x720 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(8.5,10))\n",
    "levels = numpy.arange(-1,1.1,0.1)\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(6,2,1)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "contour_plot1 = ax1.contourf(ysu_xx_geopot-4, ysu_gg/9.81/1000., \\\n",
    "                             ysu_tk_diffs_latlon_mean.T, \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "\n",
    "ax1.plot(ysu_xx_geopot[0,:]-4, ysu_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax1.plot(ysu_xx_geopot[0,:]-4, ysu_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "ax1.set_ylim(0,3.25)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "\n",
    "ax2 = fig.add_subplot(6,2,3)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax2.contourf(temf_xx_geopot-4, temf_gg/9.81/1000., \\\n",
    "                             temf_tk_diffs_latlon_mean.T, \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "ax2.set_ylim(0,3.25)\n",
    "ax2.set_xlim(6,18)\n",
    "\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.plot(temf_xx_geopot[0,:]-4, temf_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax2.plot(temf_xx_geopot[0,:]-4, temf_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(6,2,5)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax3.contourf(acm2_xx_geopot-4, acm2_gg/9.81/1000., \\\n",
    "                             acm2_tk_diffs_latlon_mean.T, \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "ax3.set_ylim(0,3.25)\n",
    "ax3.set_xlim(6,18)\n",
    "\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "ax3.plot(acm2_xx_geopot[0,:]-4, acm2_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax3.plot(acm2_xx_geopot[0,:]-4, acm2_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "\n",
    "ax4 = fig.add_subplot(6,2,7)\n",
    "ax4.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax4.contourf(uw_xx_geopot-4, uw_gg/9.81/1000., \\\n",
    "                             uw_tk_diffs_latlon_mean.T, \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "ax4.set_ylim(0,3.25)\n",
    "ax4.set_xlim(6,18)\n",
    "\n",
    "ax4.set_xticks([i for i in range(6,19,2)])\n",
    "ax4.plot(uw_xx_geopot[0,:]-4, uw_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax4.plot(uw_xx_geopot[0,:]-4, uw_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax5 = fig.add_subplot(6,2,9)\n",
    "ax5.tick_params(labelsize=fontsize)\n",
    "contour_plot5 = ax5.contourf(mynn30_xx_geopot-4, uw_gg/9.81/1000., \\\n",
    "                             mynn30_tk_diffs_latlon_mean.T, \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "ax5.set_ylim(0,3.25)\n",
    "ax5.set_xlim(6,18)\n",
    "\n",
    "ax5.set_xticks([i for i in range(6,19,2)])\n",
    "ax5.plot(uw_xx_geopot[0,:]-4, mynn30_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax5.plot(uw_xx_geopot[0,:]-4, mynn30_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax6 = fig.add_subplot(6,2,11)\n",
    "ax6.tick_params(labelsize=fontsize)\n",
    "contour_plot6 = ax6.contourf(myj_xx_geopot-4, uw_gg/9.81/1000., \\\n",
    "                             myj_tk_diffs_latlon_mean.T, \\\n",
    "                             cmap='PuOr_r', extend='both', levels=levels)\n",
    "ax6.set_ylim(0,3.25)\n",
    "ax6.set_xlim(6,18)\n",
    "ax6.set_xticks([i for i in range(6,19,2)])\n",
    "ax6.plot(uw_xx_geopot[0,:]-4, myj_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax6.plot(uw_xx_geopot[0,:]-4, myj_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax1.text(s='(a) $\\Delta$ temperature (YSU)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s='(b) $\\Delta$ temperature (TEMF)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s='(c) $\\Delta$ temperature (ACM2)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "ax4.text(s='(d) $\\Delta$ temperature (UW)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax4.transAxes)\n",
    "ax5.text(s='(e) $\\Delta$ temperature (MYNN3)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax5.transAxes)\n",
    "ax5.text(s='(f) $\\Delta$ temperature (MYJ)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax6.transAxes)\n",
    "\n",
    "ax1.set_ylabel('km', fontsize=fontsize)\n",
    "ax2.set_ylabel('km', fontsize=fontsize)\n",
    "ax3.set_ylabel('km', fontsize=fontsize)\n",
    "ax4.set_ylabel('km', fontsize=fontsize)\n",
    "ax5.set_ylabel('km', fontsize=fontsize)\n",
    "#fig.tight_layout(h_pad=2)\n",
    "\n",
    "#=================================================================\n",
    "\n",
    "levels = numpy.arange(-0.5,0.51,0.05)\n",
    "\n",
    "ax12 = fig.add_subplot(6,2,2)\n",
    "ax12.tick_params(labelsize=fontsize)\n",
    "contour_plot12 = ax12.contourf(ysu_xx_geopot-4, ysu_gg/9.81/1000., \\\n",
    "                             ysu_qvapor_diffs_latlon_mean.T*1000., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "\n",
    "ax12.plot(ysu_xx_geopot[0,:]-4, ysu_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax12.plot(ysu_xx_geopot[0,:]-4, ysu_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "ax12.set_ylim(0,3.25)\n",
    "ax12.set_xlim(6,18)\n",
    "ax12.set_xticks([i for i in range(6,19,2)])\n",
    "\n",
    "\n",
    "ax22 = fig.add_subplot(6,2,4)\n",
    "ax22.tick_params(labelsize=fontsize)\n",
    "contour_plot22 = ax22.contourf(temf_xx_geopot-4, temf_gg/9.81/1000., \\\n",
    "                             temf_qvapor_diffs_latlon_mean.T*1000., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "ax22.set_ylim(0,3.25)\n",
    "ax22.set_xlim(6,18)\n",
    "\n",
    "ax22.set_xticks([i for i in range(6,19,2)])\n",
    "ax22.plot(temf_xx_geopot[0,:]-4, temf_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax22.plot(temf_xx_geopot[0,:]-4, temf_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "\n",
    "ax32 = fig.add_subplot(6,2,6)\n",
    "ax32.tick_params(labelsize=fontsize)\n",
    "contour_plot32 = ax32.contourf(acm2_xx_geopot-4, acm2_gg/9.81/1000., \\\n",
    "                             acm2_qvapor_diffs_latlon_mean.T*1000., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "ax32.set_ylim(0,3.25)\n",
    "ax32.set_xlim(6,18)\n",
    "\n",
    "ax32.set_xticks([i for i in range(6,19,2)])\n",
    "ax32.plot(acm2_xx_geopot[0,:]-4, acm2_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax32.plot(acm2_xx_geopot[0,:]-4, acm2_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "\n",
    "ax42 = fig.add_subplot(6,2,8)\n",
    "ax42.tick_params(labelsize=fontsize)\n",
    "contour_plot42 = ax42.contourf(uw_xx_geopot-4, uw_gg/9.81/1000., \\\n",
    "                             uw_qvapor_diffs_latlon_mean.T*1000., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "ax42.set_ylim(0,3.25)\n",
    "ax42.set_xlim(6,18)\n",
    "\n",
    "ax42.set_xticks([i for i in range(6,19,2)])\n",
    "ax42.plot(uw_xx_geopot[0,:]-4, uw_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax42.plot(uw_xx_geopot[0,:]-4, uw_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax52 = fig.add_subplot(6,2,10)\n",
    "ax52.tick_params(labelsize=fontsize)\n",
    "contour_plot52 = ax52.contourf(mynn30_xx_geopot-4, uw_gg/9.81/1000., \\\n",
    "                             mynn30_qvapor_diffs_latlon_mean.T*1000., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "ax52.set_ylim(0,3.25)\n",
    "ax52.set_xlim(6,18)\n",
    "ax52.set_xticks([i for i in range(6,19,2)])\n",
    "ax52.plot(uw_xx_geopot[0,:]-4, mynn30_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax52.plot(uw_xx_geopot[0,:]-4, mynn30_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax62 = fig.add_subplot(6,2,12)\n",
    "ax62.tick_params(labelsize=fontsize)\n",
    "contour_plot62 = ax62.contourf(myj_xx_geopot-4, uw_gg/9.81/1000., \\\n",
    "                             myj_qvapor_diffs_latlon_mean.T*1000., \\\n",
    "                             cmap=cmocean.cm.balance_r, extend='both', levels=levels)\n",
    "ax62.set_ylim(0,3.25)\n",
    "ax62.set_xlim(6,18)\n",
    "ax62.set_xticks([i for i in range(6,19,2)])\n",
    "ax62.plot(uw_xx_geopot[0,:]-4, myj_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax62.plot(uw_xx_geopot[0,:]-4, myj_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax6.set_xlabel('hour', fontsize=fontsize)\n",
    "ax62.set_xlabel('hour', fontsize=fontsize)\n",
    "\n",
    "ax12.text(s='(g) $\\Delta$ moisture (YSU)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax12.transAxes)\n",
    "ax22.text(s='(h) $\\Delta$ moisture (TEMF)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax22.transAxes)\n",
    "ax32.text(s='(i) $\\Delta$ moisture (ACM2)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax32.transAxes)\n",
    "ax42.text(s='(j) $\\Delta$ moisture (UW)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax42.transAxes)\n",
    "ax52.text(s='(k) $\\Delta$ moisture (MYNN3)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax52.transAxes)\n",
    "ax52.text(s='(l) $\\Delta$ moisture (MYJ)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax62.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2, w_pad=8)\n",
    "\n",
    "ax1pos = ax12.get_position()\n",
    "ax2pos = ax62.get_position()\n",
    "cbar_ax = fig.add_axes([ax2pos.x1+0.025, ax2pos.y0, 0.025, ax1pos.y1-ax2pos.y0])\n",
    "cbar = fig.colorbar(contour_plot52, cax=cbar_ax)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_label('g kg$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "ax2pos = ax6.get_position()\n",
    "cbar_ax = fig.add_axes([ax2pos.x1+0.025, ax2pos.y0, 0.025, ax1pos.y1-ax2pos.y0])\n",
    "cbar = fig.colorbar(contour_plot1, cax=cbar_ax)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_label('$\\degree$C', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('figures/tk_AND_qvapor_diffs_PBL_schemes.png', dpi=400, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-17T19:32:38.948341Z",
     "start_time": "2018-08-17T19:32:33.709303Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 306x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,10))\n",
    "levels = numpy.arange(-1,1.1,0.1)\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(511)\n",
    "ax1.tick_params(labelsize=fontsize)\n",
    "contour_plot1 = ax1.contourf(ysu_xx_geopot-4, ysu_gg/9.81/1000., \\\n",
    "                             ysu_cldfra_diffs_latlon_mean.T*100., \\\n",
    "                             cmap=cmocean.cm.balance, extend='both', levels=levels)\n",
    "\n",
    "ax1.plot(ysu_xx_geopot[0,:]-4, ysu_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax1.plot(ysu_xx_geopot[0,:]-4, ysu_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "ax1.set_ylim(0,3.25)\n",
    "ax1.set_xlim(6,18)\n",
    "ax1.set_xticks([i for i in range(6,19,2)])\n",
    "ax1.set_ylabel('km', fontsize=fontsize)\n",
    "\n",
    "ax2 = fig.add_subplot(512)\n",
    "ax2.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax2.contourf(temf_xx_geopot-4, temf_gg/9.81/1000., \\\n",
    "                             temf_cldfra_diffs_latlon_mean.T*100., \\\n",
    "                             cmap=cmocean.cm.balance, extend='both', levels=levels)\n",
    "ax2.set_ylim(0,3.25)\n",
    "ax2.set_xlim(6,18)\n",
    "ax2.set_ylabel('km', fontsize=fontsize)\n",
    "ax2.set_xticks([i for i in range(6,19,2)])\n",
    "ax2.plot(temf_xx_geopot[0,:]-4, temf_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax2.plot(temf_xx_geopot[0,:]-4, temf_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(513)\n",
    "ax3.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax3.contourf(acm2_xx_geopot-4, acm2_gg/9.81/1000., \\\n",
    "                             acm2_cldfra_diffs_latlon_mean.T*100., \\\n",
    "                             cmap=cmocean.cm.balance, extend='both', levels=levels)\n",
    "ax3.set_ylim(0,3.25)\n",
    "ax3.set_xlim(6,18)\n",
    "ax3.set_ylabel('km', fontsize=fontsize)\n",
    "ax3.set_xticks([i for i in range(6,19,2)])\n",
    "ax3.plot(acm2_xx_geopot[0,:]-4, acm2_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax3.plot(acm2_xx_geopot[0,:]-4, acm2_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "\n",
    "ax4 = fig.add_subplot(514)\n",
    "ax4.tick_params(labelsize=fontsize)\n",
    "contour_plot2 = ax4.contourf(uw_xx_geopot-4, uw_gg/9.81/1000., \\\n",
    "                             uw_cldfra_diffs_latlon_mean.T*100., \\\n",
    "                             cmap=cmocean.cm.balance, extend='both', levels=levels)\n",
    "ax4.set_ylim(0,3.25)\n",
    "ax4.set_xlim(6,18)\n",
    "ax4.set_ylabel('km', fontsize=fontsize)\n",
    "ax4.set_xticks([i for i in range(6,19,2)])\n",
    "ax4.plot(uw_xx_geopot[0,:]-4, uw_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax4.plot(uw_xx_geopot[0,:]-4, uw_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax5 = fig.add_subplot(515)\n",
    "ax5.tick_params(labelsize=fontsize)\n",
    "contour_plot5 = ax5.contourf(mynn30_xx_geopot-4, uw_gg/9.81/1000., \\\n",
    "                             mynn30_cldfra_diffs_latlon_mean.T*100., \\\n",
    "                             cmap=cmocean.cm.balance, extend='both', levels=levels)\n",
    "ax5.set_ylim(0,3.25)\n",
    "ax5.set_xlim(6,18)\n",
    "ax5.set_ylabel('km', fontsize=fontsize)\n",
    "ax5.set_xticks([i for i in range(6,19,2)])\n",
    "ax5.plot(uw_xx_geopot[0,:]-4, mynn30_pblh_400_latlon_mean/1000., c='0.5')\n",
    "ax5.plot(uw_xx_geopot[0,:]-4, mynn30_pblh_1000_latlon_mean/1000., c='0.5', ls='--')\n",
    "\n",
    "ax1.text(s='(a) $\\Delta$ cloud fraction (YSU)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax1.transAxes)\n",
    "ax2.text(s='(b) $\\Delta$ cloud fraction (TEMF)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax2.transAxes)\n",
    "ax3.text(s='(c) $\\Delta$ cloud fraction (ACM2)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax3.transAxes)\n",
    "ax4.text(s='(d) $\\Delta$ cloud fraction (UW)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax4.transAxes)\n",
    "ax5.text(s='(e) $\\Delta$ cloud fraction (MYNN)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax5.transAxes)\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1pos = ax1.get_position()\n",
    "ax2pos = ax5.get_position()\n",
    "cbar_ax = fig.add_axes([ax2pos.x1+0.05, ax2pos.y0, 0.045, ax1pos.y1-ax2pos.y0])\n",
    "cbar = fig.colorbar(contour_plot1, cax=cbar_ax)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_label('%', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('figures/cldfra_diffs_PBL_schemes.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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
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