gp1_moisture_budget_calculations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T23:36:35.531862Z",
     "start_time": "2018-06-29T23:36:35.098941Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/cmocean/tools.py:76: MatplotlibDeprecationWarning: The is_string_like function was deprecated in version 2.1.\n",
      "  if not mpl.cbook.is_string_like(rgbin[0]):\n"
     ]
    }
   ],
   "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 cmocean\n",
    "\n",
    "import wrf\n",
    "import netCDF4\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": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T23:36:38.118416Z",
     "start_time": "2018-06-29T23:36:38.112926Z"
    }
   },
   "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": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-29T23:36:42.306733Z",
     "start_time": "2018-06-29T23:36:40.162433Z"
    }
   },
   "outputs": [],
   "source": [
    "root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/400/ens_mean_wrfout_files'\n",
    "file_list = sorted(glob.glob(root_dir+'/'+'wrfout*.nc'))\n",
    "ds_list = [netCDF4.Dataset(file) for file in file_list]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T22:57:04.205195Z",
     "start_time": "2018-05-29T22:53:48.268834Z"
    }
   },
   "source": [
    "geopot_stag_400 = wrf.getvar(ds_list, 'geopt_stag', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_geopot_stag_400.npy', geopot_stag_400)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-20T20:37:06.170072Z",
     "start_time": "2018-04-20T20:36:46.200664Z"
    }
   },
   "source": [
    "ptot_400 = wrf.getvar(ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "wa_400 = wrf.getvar(ds_list, 'wa', timeidx=wrf.ALL_TIMES).values\n",
    "ua_400 = wrf.getvar(ds_list, 'ua', timeidx=wrf.ALL_TIMES).values\n",
    "va_400 = wrf.getvar(ds_list, 'va', timeidx=wrf.ALL_TIMES).values\n",
    "tk_400 = wrf.getvar(ds_list, 'tk', timeidx=wrf.ALL_TIMES).values\n",
    "prw_400 = wrf.getvar(ds_list, 'prw', timeidx=wrf.ALL_TIMES).values\n",
    "\n",
    "geopot_400 = wrf.getvar(ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "\n",
    "numpy.save('DATA_ptot_400.npy', ptot_400)\n",
    "numpy.save('DATA_wa_400.npy', wa_400)\n",
    "numpy.save('DATA_ua_400.npy', ua_400)\n",
    "numpy.save('DATA_va_400.npy', va_400)\n",
    "numpy.save('DATA_tk_400.npy', tk_400)\n",
    "numpy.save('DATA_prw_400.npy', prw_400)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:03:02.114473Z",
     "start_time": "2018-05-29T19:01:12.754138Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400 = numpy.load('DATA_ptot_400.npy')\n",
    "wa_400 = numpy.load('DATA_wa_400.npy')\n",
    "#ua_400 = numpy.load('DATA_ua_400.npy')\n",
    "#va_400 = numpy.load('DATA_va_400.npy')\n",
    "#tk_400 = numpy.load('DATA_tk_400.npy')\n",
    "#prw_400 = numpy.load('DATA_prw_400.npy')\n",
    "\n",
    "geopot_400 = numpy.load('DATA_geopot_400.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:03:10.576659Z",
     "start_time": "2018-05-29T19:03:02.645711Z"
    }
   },
   "outputs": [],
   "source": [
    "cont_mfds = xarray.open_mfdataset(root_dir + '/' + 'wrfout*.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:03:42.408763Z",
     "start_time": "2018-05-29T19:03:11.855155Z"
    }
   },
   "outputs": [],
   "source": [
    "time = cont_mfds['Times'].values\n",
    "xlat = cont_mfds['XLAT'].values[0,:,:]\n",
    "xlong = cont_mfds['XLONG'].values[0,:,:]\n",
    "landmask = cont_mfds['LANDMASK'].values[0,:,:]\n",
    "landmask_midpoints = landmask[1:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:03:43.629000Z",
     "start_time": "2018-05-29T19:03:43.618611Z"
    }
   },
   "outputs": [],
   "source": [
    "qvapor_400 = cont_mfds['QVAPOR']\n",
    "u_400 = cont_mfds['U']\n",
    "v_400 = cont_mfds['V']\n",
    "w_400 = cont_mfds['W']\n",
    "qcloud_400 = cont_mfds['QCLOUD']\n",
    "qrain_400 = cont_mfds['QRAIN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:03:44.866763Z",
     "start_time": "2018-05-29T19:03:44.859932Z"
    }
   },
   "outputs": [],
   "source": [
    "rainc_400 = cont_mfds['RAINC']\n",
    "rainnc_400 = cont_mfds['RAINNC']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## phys data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T23:39:05.934492Z",
     "start_time": "2018-05-29T23:39:05.816436Z"
    }
   },
   "outputs": [],
   "source": [
    "root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/1000/ens_mean_wrfout_files'\n",
    "file_list = sorted(glob.glob(root_dir+'/'+'wrfout*.nc'))\n",
    "ds_list = [netCDF4.Dataset(file) for file in file_list]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T23:00:32.501004Z",
     "start_time": "2018-05-29T22:57:19.578559Z"
    }
   },
   "source": [
    "geopot_stag_1000 = wrf.getvar(ds_list, 'geopt_stag', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_geopot_stag_1000.npy', geopot_stag_400)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T23:44:27.036767Z",
     "start_time": "2018-05-29T23:39:11.162774Z"
    }
   },
   "source": [
    "geopot_1000 = wrf.getvar(ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "numpy.save('DATA_geopot_1000.npy', geopot_1000)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-19T00:43:37.640789Z",
     "start_time": "2018-04-19T00:43:15.810474Z"
    }
   },
   "source": [
    "ptot_1000 = wrf.getvar(ds_list, 'p', timeidx=wrf.ALL_TIMES).values\n",
    "wa_1000 = wrf.getvar(ds_list, 'wa', timeidx=wrf.ALL_TIMES).values\n",
    "ua_1000 = wrf.getvar(ds_list, 'ua', timeidx=wrf.ALL_TIMES).values\n",
    "va_1000 = wrf.getvar(ds_list, 'va', timeidx=wrf.ALL_TIMES).values\n",
    "tk_1000 = wrf.getvar(ds_list, 'tk', timeidx=wrf.ALL_TIMES).values\n",
    "prw_1000 = wrf.getvar(ds_list, 'prw', timeidx=wrf.ALL_TIMES).values\n",
    "geopot_1000 = wrf.getvar(ds_list, 'geopotential', timeidx=wrf.ALL_TIMES).values\n",
    "\n",
    "numpy.save('DATA_ptot_1000.npy', ptot_1000)\n",
    "numpy.save('DATA_wa_1000.npy', wa_1000)\n",
    "numpy.save('DATA_ua_1000.npy', ua_1000)\n",
    "numpy.save('DATA_va_1000.npy', va_1000)\n",
    "numpy.save('DATA_tk_1000.npy', tk_1000)\n",
    "numpy.save('DATA_prw_1000.npy', prw_1000)\n",
    "numpy.save('DATA_geopot_1000.npy', geopot_1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:05:32.962339Z",
     "start_time": "2018-05-29T19:03:48.390997Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_1000 = numpy.load('DATA_ptot_1000.npy')\n",
    "wa_1000 = numpy.load('DATA_wa_1000.npy')\n",
    "#ua_1000 = numpy.load('DATA_ua_1000.npy')\n",
    "#va_1000 = numpy.load('DATA_va_1000.npy')\n",
    "#tk_1000 = numpy.load('DATA_tk_1000.npy')\n",
    "#prw_1000 = numpy.load('DATA_prw_1000.npy')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T23:37:06.287256Z",
     "start_time": "2018-05-29T23:36:30.526592Z"
    }
   },
   "outputs": [],
   "source": [
    "geopot_1000 = numpy.load('DATA_geopot_1000.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:15:47.509322Z",
     "start_time": "2018-05-29T19:15:39.601728Z"
    }
   },
   "outputs": [],
   "source": [
    "phys_mfds = xarray.open_mfdataset(root_dir + '/' + 'wrfout*.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:15:49.684136Z",
     "start_time": "2018-05-29T19:15:49.673673Z"
    }
   },
   "outputs": [],
   "source": [
    "qvapor_1000 = phys_mfds['QVAPOR']\n",
    "u_1000 = phys_mfds['U']\n",
    "v_1000 = phys_mfds['V']\n",
    "w_1000 = phys_mfds['W']\n",
    "qcloud_1000 = phys_mfds['QCLOUD']\n",
    "qrain_1000 = phys_mfds['QRAIN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:15:51.870278Z",
     "start_time": "2018-05-29T19:15:51.863390Z"
    }
   },
   "outputs": [],
   "source": [
    "rainc_1000 = phys_mfds['RAINC']\n",
    "rainnc_1000 = phys_mfds['RAINNC']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define dx, dy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:16:05.113822Z",
     "start_time": "2018-05-29T19:16:05.106278Z"
    }
   },
   "outputs": [],
   "source": [
    "dx = 50000. # 50,000 m or 50 km\n",
    "dy = 50000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gravitational constant for geopotential height calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:16:08.041379Z",
     "start_time": "2018-05-29T19:16:08.036243Z"
    }
   },
   "outputs": [],
   "source": [
    "g = 9.80665"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get landmask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:16:10.261164Z",
     "start_time": "2018-05-29T19:16:10.226145Z"
    }
   },
   "outputs": [],
   "source": [
    "landmask = cont_mfds['LANDMASK'].sel(Time=0).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:16:12.453505Z",
     "start_time": "2018-05-29T19:16:12.447853Z"
    }
   },
   "outputs": [],
   "source": [
    "lat_indices = ((xlat<=10)&(xlat>=-5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate raintot and rain diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:17:28.902910Z",
     "start_time": "2018-05-29T19:16:14.631538Z"
    }
   },
   "outputs": [],
   "source": [
    "raintot_accum_1000 = rainnc_1000+rainc_1000\n",
    "\n",
    "raintot_accum_400 = rainnc_400+rainc_400\n",
    "\n",
    "raintot_1000 = numpy.diff(raintot_accum_1000, axis=0)\n",
    "raintot_400 = numpy.diff(raintot_accum_400, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:17:31.340907Z",
     "start_time": "2018-05-29T19:17:31.142884Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: Mean of empty slice\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "raintot_diffs = raintot_1000-raintot_400\n",
    "\n",
    "raintot_diffs_landmask = numpy.copy(raintot_diffs)\n",
    "raintot_diffs_landmask[:,landmask==0]=numpy.nan\n",
    "raintot_diffs_lat_subset = raintot_diffs_landmask[:,lat_indices[:,0],:]\n",
    "raintot_diffs_latmean = numpy.nanmean(raintot_diffs_lat_subset, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:17:33.986740Z",
     "start_time": "2018-05-29T19:17:33.976193Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: Mean of empty slice\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: Mean of empty slice\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "raintot_diffs_latmean_day1 = numpy.nanmean(raintot_diffs_latmean[:24,:], axis=0)\n",
    "raintot_diffs_latmean_day2_10 = numpy.nanmean(raintot_diffs_latmean[24:,:], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate w diffs for height-longitude plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:17:44.939861Z",
     "start_time": "2018-05-29T19:17:41.668383Z"
    }
   },
   "outputs": [],
   "source": [
    "wa_diffs = wa_1000-wa_400\n",
    "\n",
    "wa_diffs_landmask = numpy.copy(wa_diffs)\n",
    "wa_diffs_landmask[:,:,landmask==0]=numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:17:47.778661Z",
     "start_time": "2018-05-29T19:17:47.663298Z"
    }
   },
   "outputs": [],
   "source": [
    "shapefile_name = cartopy.io.shapereader.natural_earth(resolution='110m',\n",
    "                                                      category='cultural',\n",
    "                                                      name='admin_0_countries')\n",
    "\n",
    "reader = cartopy.io.shapereader.Reader(shapefile_name)\n",
    "countries = reader.records()\n",
    "country = next(countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:17:50.608149Z",
     "start_time": "2018-05-29T19:17:50.380811Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = mp.subplots(1,1,subplot_kw={'projection':cartopy.crs.PlateCarree()})\n",
    "ax.set_extent([270,330,-30,20])\n",
    "ax.coastlines()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate midpoints so that fields are 28x178x288"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:18:55.799328Z",
     "start_time": "2018-05-29T19:17:53.201271Z"
    }
   },
   "outputs": [],
   "source": [
    "u_400_midpoints = 0.5*(u_400[:,:,:,1:]+u_400[:,:,:,:-1])\n",
    "u_400_midpoints = 0.5*(u_400_midpoints[:,1:,1:,1:]+u_400_midpoints[:,:-1,:-1,:-1]).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:20:34.588322Z",
     "start_time": "2018-05-29T19:18:55.807592Z"
    }
   },
   "outputs": [],
   "source": [
    "u_1000_midpoints = 0.5*(u_1000[:,:,:,1:]+u_1000[:,:,:,:-1])\n",
    "u_1000_midpoints = 0.5*(u_1000_midpoints[:,1:,1:,1:]+u_1000_midpoints[:,:-1,:-1,:-1]).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:21:25.421815Z",
     "start_time": "2018-05-29T19:20:34.596600Z"
    }
   },
   "outputs": [],
   "source": [
    "v_400_midpoints = 0.5*(v_400[:,:,1:,:]+v_400[:,:,:-1,:])\n",
    "v_400_midpoints = 0.5*(v_400_midpoints[:,1:,1:,1:]+v_400_midpoints[:,:-1,:-1,:-1]).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:22:56.505419Z",
     "start_time": "2018-05-29T19:21:25.430115Z"
    }
   },
   "outputs": [],
   "source": [
    "v_1000_midpoints = 0.5*(v_1000[:,:,1:,:]+v_1000[:,:,:-1,:])\n",
    "v_1000_midpoints = 0.5*(v_1000_midpoints[:,1:,1:,1:]+v_1000_midpoints[:,:-1,:-1,:-1]).values"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T19:22:56.540160Z",
     "start_time": "2018-05-29T19:22:56.513713Z"
    }
   },
   "source": [
    "omega_400_midpoints = (0.5*(omega_400[:,1:,1:,1:]+omega_400[:,:-1,:-1,:-1]))\n",
    "omega_1000_midpoints = (0.5*(omega_1000[:,1:,1:,1:]+omega_1000[:,:-1,:-1,:-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T20:32:02.689651Z",
     "start_time": "2018-05-29T20:31:59.371187Z"
    }
   },
   "outputs": [],
   "source": [
    "wa_400_midpoints = (0.5*(wa_400[:,1:,1:,1:]+wa_400[:,:-1,:-1,:-1]))\n",
    "wa_1000_midpoints = (0.5*(wa_1000[:,1:,1:,1:]+wa_1000[:,:-1,:-1,:-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T20:32:06.064952Z",
     "start_time": "2018-05-29T20:32:02.698074Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_midpoints = 0.5*(ptot_400[:,1:,1:,1:]+ptot_400[:,:-1,:-1,:-1])\n",
    "ptot_1000_midpoints = 0.5*(ptot_1000[:,1:,1:,1:]+ptot_1000[:,:-1,:-1,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T20:32:12.016522Z",
     "start_time": "2018-05-29T20:32:08.304408Z"
    }
   },
   "outputs": [],
   "source": [
    "geopot_400_midpoints = 0.5*(geopot_400[:,1:,1:,1:]+geopot_400[:,:-1,:-1,:-1])\n",
    "geopot_1000_midpoints = 0.5*(geopot_1000[:,1:,1:,1:]+geopot_1000[:,:-1,:-1,:-1])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-14T18:54:06.624284Z",
     "start_time": "2018-05-14T18:54:05.364040Z"
    }
   },
   "source": [
    "geopot_400_midpoints = 0.5*(geopot_400[:,1:,1:,1:]+geopot_400[:,:-1,:-1,:-1])\n",
    "geopot_1000_midpoints = 0.5*(geopot_1000[:,1:,1:,1:]+geopot_1000[:,:-1,:-1,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T20:34:55.716338Z",
     "start_time": "2018-05-29T20:32:20.348596Z"
    }
   },
   "outputs": [],
   "source": [
    "qvapor_400_midpoints = 0.5*(qvapor_400[:,1:,1:,1:]+qvapor_400[:,:-1,:-1,:-1]).values\n",
    "qvapor_1000_midpoints = 0.5*(qvapor_1000[:,1:,1:,1:]+qvapor_1000[:,:-1,:-1,:-1]).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T20:36:35.891978Z",
     "start_time": "2018-05-29T20:34:55.724640Z"
    }
   },
   "outputs": [],
   "source": [
    "qcloud_400_midpoints = 0.5*(qcloud_400[:,1:,1:,1:]+qcloud_400[:,:-1,:-1,:-1]).values\n",
    "qcloud_1000_midpoints = 0.5*(qcloud_1000[:,1:,1:,1:]+qcloud_1000[:,:-1,:-1,:-1]).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T20:38:38.906991Z",
     "start_time": "2018-05-29T20:36:35.900255Z"
    }
   },
   "outputs": [],
   "source": [
    "qrain_400_midpoints = 0.5*(qrain_400[:,1:,1:,1:]+qrain_400[:,:-1,:-1,:-1]).values\n",
    "qrain_1000_midpoints = 0.5*(qrain_1000[:,1:,1:,1:]+qrain_1000[:,:-1,:-1,:-1]).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T20:38:54.766075Z",
     "start_time": "2018-05-29T20:38:51.236332Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_1000_midpoints = 0.5*(ptot_1000[:,1:,1:,1:]+ptot_1000[:,:-1,:-1,:-1])\n",
    "ptot_400_midpoints = 0.5*(ptot_400[:,1:,1:,1:]+ptot_400[:,:-1,:-1,:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate dqdt terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T20:38:58.826105Z",
     "start_time": "2018-05-29T20:38:58.820436Z"
    }
   },
   "outputs": [],
   "source": [
    "ntime = 241\n",
    "nlev = 28"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dqdt will be automatically placed on midpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T22:42:11.561876Z",
     "start_time": "2018-05-29T22:42:08.998508Z"
    }
   },
   "outputs": [],
   "source": [
    "dqdt_400 = numpy.zeros((ntime,nlev,xlat.shape[0]-1,xlat.shape[1]-1))*numpy.nan\n",
    "dqdt_1000 = numpy.zeros((ntime,nlev,xlat.shape[0]-1,xlat.shape[1]-1))*numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T22:42:22.136107Z",
     "start_time": "2018-05-29T22:42:15.919571Z"
    }
   },
   "outputs": [],
   "source": [
    "for t in range(1,ntime-1):\n",
    "    dqdt_400[t,:,:,:] = (qvapor_400_midpoints[t+1,:,:,:]-qvapor_400_midpoints[t-1,:,:,:])/7200.\n",
    "for t in range(1,ntime-1):\n",
    "    dqdt_1000[t,:,:,:] = (qvapor_1000_midpoints[t+1,:,:,:]-qvapor_1000_midpoints[t-1,:,:,:])/7200."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## calculate dqcloud dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T22:42:29.365443Z",
     "start_time": "2018-05-29T22:42:26.587603Z"
    }
   },
   "outputs": [],
   "source": [
    "dqclouddt_400 = numpy.zeros((ntime,nlev,xlat.shape[0]-1,xlat.shape[1]-1))*numpy.nan\n",
    "dqclouddt_1000 = numpy.zeros((ntime,nlev,xlat.shape[0]-1,xlat.shape[1]-1))*numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T22:42:39.369913Z",
     "start_time": "2018-05-29T22:42:33.972295Z"
    }
   },
   "outputs": [],
   "source": [
    "for t in range(1,ntime-1):\n",
    "    dqclouddt_400[t,:,:,:] = (qcloud_400_midpoints[t+1,:,:,:]-qcloud_400_midpoints[t-1,:,:,:])/7200.\n",
    "for t in range(1,ntime-1):\n",
    "    dqclouddt_1000[t,:,:,:] = (qcloud_1000_midpoints[t+1,:,:,:]-qcloud_1000_midpoints[t-1,:,:,:])/7200."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## calculate dqraindt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T22:42:46.610226Z",
     "start_time": "2018-05-29T22:42:43.877943Z"
    }
   },
   "outputs": [],
   "source": [
    "dqraindt_400 = numpy.zeros((ntime,nlev,xlat.shape[0]-1,xlat.shape[1]-1))*numpy.nan\n",
    "dqraindt_1000 = numpy.zeros((ntime,nlev,xlat.shape[0]-1,xlat.shape[1]-1))*numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T22:43:28.229380Z",
     "start_time": "2018-05-29T22:42:51.589190Z"
    }
   },
   "outputs": [],
   "source": [
    "for t in range(1,ntime-1):\n",
    "    dqraindt_400[t,:,:,:] = (qrain_400_midpoints[t+1,:,:,:]-qrain_400_midpoints[t-1,:,:,:])/7200.\n",
    "for t in range(1,ntime-1):\n",
    "    dqraindt_1000[t,:,:,:] = (qrain_1000_midpoints[t+1,:,:,:]-qrain_1000_midpoints[t-1,:,:,:])/7200."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate convergence terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T22:44:13.959337Z",
     "start_time": "2018-05-29T22:43:32.990515Z"
    }
   },
   "outputs": [],
   "source": [
    "dudx_400 = numpy.diff(u_400.values, axis=3)/dx\n",
    "dudx_400 = 0.5*(dudx_400[:,1:,1:,1:]+dudx_400[:,:-1,:-1,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T22:45:36.207987Z",
     "start_time": "2018-05-29T22:44:18.915393Z"
    }
   },
   "outputs": [],
   "source": [
    "dudx_1000 = numpy.diff(u_1000.values, axis=3)/dx\n",
    "dudx_1000 = 0.5*(dudx_1000[:,1:,1:,1:]+dudx_1000[:,:-1,:-1,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T22:46:20.860866Z",
     "start_time": "2018-05-29T22:45:41.126040Z"
    }
   },
   "outputs": [],
   "source": [
    "dvdy_400 = numpy.diff(v_400.values, axis=2)/dy\n",
    "dvdy_400 = 0.5*(dvdy_400[:,1:,1:,1:]+dvdy_400[:,:-1,:-1,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T22:47:41.251813Z",
     "start_time": "2018-05-29T22:46:26.007534Z"
    }
   },
   "outputs": [],
   "source": [
    "dvdy_1000 = numpy.diff(v_1000.values, axis=2)/dy\n",
    "dvdy_1000 = 0.5*(dvdy_1000[:,1:,1:,1:]+dvdy_1000[:,:-1,:-1,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T22:47:48.137692Z",
     "start_time": "2018-05-29T22:47:46.355295Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_diff = numpy.diff(ptot_400, axis=1)\n",
    "ptot_1000_diff = numpy.diff(ptot_1000, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T00:45:28.903370Z",
     "start_time": "2018-05-30T00:45:27.548918Z"
    }
   },
   "outputs": [],
   "source": [
    "geopot_400_diff = numpy.diff(geopot_400, axis=1) # needed for convergence calculation\n",
    "geopot_1000_diff = numpy.diff(geopot_1000, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T00:45:43.645229Z",
     "start_time": "2018-05-30T00:45:41.270604Z"
    }
   },
   "outputs": [],
   "source": [
    "geopot_stag_400_diff = numpy.diff(geopot_stag_400, axis=1) # needed for advection calculation\n",
    "geopot_stag_1000_diff = numpy.diff(geopot_stag_1000, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T00:48:24.328484Z",
     "start_time": "2018-05-30T00:45:48.561582Z"
    }
   },
   "outputs": [],
   "source": [
    "dwdz_400 = numpy.diff(w_400, axis=1)/(geopot_stag_400_diff/g)\n",
    "dwdz_400 = 0.5*(dwdz_400[:,1:,1:,1:]+dwdz_400[:,:-1,:-1,:-1])\n",
    "\n",
    "dwdz_1000 = numpy.diff(w_1000, axis=1)/(geopot_stag_1000_diff/g)\n",
    "dwdz_1000 = 0.5*(dwdz_1000[:,1:,1:,1:]+dwdz_1000[:,:-1,:-1,:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pressure coords"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T17:03:52.126315Z",
     "start_time": "2018-05-11T17:01:32.598242Z"
    }
   },
   "source": [
    "cont_domegadp = numpy.diff(omega_400, axis=1)/ptot_400_diff\n",
    "cont_domegadp = 0.5*(cont_domegadp[:,:,1:,1:]+cont_domegadp[:,:,:-1,:-1])\n",
    "\n",
    "phys_domegadp = numpy.diff(omega_1000, axis=1)/ptot_1000_diff\n",
    "phys_domegadp = 0.5*(phys_domegadp[:,:,1:,1:]+phys_domegadp[:,:,:-1,:-1])\n",
    "\n",
    "convergence_400 = -qvapor_400_midpoints*(dudx_400 + dvdy_400 + cont_domegadp)\n",
    "convergence_1000 = -qvapor_1000_midpoints*(dudx_1000 + dvdy_1000 + phys_domegadp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T23:25:22.563461Z",
     "start_time": "2018-05-29T23:25:14.226860Z"
    }
   },
   "outputs": [],
   "source": [
    "convergence_400 = -qvapor_400_midpoints*(dudx_400 + dvdy_400 + dwdz_400)\n",
    "convergence_1000 = -qvapor_1000_midpoints*(dudx_1000 + dvdy_1000 + dwdz_1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculate vertical and horizontal terms separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T23:25:41.752767Z",
     "start_time": "2018-05-29T23:25:31.053498Z"
    }
   },
   "outputs": [],
   "source": [
    "convergence_400_vert = -qvapor_400_midpoints*dwdz_400 #cont_domegadp\n",
    "convergence_400_horiz = -qvapor_400_midpoints*(dudx_400 + dvdy_400)\n",
    "\n",
    "convergence_1000_vert = -qvapor_1000_midpoints*dwdz_1000 #phys_domegadp\n",
    "convergence_1000_horiz = -qvapor_1000_midpoints*(dudx_1000 + dvdy_1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate advection terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T23:26:37.483883Z",
     "start_time": "2018-05-29T23:25:50.731603Z"
    }
   },
   "outputs": [],
   "source": [
    "cont_dqdx = numpy.diff(qvapor_400.values, axis=3)/dx # diff in y, then take mean \n",
    "cont_dqdx = 0.5*(cont_dqdx[:,1:,1:,:]+cont_dqdx[:,:-1,:-1,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T23:28:10.412385Z",
     "start_time": "2018-05-29T23:26:46.767014Z"
    }
   },
   "outputs": [],
   "source": [
    "phys_dqdx = numpy.diff(qvapor_1000.values, axis=3)/dx # diff in y, then take mean \n",
    "phys_dqdx = 0.5*(phys_dqdx[:,1:,1:,:]+phys_dqdx[:,:-1,:-1,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T23:28:40.786324Z",
     "start_time": "2018-05-29T23:28:19.850435Z"
    }
   },
   "outputs": [],
   "source": [
    "cont_dqdy = numpy.diff(qvapor_400.values, axis=2)/dy # diff in y, then take mean \n",
    "cont_dqdy = 0.5*(cont_dqdy[:,1:,:,1:]+cont_dqdy[:,:-1,:,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T23:29:12.618845Z",
     "start_time": "2018-05-29T23:28:50.157315Z"
    }
   },
   "outputs": [],
   "source": [
    "phys_dqdy = numpy.diff(qvapor_1000.values, axis=2)/dy # diff in y, then take mean \n",
    "phys_dqdy = 0.5*(phys_dqdy[:,1:,:,1:]+phys_dqdy[:,:-1,:,:-1])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T17:16:32.188486Z",
     "start_time": "2018-05-11T17:15:26.104325Z"
    }
   },
   "source": [
    "cont_dqdp = numpy.diff(qvapor_400.values, axis=1)/ptot_400_diff\n",
    "cont_dqdp = 0.5*(cont_dqdp[:,:,1:,1:]+cont_dqdp[:,:,:-1,:-1])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T17:18:16.660511Z",
     "start_time": "2018-05-11T17:16:32.190859Z"
    }
   },
   "source": [
    "phys_dqdp = numpy.diff(qvapor_1000.values, axis=1)/ptot_1000_diff\n",
    "phys_dqdp = 0.5*(phys_dqdp[:,:,1:,1:]+phys_dqdp[:,:,:-1,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T23:29:47.253150Z",
     "start_time": "2018-05-29T23:29:22.657459Z"
    }
   },
   "outputs": [],
   "source": [
    "cont_dqdz = numpy.diff(qvapor_400.values, axis=1)/(geopot_400_diff/g)\n",
    "cont_dqdz = 0.5*(cont_dqdz[:,:,1:,1:]+cont_dqdz[:,:,:-1,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T00:48:50.139057Z",
     "start_time": "2018-05-30T00:48:29.063571Z"
    }
   },
   "outputs": [],
   "source": [
    "phys_dqdz = numpy.diff(qvapor_1000.values, axis=1)/(geopot_1000_diff/g)\n",
    "phys_dqdz = 0.5*(phys_dqdz[:,:,1:,1:]+phys_dqdz[:,:,:-1,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T00:49:07.948768Z",
     "start_time": "2018-05-30T00:48:55.111571Z"
    }
   },
   "outputs": [],
   "source": [
    "advection_400 = -(u_400_midpoints*cont_dqdx + v_400_midpoints*cont_dqdy + wa_400_midpoints*cont_dqdz)\n",
    "advection_1000 = -(u_1000_midpoints*phys_dqdx + v_1000_midpoints*phys_dqdy + wa_1000_midpoints*phys_dqdz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for pressure terms"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T17:19:30.735177Z",
     "start_time": "2018-05-11T17:18:16.668679Z"
    }
   },
   "source": [
    "advection_400 = -(u_400_midpoints*cont_dqdx + v_400_midpoints*cont_dqdy + omega_400_midpoints*cont_dqdp)\n",
    "advection_1000 = -(u_1000_midpoints*phys_dqdx + v_1000_midpoints*phys_dqdy + omega_1000_midpoints*phys_dqdp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculate vertical and horizontal terms separately"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T17:21:45.917603Z",
     "start_time": "2018-05-11T17:19:30.743351Z"
    }
   },
   "source": [
    "advection_400_vert = -w_400_midpoints*cont_dqdp\n",
    "advection_400_horiz = -(u_400_midpoints*cont_dqdx + v_400_midpoints*cont_dqdy)\n",
    "\n",
    "advection_1000_vert = -omega_1000_midpoints*phys_dqdp\n",
    "advection_1000_horiz = -(u_1000_midpoints*phys_dqdx + v_1000_midpoints*phys_dqdy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T00:49:58.491878Z",
     "start_time": "2018-05-30T00:49:12.887418Z"
    }
   },
   "outputs": [],
   "source": [
    "advection_400_vert = -wa_400_midpoints*cont_dqdz\n",
    "advection_400_horiz = -(u_400_midpoints*cont_dqdx + v_400_midpoints*cont_dqdy)\n",
    "\n",
    "advection_1000_vert = -wa_1000_midpoints*phys_dqdz\n",
    "advection_1000_horiz = -(u_1000_midpoints*phys_dqdx + v_1000_midpoints*phys_dqdy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# set ocean points to nan"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:39:39.064002Z",
     "start_time": "2018-04-16T21:27:22.948Z"
    }
   },
   "source": [
    "convergence_400[:,:,landmask_midpoints==0]=numpy.nan\n",
    "convergence_1000[:,:,landmask_midpoints==0]=numpy.nan"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:39:39.066424Z",
     "start_time": "2018-04-16T21:27:23.340Z"
    }
   },
   "source": [
    "advection_400[:,:,landmask_midpoints==0]=numpy.nan\n",
    "advection_1000[:,:,landmask_midpoints==0]=numpy.nan"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:39:39.068866Z",
     "start_time": "2018-04-16T21:27:23.668Z"
    }
   },
   "source": [
    "omega_400_midpoints[:,:,landmask_midpoints==0]=numpy.nan\n",
    "omega_1000_midpoints[:,:,landmask_midpoints==0]=numpy.nan"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:39:39.070183Z",
     "start_time": "2018-04-16T21:27:23.838Z"
    }
   },
   "source": [
    "dqdt_400[:,:,landmask_midpoints==0]=numpy.nan\n",
    "dqdt_1000[:,:,landmask_midpoints==0]=numpy.nan"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:39:39.071377Z",
     "start_time": "2018-04-16T21:27:24.006Z"
    }
   },
   "source": [
    "dqclouddt_400[:,:,landmask_midpoints==0]=numpy.nan\n",
    "dqclouddt_1000[:,:,landmask_midpoints==0]=numpy.nan"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:39:39.072567Z",
     "start_time": "2018-04-16T21:27:25.139Z"
    }
   },
   "source": [
    "dqraindt_400[:,:,landmask_midpoints==0]=numpy.nan\n",
    "dqraindt_1000[:,:,landmask_midpoints==0]=numpy.nan"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:39:39.073880Z",
     "start_time": "2018-04-16T21:27:25.421Z"
    }
   },
   "source": [
    "ptot_400[:,:,landmask==0]=numpy.nan\n",
    "ptot_1000[:,:,landmask==0]=numpy.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate time means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only time mean for qcloud, qrain, ptot\n",
    "\n",
    "Rest have day1 mean as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T00:52:33.605919Z",
     "start_time": "2018-05-30T00:50:03.858713Z"
    }
   },
   "outputs": [],
   "source": [
    "dqclouddt_400_tm = numpy.nanmean(dqclouddt_400[24:,:,:,:], axis=0)\n",
    "dqclouddt_1000_tm = numpy.nanmean(dqclouddt_1000[24:,:,:,:], axis=0)\n",
    "\n",
    "dqraindt_400_tm = numpy.nanmean(dqraindt_400[24:,:,:,:], axis=0)\n",
    "dqraindt_1000_tm = numpy.nanmean(dqraindt_1000[24:,:,:,:], axis=0)\n",
    "\n",
    "ptot_400_tm = numpy.nanmean(ptot_400[24:,:,:,:], axis=0)\n",
    "ptot_1000_tm = numpy.nanmean(ptot_1000[24:,:,:,:], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T00:53:44.193859Z",
     "start_time": "2018-05-30T00:52:39.045284Z"
    }
   },
   "outputs": [],
   "source": [
    "dqdt_400_tm = numpy.nanmean(dqdt_400[24:,:,:,:], axis=0)\n",
    "dqdt_1000_tm = numpy.nanmean(dqdt_1000[24:,:,:,:], axis=0)\n",
    "\n",
    "dqdt_400_tm_day1 = numpy.nanmean(dqdt_400[:24,:,:,:], axis=0)\n",
    "dqdt_1000_tm_day1 = numpy.nanmean(dqdt_1000[:24,:,:,:], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T00:54:27.419455Z",
     "start_time": "2018-05-30T00:53:49.478441Z"
    }
   },
   "outputs": [],
   "source": [
    "advection_400_tm = numpy.nanmean(advection_400[24:,:,:,:], axis=0)\n",
    "advection_1000_tm = numpy.nanmean(advection_1000[24:,:,:,:], axis=0)\n",
    "\n",
    "advection_400_tm_day1 = numpy.nanmean(advection_400[:24,:,:,:], axis=0)\n",
    "advection_1000_tm_day1 = numpy.nanmean(advection_1000[:24,:,:,:], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T00:55:06.855207Z",
     "start_time": "2018-05-30T00:54:33.158138Z"
    }
   },
   "outputs": [],
   "source": [
    "convergence_400_tm = numpy.nanmean(convergence_400[24:,:,:,:], axis=0)\n",
    "convergence_1000_tm = numpy.nanmean(convergence_1000[24:,:,:,:], axis=0)\n",
    "\n",
    "convergence_400_tm_day1 = numpy.nanmean(convergence_400[:24,:,:,:], axis=0)\n",
    "convergence_1000_tm_day1 = numpy.nanmean(convergence_1000[:24,:,:,:], axis=0)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T00:55:12.461837Z",
     "start_time": "2018-05-30T00:55:12.432674Z"
    }
   },
   "source": [
    "omega_400_tm = numpy.nanmean(omega_400_midpoints[24:,:,:,:], axis=0)\n",
    "omega_1000_tm = numpy.nanmean(omega_1000_midpoints[24:,:,:,:], axis=0)\n",
    "\n",
    "omega_400_tm_day1 = numpy.nanmean(omega_400_midpoints[:24,:,:,:], axis=0)\n",
    "omega_1000_tm_day1 = numpy.nanmean(omega_1000_midpoints[:24,:,:,:], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T00:57:17.329540Z",
     "start_time": "2018-05-30T00:56:36.215562Z"
    }
   },
   "outputs": [],
   "source": [
    "convergence_400_tm = numpy.nanmean(convergence_400[24:,:,:,:], axis=0)\n",
    "convergence_1000_tm = numpy.nanmean(convergence_1000[24:,:,:,:], axis=0)\n",
    "\n",
    "convergence_400_tm_day1 = numpy.nanmean(convergence_400[:24,:,:,:], axis=0)\n",
    "convergence_1000_tm_day1 = numpy.nanmean(convergence_1000[:24,:,:,:], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculate vertical and horizontal advection terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T00:58:39.650427Z",
     "start_time": "2018-05-30T00:57:22.905369Z"
    }
   },
   "outputs": [],
   "source": [
    "advection_400_vert_tm = numpy.nanmean(advection_400_vert[24:,:,:,:], axis=0)\n",
    "advection_1000_vert_tm = numpy.nanmean(advection_1000_vert[24:,:,:,:], axis=0)\n",
    "advection_400_vert_tm_day1 = numpy.nanmean(advection_400_vert[:24,:,:,:], axis=0)\n",
    "advection_1000_vert_tm_day1 = numpy.nanmean(advection_1000_vert[:24,:,:,:], axis=0)\n",
    "\n",
    "advection_400_horiz_tm = numpy.nanmean(advection_400_horiz[24:,:,:,:], axis=0)\n",
    "advection_1000_horiz_tm = numpy.nanmean(advection_1000_horiz[24:,:,:,:], axis=0)\n",
    "advection_400_horiz_tm_day1 = numpy.nanmean(advection_400_horiz[:24,:,:,:], axis=0)\n",
    "advection_1000_horiz_tm_day1 = numpy.nanmean(advection_1000_horiz[:24,:,:,:], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T00:59:53.710818Z",
     "start_time": "2018-05-30T00:58:45.128421Z"
    }
   },
   "outputs": [],
   "source": [
    "convergence_400_vert_tm = numpy.nanmean(convergence_400_vert[24:,:,:,:], axis=0)\n",
    "convergence_1000_vert_tm = numpy.nanmean(convergence_1000_vert[24:,:,:,:], axis=0)\n",
    "convergence_400_vert_tm_day1 = numpy.nanmean(convergence_400_vert[:24,:,:,:], axis=0)\n",
    "convergence_1000_vert_tm_day1 = numpy.nanmean(convergence_1000_vert[:24,:,:,:], axis=0)\n",
    "\n",
    "convergence_400_horiz_tm = numpy.nanmean(convergence_400_horiz[24:,:,:,:], axis=0)\n",
    "convergence_1000_horiz_tm = numpy.nanmean(convergence_1000_horiz[24:,:,:,:], axis=0)\n",
    "convergence_400_horiz_tm_day1 = numpy.nanmean(convergence_400_horiz[:24,:,:,:], axis=0)\n",
    "convergence_1000_horiz_tm_day1 = numpy.nanmean(convergence_1000_horiz[:24,:,:,:], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T00:59:59.445153Z",
     "start_time": "2018-05-30T00:59:59.439658Z"
    }
   },
   "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, dry_lon_hi, dry_lat_lo, dry_lat_hi = -60,-50,-5,10\n",
    "wet_lon_lo, wet_lon_hi, wet_lat_lo, wet_lat_hi = -80,-70,-5,10\n",
    "\n",
    "#dry_lon_lo, dry_lon_hi, dry_lat_lo, dry_lat_hi = -65,-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": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:00:05.199412Z",
     "start_time": "2018-05-30T01:00:05.194217Z"
    }
   },
   "outputs": [],
   "source": [
    "xlat_midpoints = 0.5*(xlat[1:,1:]+xlat[:-1,:-1])\n",
    "xlong_midpoints = 0.5*(xlong[1:,1:]+xlong[:-1,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:00:10.953279Z",
     "start_time": "2018-05-30T01:00:10.945045Z"
    }
   },
   "outputs": [],
   "source": [
    "wet_latlon_subset_midpoints = ((xlat_midpoints<=wet_lat_hi) & (xlat_midpoints>=wet_lat_lo) \\\n",
    "                               & (xlong_midpoints<=wet_lon_hi) & (xlong_midpoints>=wet_lon_lo))\n",
    "\n",
    "dry_latlon_subset_midpoints = ((xlat_midpoints<=dry_lat_hi) & (xlat_midpoints>=dry_lat_lo) \\\n",
    "                               & (xlong_midpoints<=dry_lon_hi) & (xlong_midpoints>=dry_lon_lo))\n",
    "\n",
    "lat_indices_midpoints = ((xlat_midpoints<=10)&(xlat_midpoints>=-10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:00:16.520431Z",
     "start_time": "2018-05-30T01:00:16.512416Z"
    }
   },
   "outputs": [],
   "source": [
    "wet_latlon_subset = ((xlat<=wet_lat_hi) & (xlat>=wet_lat_lo) \\\n",
    "                               & (xlong<=wet_lon_hi) & (xlong>=wet_lon_lo))\n",
    "\n",
    "dry_latlon_subset = ((xlat<=dry_lat_hi) & (xlat>=dry_lat_lo) \\\n",
    "                               & (xlong<=dry_lon_hi) & (xlong>=dry_lon_lo))\n",
    "\n",
    "lat_indices = ((xlat<=10)&(xlat>=-10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pulling out wet and dry regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:00:22.287034Z",
     "start_time": "2018-05-30T01:00:22.271326Z"
    }
   },
   "outputs": [],
   "source": [
    "dqdt_400_wet_region = numpy.nanmean(dqdt_400_tm[:,wet_latlon_subset_midpoints], axis=1)\n",
    "dqdt_400_dry_region = numpy.nanmean(dqdt_400_tm[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "dqdt_1000_wet_region = numpy.nanmean(dqdt_1000_tm[:,wet_latlon_subset_midpoints], axis=1)\n",
    "dqdt_1000_dry_region = numpy.nanmean(dqdt_1000_tm[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "dqdt_400_wet_region_day1 = numpy.nanmean(dqdt_400_tm_day1[:,wet_latlon_subset_midpoints], axis=1)\n",
    "dqdt_400_dry_region_day1 = numpy.nanmean(dqdt_400_tm_day1[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "dqdt_1000_wet_region_day1 = numpy.nanmean(dqdt_1000_tm_day1[:,wet_latlon_subset_midpoints], axis=1)\n",
    "dqdt_1000_dry_region_day1 = numpy.nanmean(dqdt_1000_tm_day1[:,dry_latlon_subset_midpoints], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:00:28.058739Z",
     "start_time": "2018-05-30T01:00:28.042916Z"
    }
   },
   "outputs": [],
   "source": [
    "convergence_400_wet_region = numpy.nanmean(convergence_400_tm[:,wet_latlon_subset_midpoints], axis=1)\n",
    "convergence_400_dry_region = numpy.nanmean(convergence_400_tm[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "convergence_1000_wet_region = numpy.nanmean(convergence_1000_tm[:,wet_latlon_subset_midpoints], axis=1)\n",
    "convergence_1000_dry_region = numpy.nanmean(convergence_1000_tm[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "convergence_400_wet_region_day1 = numpy.nanmean(convergence_400_tm_day1[:,wet_latlon_subset_midpoints], axis=1)\n",
    "convergence_400_dry_region_day1 = numpy.nanmean(convergence_400_tm_day1[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "convergence_1000_wet_region_day1 = numpy.nanmean(convergence_1000_tm_day1[:,wet_latlon_subset_midpoints], axis=1)\n",
    "convergence_1000_dry_region_day1 = numpy.nanmean(convergence_1000_tm_day1[:,dry_latlon_subset_midpoints], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:00:34.036259Z",
     "start_time": "2018-05-30T01:00:34.021208Z"
    }
   },
   "outputs": [],
   "source": [
    "advection_400_wet_region = numpy.nanmean(advection_400_tm[:,wet_latlon_subset_midpoints], axis=1)\n",
    "advection_400_dry_region = numpy.nanmean(advection_400_tm[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "advection_1000_wet_region = numpy.nanmean(advection_1000_tm[:,wet_latlon_subset_midpoints], axis=1)\n",
    "advection_1000_dry_region = numpy.nanmean(advection_1000_tm[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "advection_400_wet_region_day1 = numpy.nanmean(advection_400_tm_day1[:,wet_latlon_subset_midpoints], axis=1)\n",
    "advection_400_dry_region_day1 = numpy.nanmean(advection_400_tm_day1[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "advection_1000_wet_region_day1 = numpy.nanmean(advection_1000_tm_day1[:,wet_latlon_subset_midpoints], axis=1)\n",
    "advection_1000_dry_region_day1 = numpy.nanmean(advection_1000_tm_day1[:,dry_latlon_subset_midpoints], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:01:25.143884Z",
     "start_time": "2018-05-30T01:00:39.827654Z"
    }
   },
   "outputs": [],
   "source": [
    "wa_400_tm = numpy.nanmean(wa_400[24:,:,:,:], axis=0)\n",
    "wa_1000_tm = numpy.nanmean(wa_1000[24:,:,:,:], axis=0)\n",
    "\n",
    "wa_400_tm_day1 = numpy.nanmean(wa_400[:24,:,:,:], axis=0)\n",
    "wa_1000_tm_day1 = numpy.nanmean(wa_1000[:24,:,:,:], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:01:31.006595Z",
     "start_time": "2018-05-30T01:01:30.991291Z"
    }
   },
   "outputs": [],
   "source": [
    "wa_400_wet = numpy.nanmean(wa_400_tm[:,wet_latlon_subset],axis=1)\n",
    "wa_400_dry = numpy.nanmean(wa_400_tm[:,dry_latlon_subset],axis=1)\n",
    "\n",
    "wa_1000_wet = numpy.nanmean(wa_1000_tm[:,wet_latlon_subset],axis=1)\n",
    "wa_1000_dry = numpy.nanmean(wa_1000_tm[:,dry_latlon_subset],axis=1)\n",
    "\n",
    "wa_400_wet_day1 = numpy.nanmean(wa_400_tm_day1[:,wet_latlon_subset],axis=1)\n",
    "wa_400_dry_day1 = numpy.nanmean(wa_400_tm_day1[:,dry_latlon_subset],axis=1)\n",
    "\n",
    "wa_1000_wet_day1 = numpy.nanmean(wa_1000_tm_day1[:,wet_latlon_subset],axis=1)\n",
    "wa_1000_dry_day1 = numpy.nanmean(wa_1000_tm_day1[:,dry_latlon_subset],axis=1)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:01:36.713314Z",
     "start_time": "2018-05-30T01:01:36.687296Z"
    }
   },
   "source": [
    "omega_400_wet = numpy.nanmean(omega_400_tm[:,wet_latlon_subset_midpoints],axis=1)\n",
    "omega_400_dry = numpy.nanmean(omega_400_tm[:,dry_latlon_subset_midpoints],axis=1)\n",
    "\n",
    "omega_1000_wet = numpy.nanmean(omega_1000_tm[:,wet_latlon_subset_midpoints],axis=1)\n",
    "omega_1000_dry = numpy.nanmean(omega_1000_tm[:,dry_latlon_subset_midpoints],axis=1)\n",
    "\n",
    "omega_400_wet_day1 = numpy.nanmean(omega_400_tm_day1[:,wet_latlon_subset_midpoints],axis=1)\n",
    "omega_400_dry_day1 = numpy.nanmean(omega_400_tm_day1[:,dry_latlon_subset_midpoints],axis=1)\n",
    "\n",
    "omega_1000_wet_day1 = numpy.nanmean(omega_1000_tm_day1[:,wet_latlon_subset_midpoints],axis=1)\n",
    "omega_1000_dry_day1 = numpy.nanmean(omega_1000_tm_day1[:,dry_latlon_subset_midpoints],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:01:49.314124Z",
     "start_time": "2018-05-30T01:01:49.295791Z"
    }
   },
   "outputs": [],
   "source": [
    "convergence_400_vert_wet_region = numpy.nanmean(convergence_400_vert_tm[:,wet_latlon_subset_midpoints], axis=1)\n",
    "convergence_400_vert_dry_region = numpy.nanmean(convergence_400_vert_tm[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "convergence_1000_vert_wet_region = numpy.nanmean(convergence_1000_vert_tm[:,wet_latlon_subset_midpoints], axis=1)\n",
    "convergence_1000_vert_dry_region = numpy.nanmean(convergence_1000_vert_tm[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "convergence_400_vert_wet_region_day1 = numpy.nanmean(convergence_400_vert_tm_day1[:,wet_latlon_subset_midpoints], axis=1)\n",
    "convergence_400_vert_dry_region_day1 = numpy.nanmean(convergence_400_vert_tm_day1[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "convergence_1000_vert_wet_region_day1 = numpy.nanmean(convergence_1000_vert_tm_day1[:,wet_latlon_subset_midpoints], axis=1)\n",
    "convergence_1000_vert_dry_region_day1 = numpy.nanmean(convergence_1000_vert_tm_day1[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:01:55.532943Z",
     "start_time": "2018-05-30T01:01:55.517069Z"
    }
   },
   "outputs": [],
   "source": [
    "convergence_400_horiz_wet_region = numpy.nanmean(convergence_400_horiz_tm[:,wet_latlon_subset_midpoints], axis=1)\n",
    "convergence_400_horiz_dry_region = numpy.nanmean(convergence_400_horiz_tm[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "convergence_1000_horiz_wet_region = numpy.nanmean(convergence_1000_horiz_tm[:,wet_latlon_subset_midpoints], axis=1)\n",
    "convergence_1000_horiz_dry_region = numpy.nanmean(convergence_1000_horiz_tm[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "convergence_400_horiz_wet_region_day1 = numpy.nanmean(convergence_400_horiz_tm_day1[:,wet_latlon_subset_midpoints], axis=1)\n",
    "convergence_400_horiz_dry_region_day1 = numpy.nanmean(convergence_400_horiz_tm_day1[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "convergence_1000_horiz_wet_region_day1 = numpy.nanmean(convergence_1000_horiz_tm_day1[:,wet_latlon_subset_midpoints], axis=1)\n",
    "convergence_1000_horiz_dry_region_day1 = numpy.nanmean(convergence_1000_horiz_tm_day1[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:02:01.427186Z",
     "start_time": "2018-05-30T01:02:01.411435Z"
    }
   },
   "outputs": [],
   "source": [
    "advection_400_vert_wet_region = numpy.nanmean(advection_400_vert_tm[:,wet_latlon_subset_midpoints], axis=1)\n",
    "advection_400_vert_dry_region = numpy.nanmean(advection_400_vert_tm[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "advection_1000_vert_wet_region = numpy.nanmean(advection_1000_vert_tm[:,wet_latlon_subset_midpoints], axis=1)\n",
    "advection_1000_vert_dry_region = numpy.nanmean(advection_1000_vert_tm[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "advection_400_vert_wet_region_day1 = numpy.nanmean(advection_400_vert_tm_day1[:,wet_latlon_subset_midpoints], axis=1)\n",
    "advection_400_vert_dry_region_day1 = numpy.nanmean(advection_400_vert_tm_day1[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "advection_1000_vert_wet_region_day1 = numpy.nanmean(advection_1000_vert_tm_day1[:,wet_latlon_subset_midpoints], axis=1)\n",
    "advection_1000_vert_dry_region_day1 = numpy.nanmean(advection_1000_vert_tm_day1[:,dry_latlon_subset_midpoints], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:02:07.152079Z",
     "start_time": "2018-05-30T01:02:07.136391Z"
    }
   },
   "outputs": [],
   "source": [
    "advection_400_horiz_wet_region = numpy.nanmean(advection_400_horiz_tm[:,wet_latlon_subset_midpoints], axis=1)\n",
    "advection_400_horiz_dry_region = numpy.nanmean(advection_400_horiz_tm[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "advection_1000_horiz_wet_region = numpy.nanmean(advection_1000_horiz_tm[:,wet_latlon_subset_midpoints], axis=1)\n",
    "advection_1000_horiz_dry_region = numpy.nanmean(advection_1000_horiz_tm[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "advection_400_horiz_wet_region_day1 = numpy.nanmean(advection_400_horiz_tm_day1[:,wet_latlon_subset_midpoints], axis=1)\n",
    "advection_400_horiz_dry_region_day1 = numpy.nanmean(advection_400_horiz_tm_day1[:,dry_latlon_subset_midpoints], axis=1)\n",
    "\n",
    "advection_1000_horiz_wet_region_day1 = numpy.nanmean(advection_1000_horiz_tm_day1[:,wet_latlon_subset_midpoints], axis=1)\n",
    "advection_1000_horiz_dry_region_day1 = numpy.nanmean(advection_1000_horiz_tm_day1[:,dry_latlon_subset_midpoints], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot vertical changes using ptot change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:02:13.257209Z",
     "start_time": "2018-05-30T01:02:13.093195Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_dry_region_midpoints = numpy.nanmean(ptot_400_midpoints[:,:,dry_latlon_subset_midpoints], axis=(0,2))\n",
    "ptot_400_wet_region_midpoints = numpy.nanmean(ptot_400_midpoints[:,:,wet_latlon_subset_midpoints], axis=(0,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:02:19.366095Z",
     "start_time": "2018-05-30T01:02:19.179540Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_dry_region = numpy.nanmean(ptot_400[:,:,dry_latlon_subset], axis=(0,2))\n",
    "ptot_400_wet_region = numpy.nanmean(ptot_400[:,:,wet_latlon_subset], axis=(0,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:02:25.129935Z",
     "start_time": "2018-05-30T01:02:25.123696Z"
    }
   },
   "outputs": [],
   "source": [
    "diff_wet_region_dqdt = dqdt_1000_wet_region-dqdt_400_wet_region\n",
    "diff_dry_region_dqdt = dqdt_1000_dry_region-dqdt_400_dry_region\n",
    "\n",
    "diff_wet_region_dqdt_day1 = dqdt_1000_wet_region_day1-dqdt_400_wet_region_day1\n",
    "diff_dry_region_dqdt_day1 = dqdt_1000_dry_region_day1-dqdt_400_dry_region_day1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:02:31.069514Z",
     "start_time": "2018-05-30T01:02:31.062939Z"
    }
   },
   "outputs": [],
   "source": [
    "diff_wet_region_conv = convergence_1000_wet_region-convergence_400_wet_region\n",
    "diff_dry_region_conv = convergence_1000_dry_region-convergence_400_dry_region\n",
    "\n",
    "diff_wet_region_conv_day1 = convergence_1000_wet_region_day1-convergence_400_wet_region_day1\n",
    "diff_dry_region_conv_day1 = convergence_1000_dry_region_day1-convergence_400_dry_region_day1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:02:37.433672Z",
     "start_time": "2018-05-30T01:02:37.427009Z"
    }
   },
   "outputs": [],
   "source": [
    "diff_wet_region_adv = advection_1000_wet_region-advection_400_wet_region\n",
    "diff_dry_region_adv = advection_1000_dry_region-advection_400_dry_region\n",
    "\n",
    "diff_wet_region_adv_day1 = advection_1000_wet_region_day1-advection_400_wet_region_day1\n",
    "diff_dry_region_adv_day1 = advection_1000_dry_region_day1-advection_400_dry_region_day1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:02:43.210393Z",
     "start_time": "2018-05-30T01:02:43.204233Z"
    }
   },
   "outputs": [],
   "source": [
    "wa_diff_wet_region = wa_1000_wet-wa_400_wet\n",
    "wa_diff_dry_region = wa_1000_dry-wa_400_dry\n",
    "\n",
    "wa_diff_wet_region_day1 = wa_1000_wet_day1-wa_400_wet_day1\n",
    "wa_diff_dry_region_day1 = wa_1000_dry_day1-wa_400_dry_day1"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-07T18:32:43.351942Z",
     "start_time": "2018-05-07T18:32:43.345957Z"
    }
   },
   "source": [
    "omega_diff_wet_region = omega_1000_wet-omega_400_wet\n",
    "omega_diff_dry_region = omega_1000_dry-omega_400_dry\n",
    "\n",
    "omega_diff_wet_region_day1 = omega_1000_wet_day1-omega_400_wet_day1\n",
    "omega_diff_dry_region_day1 = omega_1000_dry_day1-omega_400_dry_day1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "same but for vert and horiz split up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:02:49.176023Z",
     "start_time": "2018-05-30T01:02:49.167404Z"
    }
   },
   "outputs": [],
   "source": [
    "diff_wet_region_vert_conv = convergence_1000_vert_wet_region-convergence_400_vert_wet_region\n",
    "diff_dry_region_vert_conv = convergence_1000_vert_dry_region-convergence_400_vert_dry_region\n",
    "\n",
    "diff_wet_region_vert_conv_day1 = convergence_1000_vert_wet_region_day1-convergence_400_vert_wet_region_day1\n",
    "diff_dry_region_vert_conv_day1 = convergence_1000_vert_dry_region_day1-convergence_400_vert_dry_region_day1\n",
    "\n",
    "diff_wet_region_vert_adv = advection_1000_vert_wet_region-advection_400_vert_wet_region\n",
    "diff_dry_region_vert_adv = advection_1000_vert_dry_region-advection_400_vert_dry_region\n",
    "\n",
    "diff_wet_region_vert_adv_day1 = advection_1000_vert_wet_region_day1-advection_400_vert_wet_region_day1\n",
    "diff_dry_region_vert_adv_day1 = advection_1000_vert_dry_region_day1-advection_400_vert_dry_region_day1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:02:55.160237Z",
     "start_time": "2018-05-30T01:02:55.151199Z"
    }
   },
   "outputs": [],
   "source": [
    "diff_wet_region_horiz_conv = convergence_1000_horiz_wet_region-convergence_400_horiz_wet_region\n",
    "diff_dry_region_horiz_conv = convergence_1000_horiz_dry_region-convergence_400_horiz_dry_region\n",
    "\n",
    "diff_wet_region_horiz_conv_day1 = convergence_1000_horiz_wet_region_day1-convergence_400_horiz_wet_region_day1\n",
    "diff_dry_region_horiz_conv_day1 = convergence_1000_horiz_dry_region_day1-convergence_400_horiz_dry_region_day1\n",
    "\n",
    "diff_wet_region_horiz_adv = advection_1000_horiz_wet_region-advection_400_horiz_wet_region\n",
    "diff_dry_region_horiz_adv = advection_1000_horiz_dry_region-advection_400_horiz_dry_region\n",
    "\n",
    "diff_wet_region_horiz_adv_day1 = advection_1000_horiz_wet_region_day1-advection_400_horiz_wet_region_day1\n",
    "diff_dry_region_horiz_adv_day1 = advection_1000_horiz_dry_region_day1-advection_400_horiz_dry_region_day1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot vertical profiles of changes in moisture advection (total)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T00:34:24.982838Z",
     "start_time": "2018-05-11T00:34:20.247815Z"
    }
   },
   "source": [
    "fontsize=12\n",
    "\n",
    "fig = mp.figure(figsize=(4.25,6))\n",
    "\n",
    "ax_list = []\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(221)\n",
    "\n",
    "ax.plot(diff_wet_region_adv*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moist. adv.')\n",
    "ax.plot(diff_wet_region_conv*1000.*86400.,ptot_400_wet_region_midpoints/100.,c='dodgerblue', label='moist. conv.')\n",
    "ax.plot(diff_wet_region_dqdt*1000.*86400.,ptot_400_wet_region_midpoints/100.,c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1')\n",
    "ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-0.9,0.55)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Andes\\n10d mean', x=0.07, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax_list.append(ax)\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(222)\n",
    "\n",
    "ax.plot(diff_dry_region_adv*1000.*86400.,ptot_400_dry_region_midpoints/100., c='green', label='moist. adv.')\n",
    "ax.plot(diff_dry_region_conv*1000.*86400.,ptot_400_dry_region_midpoints/100.,c='dodgerblue', label='moist. conv.')\n",
    "ax.plot(diff_dry_region_dqdt*1000.*86400.,ptot_400_dry_region_midpoints/100.,c='0.5',label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1')\n",
    "ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-0.5,0.25)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_yticklabels([])\n",
    "#ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Amazon\\n10d mean', x=0.07, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='860\\nhPa', x=0.3, y=860, ha='left', va='center', fontsize=fontsize)\n",
    "ax_list.append(ax)\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(223)\n",
    "\n",
    "ax.plot(diff_wet_region_adv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moist. adv.')\n",
    "ax.plot(diff_wet_region_conv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='dodgerblue', label='moist. conv.')\n",
    "ax.plot(diff_wet_region_dqdt_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1')\n",
    "ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-0.9,0.55)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Andes\\nday 1', x=0.07, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax_list.append(ax)\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(224)\n",
    "\n",
    "ax.plot(diff_dry_region_adv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moist. adv.')\n",
    "ax.plot(diff_dry_region_conv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='dodgerblue', label='moist. conv.')\n",
    "ax.plot(diff_dry_region_dqdt_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1')\n",
    "ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-0.5,0.25)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_yticklabels([])\n",
    "ax_list.append(ax)\n",
    "ax.text(s='860\\nhPa', x=0.3, y=860, ha='left', va='center', fontsize=fontsize)\n",
    "ax.text(s='Amazon\\nday 1', x=0.07, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "legend = ax_list[1].legend(handles, labels, fontsize=fontsize, bbox_to_anchor=(1.05,1), edgecolor='None', \\\n",
    "                   facecolor='None', fancybox=False, bbox_transform=ax_list[1].transAxes, loc='upper left', \\\n",
    "                   borderaxespad=0., borderpad=0.25, handlelength=1.0, handletextpad=0.5, \\\n",
    "                   columnspacing=0.5, markerscale=1)\n",
    "\n",
    "fig.savefig('mean_moisture_TOTAL_advection_10d_and_day1.png', dpi=600, transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot climatological moisture advection/convergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:03:07.710131Z",
     "start_time": "2018-05-30T01:03:04.022780Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "\n",
    "fig = mp.figure(figsize=(4.5,6))\n",
    "\n",
    "ax_list = []\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(221)\n",
    "\n",
    "ax.plot(advection_400_wet_region_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moist. adv.')\n",
    "ax.plot(convergence_400_wet_region_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='dodgerblue', label='moist. conv.')\n",
    "#ax.plot(diff_wet_region_dqdt_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-10,10)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Andes\\nday 1', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='(a)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "ax_list.append(ax)\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(222)\n",
    "\n",
    "ax.plot(advection_400_dry_region_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moisture\\nadvection')\n",
    "ax.plot(convergence_400_dry_region_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='dodgerblue', label='moisture\\nconvergence')\n",
    "#ax.plot(diff_dry_region_dqdt_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-5,5)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_yticklabels([])\n",
    "ax_list.append(ax)\n",
    "#ax.text(s='860\\nhPa', x=0.3, y=860, ha='left', va='center', fontsize=fontsize)\n",
    "ax.text(s='Amazon\\nday 1', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='(b)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(223)\n",
    "\n",
    "ax.plot(advection_400_wet_region*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moisture\\nadvection')\n",
    "ax.plot(convergence_400_wet_region*1000.*86400.,ptot_400_wet_region_midpoints/100.,c='dodgerblue', label='moisture\\nconvergence')\n",
    "#ax.plot(diff_wet_region_dqdt*1000.*86400.,ptot_400_wet_region_midpoints/100.,c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-10,10)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Andes\\ndays 2–10', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='(c)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "ax_list.append(ax)\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(224)\n",
    "\n",
    "ax.plot(advection_400_dry_region*1000.*86400.,ptot_400_dry_region_midpoints/100., c='green', label='moisture\\nadvection')\n",
    "ax.plot(convergence_400_dry_region*1000.*86400.,ptot_400_dry_region_midpoints/100.,c='dodgerblue', label='moisture\\nconvergence')\n",
    "#ax.plot(diff_dry_region_dqdt*1000.*86400.,ptot_400_dry_region_midpoints/100.,c='0.5',label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-5,5)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_yticklabels([])\n",
    "#ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Amazon\\ndays 2–10', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "#ax.text(s='860\\nhPa', x=0.3, y=860, ha='left', va='center', fontsize=fontsize)\n",
    "ax.text(s='(d)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "ax_list.append(ax)\n",
    "\n",
    "fig.tight_layout(h_pad=1)\n",
    "\n",
    "handles, labels = ax_list[1].get_legend_handles_labels()\n",
    "legend = ax_list[1].legend(handles, labels, fontsize=fontsize, bbox_to_anchor=(1.05,1), edgecolor='None', \\\n",
    "                   facecolor='None', fancybox=False, bbox_transform=ax_list[1].transAxes, loc='upper left', \\\n",
    "                   borderaxespad=0., borderpad=0.25, handlelength=1.0, handletextpad=0.5, \\\n",
    "                   columnspacing=0.5, markerscale=1)\n",
    "\n",
    "fig.savefig('manuscript_figXX_climatological_moisture_budget_TOTAL_10d_and_day1.png', dpi=600, \\\n",
    "            transparent=True, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot individual conv, div, dqdt terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:03:16.858782Z",
     "start_time": "2018-05-30T01:03:13.548573Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "\n",
    "fig = mp.figure(figsize=(4.5,6))\n",
    "\n",
    "ax_list = []\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(221)\n",
    "\n",
    "ax.plot(diff_wet_region_adv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moist. adv.')\n",
    "ax.plot(diff_wet_region_conv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='dodgerblue', label='moist. conv.')\n",
    "ax.plot(diff_wet_region_dqdt_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-1.5,1)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Andes\\nday 1', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='(a)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "ax_list.append(ax)\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(222)\n",
    "\n",
    "ax.plot(diff_dry_region_adv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moisture\\nadvection')\n",
    "ax.plot(diff_dry_region_conv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='dodgerblue', label='moisture\\nconvergence')\n",
    "ax.plot(diff_dry_region_dqdt_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-0.75,0.5)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_yticklabels([])\n",
    "ax_list.append(ax)\n",
    "#ax.text(s='860\\nhPa', x=0.3, y=860, ha='left', va='center', fontsize=fontsize)\n",
    "ax.text(s='Amazon\\nday 1', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='(b)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(223)\n",
    "\n",
    "ax.plot(diff_wet_region_adv*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moisture\\nadvection')\n",
    "ax.plot(diff_wet_region_conv*1000.*86400.,ptot_400_wet_region_midpoints/100.,c='dodgerblue', label='moisture\\nconvergence')\n",
    "ax.plot(diff_wet_region_dqdt*1000.*86400.,ptot_400_wet_region_midpoints/100.,c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-1.5,1)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Andes\\ndays 2–10', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='(c)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "ax_list.append(ax)\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(224)\n",
    "\n",
    "ax.plot(diff_dry_region_adv*1000.*86400.,ptot_400_dry_region_midpoints/100., c='green', label='moisture\\nadvection')\n",
    "ax.plot(diff_dry_region_conv*1000.*86400.,ptot_400_dry_region_midpoints/100.,c='dodgerblue', label='moisture\\nconvergence')\n",
    "ax.plot(diff_dry_region_dqdt*1000.*86400.,ptot_400_dry_region_midpoints/100.,c='0.5',label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-0.75,0.5)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_yticklabels([])\n",
    "#ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Amazon\\ndays 2–10', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "#ax.text(s='860\\nhPa', x=0.3, y=860, ha='left', va='center', fontsize=fontsize)\n",
    "ax.text(s='(d)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "ax_list.append(ax)\n",
    "\n",
    "fig.tight_layout(h_pad=1)\n",
    "\n",
    "handles, labels = ax_list[1].get_legend_handles_labels()\n",
    "legend = ax_list[1].legend(handles, labels, fontsize=fontsize, bbox_to_anchor=(1.05,1), edgecolor='None', \\\n",
    "                   facecolor='None', fancybox=False, bbox_transform=ax_list[1].transAxes, loc='upper left', \\\n",
    "                   borderaxespad=0., borderpad=0.25, handlelength=1.0, handletextpad=0.5, \\\n",
    "                   columnspacing=0.5, markerscale=1)\n",
    "\n",
    "fig.savefig('manuscript_figXX_mean_moisture_budget_TOTAL_10d_and_day1.png', dpi=600, \\\n",
    "            transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:03:26.481258Z",
     "start_time": "2018-05-30T01:03:24.648894Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "\n",
    "fig = mp.figure(figsize=(4.5,3))\n",
    "\n",
    "ax_list = []\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(121)\n",
    "\n",
    "ax.plot(diff_wet_region_adv*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moisture\\nadvection')\n",
    "ax.plot(diff_wet_region_conv*1000.*86400.,ptot_400_wet_region_midpoints/100.,c='dodgerblue', label='moisture\\nconvergence')\n",
    "ax.plot(diff_wet_region_dqdt*1000.*86400.,ptot_400_wet_region_midpoints/100.,c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-1.5,1)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Andes\\ndays 2–10', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='(a)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "ax_list.append(ax)\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(122)\n",
    "\n",
    "ax.plot(diff_dry_region_adv*1000.*86400.,ptot_400_dry_region_midpoints/100., c='green', label='moisture\\nadvection')\n",
    "ax.plot(diff_dry_region_conv*1000.*86400.,ptot_400_dry_region_midpoints/100.,c='dodgerblue', label='moisture\\nconvergence')\n",
    "ax.plot(diff_dry_region_dqdt*1000.*86400.,ptot_400_dry_region_midpoints/100.,c='0.5',label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-0.75,0.5)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_yticklabels([])\n",
    "#ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Amazon\\ndays 2–10', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "#ax.text(s='860\\nhPa', x=0.3, y=860, ha='left', va='center', fontsize=fontsize)\n",
    "ax.text(s='(b)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "ax_list.append(ax)\n",
    "\n",
    "fig.tight_layout(h_pad=1)\n",
    "\n",
    "handles, labels = ax_list[1].get_legend_handles_labels()\n",
    "legend = ax_list[1].legend(handles, labels, fontsize=fontsize, bbox_to_anchor=(1.05,1), edgecolor='None', \\\n",
    "                   facecolor='None', fancybox=False, bbox_transform=ax_list[1].transAxes, loc='upper left', \\\n",
    "                   borderaxespad=0., borderpad=0.25, handlelength=1.0, handletextpad=0.5, \\\n",
    "                   columnspacing=0.5, markerscale=1)\n",
    "\n",
    "fig.savefig('manuscript_figXX_mean_moisture_budget_TOTAL_10d_ONLY.png', dpi=600, \\\n",
    "            transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Do same for vertical terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:03:44.606794Z",
     "start_time": "2018-05-30T01:03:41.189858Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "\n",
    "fig = mp.figure(figsize=(4.5,6))\n",
    "\n",
    "ax_list = []\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(221)\n",
    "\n",
    "ax.plot(diff_wet_region_vert_adv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moist. adv.')\n",
    "ax.plot(diff_wet_region_vert_conv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='dodgerblue', label='moist. conv.')\n",
    "ax.plot(diff_wet_region_dqdt_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-1.5,1)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Andes\\nday 1', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='(a)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "ax_list.append(ax)\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(222)\n",
    "\n",
    "ax.plot(diff_dry_region_vert_adv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moisture\\nadvection')\n",
    "ax.plot(diff_dry_region_vert_conv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='dodgerblue', label='moisture\\nconvergence')\n",
    "ax.plot(diff_dry_region_dqdt_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-0.75,0.5)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_yticklabels([])\n",
    "ax_list.append(ax)\n",
    "#ax.text(s='860\\nhPa', x=0.3, y=860, ha='left', va='center', fontsize=fontsize)\n",
    "ax.text(s='Amazon\\nday 1', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='(b)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(223)\n",
    "\n",
    "ax.plot(diff_wet_region_vert_adv*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moisture\\nadvection')\n",
    "ax.plot(diff_wet_region_vert_conv*1000.*86400.,ptot_400_wet_region_midpoints/100.,c='dodgerblue', label='moisture\\nconvergence')\n",
    "ax.plot(diff_wet_region_dqdt*1000.*86400.,ptot_400_wet_region_midpoints/100.,c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-1.5,1)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Andes\\ndays 2–10', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='(c)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "ax_list.append(ax)\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(224)\n",
    "\n",
    "ax.plot(diff_dry_region_vert_adv*1000.*86400.,ptot_400_dry_region_midpoints/100., c='green', label='moisture\\nadvection')\n",
    "ax.plot(diff_dry_region_vert_conv*1000.*86400.,ptot_400_dry_region_midpoints/100.,c='dodgerblue', label='moisture\\nconvergence')\n",
    "ax.plot(diff_dry_region_dqdt*1000.*86400.,ptot_400_dry_region_midpoints/100.,c='0.5',label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-0.75,0.5)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_yticklabels([])\n",
    "#ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Amazon\\ndays 2–10', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "#ax.text(s='860\\nhPa', x=0.3, y=860, ha='left', va='center', fontsize=fontsize)\n",
    "ax.text(s='(d)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "ax_list.append(ax)\n",
    "\n",
    "fig.tight_layout(h_pad=1)\n",
    "\n",
    "handles, labels = ax_list[1].get_legend_handles_labels()\n",
    "legend = ax_list[1].legend(handles, labels, fontsize=fontsize, bbox_to_anchor=(1.05,1), edgecolor='None', \\\n",
    "                   facecolor='None', fancybox=False, bbox_transform=ax_list[1].transAxes, loc='upper left', \\\n",
    "                   borderaxespad=0., borderpad=0.25, handlelength=1.0, handletextpad=0.5, \\\n",
    "                   columnspacing=0.5, markerscale=1)\n",
    "\n",
    "axpos1 = ax_list[0].get_position()\n",
    "axpos2 = ax_list[1].get_position()\n",
    "\n",
    "fig.text(s='vertical terms', x=0.5*(axpos1.x0+axpos2.x1), y=1.02, transform=fig.transFigure, ha='center', va='bottom', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('manuscript_figXX_mean_moisture_budget_VERTICAL_10d_and_day1.png', dpi=600, \\\n",
    "            transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# horizontal terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:03:53.991269Z",
     "start_time": "2018-05-30T01:03:50.637934Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "\n",
    "fig = mp.figure(figsize=(4.5,6))\n",
    "\n",
    "ax_list = []\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(221)\n",
    "\n",
    "ax.plot(diff_wet_region_horiz_adv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moist. adv.')\n",
    "ax.plot(diff_wet_region_horiz_conv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='dodgerblue', label='moist. conv.')\n",
    "ax.plot(diff_wet_region_dqdt_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-1.5,1)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Andes\\nday 1', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='(a)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "ax_list.append(ax)\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(222)\n",
    "\n",
    "ax.plot(diff_dry_region_horiz_adv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moisture\\nadvection')\n",
    "ax.plot(diff_dry_region_horiz_conv_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='dodgerblue', label='moisture\\nconvergence')\n",
    "ax.plot(diff_dry_region_dqdt_day1*1000.*86400.,ptot_400_wet_region_midpoints/100., c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-0.75,0.5)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_yticklabels([])\n",
    "ax_list.append(ax)\n",
    "#ax.text(s='860\\nhPa', x=0.3, y=860, ha='left', va='center', fontsize=fontsize)\n",
    "ax.text(s='Amazon\\nday 1', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='(b)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(223)\n",
    "\n",
    "ax.plot(diff_wet_region_horiz_adv*1000.*86400.,ptot_400_wet_region_midpoints/100., c='green', label='moisture\\nadvection')\n",
    "ax.plot(diff_wet_region_horiz_conv*1000.*86400.,ptot_400_wet_region_midpoints/100.,c='dodgerblue', label='moisture\\nconvergence')\n",
    "ax.plot(diff_wet_region_dqdt*1000.*86400.,ptot_400_wet_region_midpoints/100.,c='0.5', label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-1.5,1)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Andes\\ndays 2–10', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='(c)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "ax_list.append(ax)\n",
    "\n",
    "#####\n",
    "ax = fig.add_subplot(224)\n",
    "\n",
    "ax.plot(diff_dry_region_horiz_adv*1000.*86400.,ptot_400_dry_region_midpoints/100., c='green', label='moisture\\nadvection')\n",
    "ax.plot(diff_dry_region_horiz_conv*1000.*86400.,ptot_400_dry_region_midpoints/100.,c='dodgerblue', label='moisture\\nconvergence')\n",
    "ax.plot(diff_dry_region_dqdt*1000.*86400.,ptot_400_dry_region_midpoints/100.,c='0.5',label='dq/dt')\n",
    "ax.axvline(0, ls='--', c='0.1', zorder=1)\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,200)\n",
    "ax.set_xlim(-0.75,0.5)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_yticklabels([])\n",
    "#ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='Amazon\\ndays 2–10', x=0.05, y=0.95, ha='left', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "#ax.text(s='860\\nhPa', x=0.3, y=860, ha='left', va='center', fontsize=fontsize)\n",
    "ax.text(s='(d)', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.yaxis.grid()\n",
    "ax_list.append(ax)\n",
    "\n",
    "fig.tight_layout(h_pad=1)\n",
    "\n",
    "handles, labels = ax_list[1].get_legend_handles_labels()\n",
    "legend = ax_list[1].legend(handles, labels, fontsize=fontsize, bbox_to_anchor=(1.05,1), edgecolor='None', \\\n",
    "                   facecolor='None', fancybox=False, bbox_transform=ax_list[1].transAxes, loc='upper left', \\\n",
    "                   borderaxespad=0., borderpad=0.25, handlelength=1.0, handletextpad=0.5, \\\n",
    "                   columnspacing=0.5, markerscale=1)\n",
    "\n",
    "axpos1 = ax_list[0].get_position()\n",
    "axpos2 = ax_list[1].get_position()\n",
    "\n",
    "fig.text(s='horizontal terms', x=0.5*(axpos1.x0+axpos2.x1), y=1.02, transform=fig.transFigure, ha='center', va='bottom', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('manuscript_figXX_mean_moisture_budget_HORIZONTAL_10d_and_day1.png', dpi=600, \\\n",
    "            transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot omega vertical structure change"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-08T15:54:26.975822Z",
     "start_time": "2018-05-08T15:54:26.460405Z"
    }
   },
   "source": [
    "fontsize=12\n",
    "\n",
    "fig = mp.figure(figsize=(6,8))\n",
    "\n",
    "ax = fig.add_subplot(221)\n",
    "\n",
    "ax.plot(omega_diff_wet_region*86400./100.,ptot_400_wet_region_midpoints/100., c='firebrick', label='10d mean')\n",
    "ax.plot(omega_diff_wet_region_day1*86400./100.,ptot_400_wet_region_midpoints/100., c='dodgerblue', label='day 1')\n",
    "ax.axvline(0, ls='--', c='0.1')\n",
    "ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,50)\n",
    "\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('$\\Delta\\omega$ (hPa day$^{\\,-1}$)', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "\n",
    "ax.text(s='Andes', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = handles[::-1]; labels=labels[::-1] # to reverse\n",
    "legend = ax.legend(handles, labels, fontsize=fontsize, bbox_to_anchor=(0.03,0.99), edgecolor='None', \\\n",
    "                   facecolor='None', fancybox=False, bbox_transform=ax.transAxes, loc='upper left', \\\n",
    "                   borderaxespad=0, borderpad=0.25, handlelength=1.0, handletextpad=0.5, \\\n",
    "                   columnspacing=0.5, markerscale=1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(222)\n",
    "\n",
    "ax.plot(omega_diff_dry_region*86400./100.,ptot_400_dry_region_midpoints/100., c='firebrick')\n",
    "ax.plot(omega_diff_dry_region_day1*86400./100.,ptot_400_dry_region_midpoints/100., c='dodgerblue')\n",
    "ax.axvline(0, ls='--', c='0.1')\n",
    "ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,50)\n",
    "\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('$\\Delta\\omega$ (hPa day$^{\\,-1}$)', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "\n",
    "ax.text(s='Amazon', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(223)\n",
    "\n",
    "ax.plot(omega_400_wet*86400./100.,ptot_400_wet_region_midpoints/100., c='firebrick', label='10d mean')\n",
    "ax.axvline(0, ls='--', c='0.1')\n",
    "ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,50)\n",
    "\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('$\\omega$ (hPa day$^{\\,-1}$)', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "\n",
    "ax.text(s='Andes', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = handles[::-1]; labels=labels[::-1] # to reverse\n",
    "legend = ax.legend(handles, labels, fontsize=fontsize, bbox_to_anchor=(0.03,0.99), edgecolor='None', \\\n",
    "                   facecolor='None', fancybox=False, bbox_transform=ax.transAxes, loc='upper left', \\\n",
    "                   borderaxespad=0, borderpad=0.25, handlelength=1.0, handletextpad=0.5, \\\n",
    "                   columnspacing=0.5, markerscale=1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(224)\n",
    "\n",
    "ax.plot(omega_400_dry*86400./100.,ptot_400_dry_region_midpoints/100., c='firebrick')\n",
    "ax.axvline(0, ls='--', c='0.1')\n",
    "ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,50)\n",
    "\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('$\\omega$ (hPa day$^{\\,-1}$)', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "\n",
    "ax.text(s='Amazon', x=0, y=1.02, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig('vertical_velocity_andes_amazon.png', dpi=600, transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# lat subsets and latmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:04:05.229415Z",
     "start_time": "2018-05-30T01:04:00.016111Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_midpoints_lat_subset = ptot_400_midpoints[:,:,lat_indices_midpoints[:,0],:]\n",
    "ptot_1000_midpoints_lat_subset = ptot_1000_midpoints[:,:,lat_indices_midpoints[:,0],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:04:15.991121Z",
     "start_time": "2018-05-30T01:04:11.214257Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_lat_subset = ptot_400[:,:,lat_indices[:,0],:]\n",
    "ptot_1000_lat_subset = ptot_1000[:,:,lat_indices[:,0],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:04:32.139934Z",
     "start_time": "2018-05-30T01:04:22.270719Z"
    }
   },
   "outputs": [],
   "source": [
    "advection_400_lat_subset = advection_400[:,:,lat_indices_midpoints[:,0],:]\n",
    "advection_1000_lat_subset = advection_1000[:,:,lat_indices_midpoints[:,0],:]\n",
    "\n",
    "convergence_400_lat_subset = convergence_400[:,:,lat_indices_midpoints[:,0],:]\n",
    "convergence_1000_lat_subset = convergence_1000[:,:,lat_indices_midpoints[:,0],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:05:01.820388Z",
     "start_time": "2018-05-30T01:04:39.002280Z"
    }
   },
   "outputs": [],
   "source": [
    "cont_adv_latmean = numpy.nanmean(advection_400_lat_subset, axis=2)\n",
    "phys_adv_latmean = numpy.nanmean(advection_1000_lat_subset, axis=2)\n",
    "\n",
    "cont_conv_latmean = numpy.nanmean(convergence_400_lat_subset, axis=2)\n",
    "phys_conv_latmean = numpy.nanmean(convergence_1000_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:05:17.561943Z",
     "start_time": "2018-05-30T01:05:08.205927Z"
    }
   },
   "outputs": [],
   "source": [
    "dqdt_400_lat_subset = dqdt_400[:,:,lat_indices_midpoints[:,0],:]\n",
    "dqdt_1000_lat_subset = dqdt_1000[:,:,lat_indices_midpoints[:,0],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:06:17.576426Z",
     "start_time": "2018-05-30T01:05:24.291228Z"
    }
   },
   "outputs": [],
   "source": [
    "dqclouddt_400_lat_subset = dqclouddt_400[:,:,lat_indices_midpoints[:,0],:]\n",
    "dqclouddt_1000_lat_subset = dqclouddt_1000[:,:,lat_indices_midpoints[:,0],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:09:56.575433Z",
     "start_time": "2018-05-30T01:06:25.344011Z"
    }
   },
   "outputs": [],
   "source": [
    "dqraindt_400_lat_subset = dqraindt_400[:,:,lat_indices_midpoints[:,0],:]\n",
    "dqraindt_1000_lat_subset = dqraindt_1000[:,:,lat_indices_midpoints[:,0],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:11:30.159059Z",
     "start_time": "2018-05-30T01:10:03.599689Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_latmean = numpy.nanmean(ptot_400_lat_subset, axis=2)\n",
    "ptot_1000_latmean = numpy.nanmean(ptot_1000_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:11:45.796414Z",
     "start_time": "2018-05-30T01:11:37.367022Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_midpoints_latmean = numpy.nanmean(ptot_400_midpoints_lat_subset, axis=2)\n",
    "ptot_1000_midpoints_latmean = numpy.nanmean(ptot_1000_midpoints_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:12:58.212365Z",
     "start_time": "2018-05-30T01:11:52.852504Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: Mean of empty slice\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: Mean of empty slice\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "dqdt_400_latmean = numpy.nanmean(dqdt_400_lat_subset, axis=2)\n",
    "dqdt_1000_latmean = numpy.nanmean(dqdt_1000_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:13:45.826614Z",
     "start_time": "2018-05-30T01:13:05.301234Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: Mean of empty slice\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: Mean of empty slice\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "dqclouddt_400_latmean = numpy.nanmean(dqclouddt_400_lat_subset, axis=2)\n",
    "dqclouddt_1000_latmean = numpy.nanmean(dqclouddt_1000_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:14:05.152677Z",
     "start_time": "2018-05-30T01:13:52.778787Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: Mean of empty slice\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: Mean of empty slice\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "dqraindt_400_latmean = numpy.nanmean(dqraindt_400_lat_subset, axis=2)\n",
    "dqraindt_1000_latmean = numpy.nanmean(dqraindt_1000_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:14:14.293068Z",
     "start_time": "2018-05-30T01:14:12.293538Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_midpoints_lat_subset = ptot_400_midpoints[:,:,lat_indices_midpoints[:,0],:]\n",
    "ptot_400_midpoints_latmean = numpy.nanmean(ptot_400_midpoints_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:14:21.433250Z",
     "start_time": "2018-05-30T01:14:21.425945Z"
    }
   },
   "outputs": [],
   "source": [
    "xx,zz=numpy.meshgrid(xlong[0,:],ptot_400[0,:,:,0].mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:14:28.655587Z",
     "start_time": "2018-05-30T01:14:28.572955Z"
    }
   },
   "outputs": [],
   "source": [
    "diff_adv_latmean = phys_adv_latmean-cont_adv_latmean\n",
    "diff_conv_latmean = phys_conv_latmean-cont_conv_latmean\n",
    "diff_dqdt_latmean = dqdt_1000_latmean-dqdt_400_latmean\n",
    "diff_dqclouddt_latmean = dqclouddt_1000_latmean-dqclouddt_400_latmean\n",
    "diff_dqraindt_latmean = dqraindt_1000_latmean-dqraindt_400_latmean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:14:55.375933Z",
     "start_time": "2018-05-30T01:14:36.031361Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: Mean of empty slice\n",
      "  \n",
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: Mean of empty slice\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "wa_diffs_lat_subset = wa_diffs_landmask[:,:,lat_indices[:,0],:]\n",
    "wa_diffs_latmean = numpy.nanmean(wa_diffs_lat_subset, axis=2)\n",
    "wa_diffs_time_latmean = numpy.nanmean(wa_diffs_latmean[24:,:,:], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:15:02.719857Z",
     "start_time": "2018-05-30T01:15:02.695028Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_time_latmean = numpy.nanmean(ptot_400_latmean, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gridspec version of plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:37:22.265691Z",
     "start_time": "2018-05-30T01:37:16.518525Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x504 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "\n",
    "fig = mp.figure(figsize=(6.5,7))\n",
    "gs = matplotlib.gridspec.GridSpec(3,2,height_ratios=[2,1,3])\n",
    "\n",
    "xticks_deg_E=numpy.array((280,290,300,310,320))\n",
    "xticks = -(360-xticks_deg_E)\n",
    "xticks_format=[str(tick)+DEGREE_SIGN+'E' for tick in xticks_deg_E]\n",
    "\n",
    "ax_a = mp.subplot(gs[0,:])\n",
    "ax_b = mp.subplot(gs[1,:])\n",
    "ax_c = mp.subplot(gs[2,0])\n",
    "ax_d = mp.subplot(gs[2,1])\n",
    "#ax_e = mp.subplot(gs[3,0])\n",
    "#ax_f = mp.subplot(gs[3,1])\n",
    "\n",
    "ax_list = [ax_a,ax_b,ax_c,ax_d]#,ax_e,ax_f]\n",
    "\n",
    "\n",
    "levels = numpy.arange(-.2,.21,0.025)\n",
    "contour_plot = ax_a.contourf(xx+360., ptot_400_time_latmean/100., \\\n",
    "                           wa_diffs_time_latmean*86400./1000., \\\n",
    "                           levels=levels, \\\n",
    "                           cmap='RdBu', \\\n",
    "                           extend='both')\n",
    "ax_a.tick_params(labelsize=fontsize)\n",
    "ax_a.set_ylim(1013.25,100)\n",
    "ax_a.set_xlim(279,321)\n",
    "ax_a.text(s='(a) Mean $\\Delta w$ (days 2–10)',\\\n",
    "       x=0, y=1.01, \\\n",
    "       ha='left', va='bottom', \\\n",
    "       fontsize=fontsize, \\\n",
    "       transform=ax_a.transAxes)\n",
    "ax_a.set_xticks(xticks_deg_E)\n",
    "ax_a.set_xticklabels(xticks_format)\n",
    "ax_a.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax_a.set_facecolor('0.1')\n",
    "ax_a.fill_between(x=[wet_lon_lo_degE,wet_lon_hi_degE], y1=ax_a.get_ylim()[0], y2=ax_a.get_ylim()[1],\\\n",
    "                  facecolor='None', edgecolor='0.1',\n",
    "                  hatch='/', alpha=0.5)\n",
    "ax_a.fill_between(x=[dry_lon_lo_degE,dry_lon_hi_degE], y1=ax_a.get_ylim()[0], y2=ax_a.get_ylim()[1],\\\n",
    "                  facecolor='None', edgecolor='0.1',\n",
    "                  hatch='\\\\', alpha=0.5)\n",
    "ax_a.text(s='Andes\\nregion',x=wet_lon_hi_degE+0.5, y=600, ha='left',va='top',fontsize=fontsize,color='0.1')\n",
    "ax_a.text(s='Amazon\\nregion',x=dry_lon_hi_degE+0.5, y=400, ha='left',va='top',fontsize=fontsize,color='0.1')\n",
    "\n",
    "\n",
    "ax_b.plot(360+xlong[0,:],pandas.Series(raintot_diffs_latmean_day1*24).rolling(window=3, center=True).mean().values, c='coral', label='day 1')\n",
    "ax_b.plot(360+xlong[0,:],pandas.Series(raintot_diffs_latmean_day2_10*24).rolling(window=3, center=True).mean().values, c='firebrick', label='days 2–10')\n",
    "ax_b.axhline(0, c='0.1', ls='--')\n",
    "ax_b.set_xlim(279,321)\n",
    "ax_b.set_ylim(-1.6,1.1)\n",
    "ax_b.set_yticks([-1,0,1])\n",
    "ax_b.set_xticks(xticks_deg_E)\n",
    "ax_b.set_xticklabels(xticks_format)\n",
    "ax_b.tick_params(labelsize=fontsize)\n",
    "ax_b.text(s='(b) $\\Delta$ precipitation',\\\n",
    "       x=0, y=1.01, \\\n",
    "       ha='left', va='bottom', \\\n",
    "       fontsize=fontsize, \\\n",
    "       transform=ax_b.transAxes)\n",
    "ax_b.set_ylabel('mm day$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "ax=ax_c\n",
    "ax.plot(wa_diff_wet_region*86400./1000.,ptot_400_wet_region/100., c='firebrick')\n",
    "ax.plot(wa_diff_wet_region_day1*86400./1000.,ptot_400_wet_region/100., c='coral')\n",
    "ax.axvline(0, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,100)\n",
    "ax.set_xlim(-.25,.15)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('km day$^{\\,-1}$', fontsize=fontsize)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='(c) Andes region', x=0., y=1.01, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "#ax.text(s='Andes', x=0.97, y=0.97, ha='right', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.set_yticks(numpy.arange(1000,99,-200))\n",
    "ax.set_xticks([-.1,0,.1])\n",
    "\n",
    "ax=ax_d\n",
    "ax.plot(wa_diff_dry_region_day1*86400/1000.,ptot_400_dry_region/100., c='coral', label='$\\Delta w$ (day 1)')\n",
    "ax.plot(wa_diff_dry_region*86400/1000.,ptot_400_dry_region/100., c='firebrick', label='$\\Delta w$ (days 2–10)')\n",
    "ax.axvline(0, ls='--', c='0.1')\n",
    "#ax.axhline(860, ls='--', c='0.1')\n",
    "ax.set_ylim(1000,100)\n",
    "ax.set_xlim(-.15,.25)\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xlabel('km day$^{\\,-1}$', fontsize=fontsize)\n",
    "#ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.text(s='(d) Amazon region', x=0., y=1.01, ha='left', va='bottom', transform=ax.transAxes, fontsize=fontsize)\n",
    "#ax.text(s='Amazon', x=0.97, y=0.97, ha='right', va='top', transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.set_yticks(numpy.arange(1000,99,-200))\n",
    "ax.set_xticks([-.1,0,.1])\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax_pos = ax_a.get_position()\n",
    "cbar_ax = fig.add_axes([ax_pos.x1+0.025, ax_pos.y0, 0.02, ax_pos.height])\n",
    "cbar = fig.colorbar(contour_plot, cax=cbar_ax)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_label('km day$^{\\,-1}$', fontsize=fontsize)\n",
    "cbar.set_ticks(levels[0::4])\n",
    "\n",
    "handles, labels = ax_b.get_legend_handles_labels()\n",
    "ax_b.legend(handles, labels, fontsize=fontsize, scatterpoints=1, bbox_to_anchor=(1.025,1.), \\\n",
    "            bbox_transform=ax_b.transAxes, loc='upper left', borderaxespad=0, labelspacing=0.25, \\\n",
    "            fancybox=False, edgecolor='0', framealpha=0, borderpad=0.25, \\\n",
    "            handletextpad=0.5, markerscale=1, handlelength=1.25)\n",
    "\n",
    "ax_pos = ax_c.get_position()\n",
    "ax_c_in = fig.add_axes([ax_pos.x0,ax_pos.y0+0.2*ax_pos.height,0.35*ax_pos.width,0.7*ax_pos.height])#, frameon=False)\n",
    "ax_c_in.plot(wa_400_dry*86400./1000.,ptot_400_wet_region/100., c='0.25', label='10d mean')\n",
    "ax_c_in.tick_params(labelsize=fontsize)\n",
    "ax_c_in.tick_params(axis='x', pad=0)\n",
    "ax_c_in.set_ylim(1000,100)\n",
    "ax_c_in.yaxis.tick_right()\n",
    "ax_c_in.axvline(0, lw=0.5, color='0.25', ls='-')\n",
    "ax_c_in.set_xticks([0.,.2])\n",
    "ax_c_in.set_xticklabels(['0','0.2'])\n",
    "#ax_c_in.set_xlabel('mm day$^{\\,-1}$', fontsize=fontsize)\n",
    "#ax_c_in.text(s='$w$ clim.', x=1., y=1.02, ha='right', va='bottom', transform=ax_c_in.transAxes, fontsize=fontsize)\n",
    "\n",
    "ax_pos = ax_d.get_position()\n",
    "ax_d_in = fig.add_axes([ax_pos.x0+ax_pos.width*0.65,ax_pos.y0+0.2*ax_pos.height,0.35*ax_pos.width,0.7*ax_pos.height])#, frameon=False)\n",
    "ax_d_in.plot(wa_400_wet*86400./1000.,ptot_400_wet_region/100., c='0.25', label='$w$ climatology')\n",
    "ax_d_in.tick_params(labelsize=fontsize)\n",
    "ax_d_in.tick_params(axis='x', pad=0)\n",
    "ax_d_in.set_ylim(1000,100)\n",
    "#ax_d_in.set_xlabel('mm day$^{\\,-1}$', fontsize=fontsize)\n",
    "#ax_d_in.text(s='$w$ clim.', x=0, y=1.02, ha='left', va='bottom', transform=ax_d_in.transAxes, fontsize=fontsize, label='$w$ clim.\\n(days 2–10)')\n",
    "ax_d_in.axvline(0, lw=0.5, color='0.25', ls='-')\n",
    "\n",
    "\n",
    "h_out, l_out=ax_d.get_legend_handles_labels()\n",
    "h_in, l_in = ax_d_in.get_legend_handles_labels()\n",
    "ax_d.legend(h_out+h_in, l_out+l_in, fontsize=fontsize, scatterpoints=1, bbox_to_anchor=(1.05,1.), \\\n",
    "            bbox_transform=ax_d.transAxes, loc='upper left', borderaxespad=0, labelspacing=0.25, \\\n",
    "            fancybox=False, edgecolor='0', framealpha=0, borderpad=0.25, \\\n",
    "            handletextpad=0.5, markerscale=1, handlelength=1.25)\n",
    "\n",
    "fig.savefig('manuscript_fig4_winds_precip.png', dpi=600, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot dqdt and moisture convergence in lat means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:37:49.991908Z",
     "start_time": "2018-05-30T01:37:49.967479Z"
    }
   },
   "outputs": [],
   "source": [
    "dqdt_diff_latmean = dqdt_1000_latmean - dqdt_400_latmean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:37:57.549075Z",
     "start_time": "2018-05-30T01:37:57.542174Z"
    }
   },
   "outputs": [],
   "source": [
    "xx_midpoints = 0.5*(xx[1:,1:]+xx[:-1,:-1])\n",
    "\n",
    "ptot_400_time_latmean_midpoints = 0.5*(ptot_400_time_latmean[1:,1:]+\\\n",
    "                                       ptot_400_time_latmean[:-1,:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# dqdt and convergence terms on day 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:38:08.211410Z",
     "start_time": "2018-05-30T01:38:04.797025Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x252 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "\n",
    "xlong_min = -154.76123\n",
    "xlong_max = -25.238678\n",
    "\n",
    "levels = numpy.arange(-3,3.1,0.5)\n",
    "\n",
    "fig = mp.figure(figsize=(6.5,3.5))\n",
    "\n",
    "xticks_deg_E=numpy.array((280,300,320))\n",
    "xticks = -(360-xticks_deg_E)\n",
    "xticks_format=[str(tick)+DEGREE_SIGN+'E' for tick in xticks_deg_E]\n",
    "\n",
    "ax_list = []\n",
    "\n",
    "hour1 = 13\n",
    "hour1_string = hour1-4+1\n",
    "\n",
    "hour2 = 14\n",
    "hour2_string = hour2-4+1\n",
    "\n",
    "ax = fig.add_subplot(221)\n",
    "contour_plot = ax.contourf(xx_midpoints+360., \\\n",
    "                           ptot_400_time_latmean_midpoints/100., \\\n",
    "                           dqdt_diff_latmean[hour1,:,:]*86400.*1000., cmap='RdBu',\\\n",
    "                           levels=levels, extend='both')\n",
    "\n",
    "ax.set_ylim(1013.25,700)\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xlim(xlong_min+67.+360.,xlong_max+360.-7)\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_yticks([700,800,900,1000])\n",
    "ax.text(s='(a) dq/dt for hour '+'{:02d}'.format(hour1_string), x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "#mp.colorbar(contour_plot)\n",
    "ax_list.append(ax)\n",
    "\n",
    "ax = fig.add_subplot(222)\n",
    "contour_plot = ax.contourf(xx_midpoints+360., \\\n",
    "                           ptot_400_time_latmean_midpoints/100., \\\n",
    "                           diff_conv_latmean[hour1,:,:]*86400.*1000., cmap='RdBu', \\\n",
    "                           levels=levels, extend='both')\n",
    "\n",
    "ax.set_ylim(1013.25,700)\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xlim(xlong_min+67.+360.,xlong_max+360.-7)\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_yticks([700,800,900,1000])\n",
    "ax.text(s='(b) moist. converg. for hour '+'{:02d}'.format(hour1_string), x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "#mp.colorbar(contour_plot)\n",
    "ax_list.append(ax)\n",
    "\n",
    "ax = fig.add_subplot(223)\n",
    "contour_plot = ax.contourf(xx_midpoints+360., \\\n",
    "                           ptot_400_time_latmean_midpoints/100., \\\n",
    "                           dqdt_diff_latmean[hour2,:,:]*86400.*1000., cmap='RdBu', \\\n",
    "                           levels=levels, extend='both')\n",
    "\n",
    "ax.set_ylim(1013.25,700)\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xlim(xlong_min+67.+360.,xlong_max+360.-7)\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_yticks([700,800,900,1000])\n",
    "ax.text(s='(c) dq/dt for hour '+'{:02d}'.format(hour2_string), x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "ax_list.append(ax)\n",
    "\n",
    "ax = fig.add_subplot(224)\n",
    "contour_plot = ax.contourf(xx_midpoints+360., \\\n",
    "                           ptot_400_time_latmean_midpoints/100., \\\n",
    "                           diff_conv_latmean[hour2,:,:]*86400.*1000., cmap='RdBu', \\\n",
    "                           levels=levels, extend='both')\n",
    "\n",
    "ax.set_ylim(1013.25,700)\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xlim(xlong_min+67.+360.,xlong_max+360.-7)\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_yticks([700,800,900,1000])\n",
    "ax.text(s='(d) moist. converg. for hour '+'{:02d}'.format(hour2_string), x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "#mp.colorbar(contour_plot)\n",
    "ax_list.append(ax)\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "axpos_bl = ax_list[2].get_position()\n",
    "axpos_br = ax_list[3].get_position()\n",
    "\n",
    "cbar_ax = fig.add_axes([axpos_bl.x0, axpos_bl.y0-0.175, axpos_br.x1-axpos_bl.x0, 0.045])\n",
    "cbar = fig.colorbar(contour_plot, cax=cbar_ax, orientation='horizontal')\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_label('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('dqdt_and_q_convergence_hours_'+\\\n",
    "            '{:02d}'.format(hour1_string)+\\\n",
    "            'and'+'{:02d}'.format(hour2_string)+'.png',\\\n",
    "            dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:38:18.841781Z",
     "start_time": "2018-05-30T01:38:15.707923Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x252 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "\n",
    "xlong_min = -154.76123\n",
    "xlong_max = -25.238678\n",
    "\n",
    "levels = numpy.arange(-3,3.1,0.5)\n",
    "\n",
    "fig = mp.figure(figsize=(6.5,3.5))\n",
    "\n",
    "xticks_deg_E=numpy.array((280,300,320))\n",
    "xticks = -(360-xticks_deg_E)\n",
    "xticks_format=[str(tick)+DEGREE_SIGN+'E' for tick in xticks_deg_E]\n",
    "\n",
    "ax_list = []\n",
    "\n",
    "hour1 = 13\n",
    "hour1_string = hour1-4+1\n",
    "\n",
    "hour2 = 14\n",
    "hour2_string = hour2-4+1\n",
    "\n",
    "ax = fig.add_subplot(221)\n",
    "contour_plot = ax.contourf(xx_midpoints+360., \\\n",
    "                           ptot_400_time_latmean_midpoints/100., \\\n",
    "                           dqdt_diff_latmean[hour1,:,:]*86400.*1000., cmap='RdBu',\\\n",
    "                           levels=levels, extend='both')\n",
    "\n",
    "ax.set_ylim(1013.25,700)\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xlim(xlong_min+67.+360.,xlong_max+360.-7)\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_yticks([700,800,900,1000])\n",
    "ax.text(s='(a) dq/dt for hour '+'{:02d}'.format(hour1_string), x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "#mp.colorbar(contour_plot)\n",
    "ax_list.append(ax)\n",
    "\n",
    "ax = fig.add_subplot(222)\n",
    "contour_plot = ax.contourf(xx_midpoints+360., \\\n",
    "                           ptot_400_time_latmean_midpoints/100., \\\n",
    "                           diff_adv_latmean[hour1,:,:]*86400.*1000., cmap='RdBu', \\\n",
    "                           levels=levels, extend='both')\n",
    "\n",
    "ax.set_ylim(1013.25,700)\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xlim(xlong_min+67.+360.,xlong_max+360.-7)\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_yticks([700,800,900,1000])\n",
    "ax.text(s='(b) moist. converg. for hour '+'{:02d}'.format(hour1_string), x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "#mp.colorbar(contour_plot)\n",
    "ax_list.append(ax)\n",
    "\n",
    "ax = fig.add_subplot(223)\n",
    "contour_plot = ax.contourf(xx_midpoints+360., \\\n",
    "                           ptot_400_time_latmean_midpoints/100., \\\n",
    "                           dqdt_diff_latmean[hour2,:,:]*86400.*1000., cmap='RdBu', \\\n",
    "                           levels=levels, extend='both')\n",
    "\n",
    "ax.set_ylim(1013.25,700)\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xlim(xlong_min+67.+360.,xlong_max+360.-7)\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_yticks([700,800,900,1000])\n",
    "ax.text(s='(c) dq/dt for hour '+'{:02d}'.format(hour2_string), x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "ax_list.append(ax)\n",
    "\n",
    "ax = fig.add_subplot(224)\n",
    "contour_plot = ax.contourf(xx_midpoints+360., \\\n",
    "                           ptot_400_time_latmean_midpoints/100., \\\n",
    "                           diff_adv_latmean[hour2,:,:]*86400.*1000., cmap='RdBu', \\\n",
    "                           levels=levels, extend='both')\n",
    "\n",
    "ax.set_ylim(1013.25,700)\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xlim(xlong_min+67.+360.,xlong_max+360.-7)\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_yticks([700,800,900,1000])\n",
    "ax.text(s='(d) moist. converg. for hour '+'{:02d}'.format(hour2_string), x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "#mp.colorbar(contour_plot)\n",
    "ax_list.append(ax)\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "axpos_bl = ax_list[2].get_position()\n",
    "axpos_br = ax_list[3].get_position()\n",
    "\n",
    "cbar_ax = fig.add_axes([axpos_bl.x0, axpos_bl.y0-0.175, axpos_br.x1-axpos_bl.x0, 0.045])\n",
    "cbar = fig.colorbar(contour_plot, cax=cbar_ax, orientation='horizontal')\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_label('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('dqdt_and_q_advection_hours_'+\\\n",
    "            '{:02d}'.format(hour1_string)+\\\n",
    "            'and'+'{:02d}'.format(hour2_string)+'.png',\\\n",
    "            dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-30T01:38:32.419264Z",
     "start_time": "2018-05-30T01:38:28.070092Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x252 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "\n",
    "xlong_min = -154.76123\n",
    "xlong_max = -25.238678\n",
    "\n",
    "levels = numpy.arange(-3,3.1,0.5)\n",
    "\n",
    "fig = mp.figure(figsize=(8,3.5))\n",
    "\n",
    "xticks_deg_E=numpy.array((280,300,320))\n",
    "xticks = -(360-xticks_deg_E)\n",
    "xticks_format=[str(tick)+DEGREE_SIGN+'E' for tick in xticks_deg_E]\n",
    "\n",
    "ax_list = []\n",
    "\n",
    "hour1 = 13\n",
    "hour1_string = hour1-4+1\n",
    "\n",
    "hour2 = 14\n",
    "hour2_string = hour2-4+1\n",
    "\n",
    "ax = fig.add_subplot(231)\n",
    "contour_plot = ax.contourf(xx_midpoints+360., \\\n",
    "                           ptot_400_time_latmean_midpoints/100., \\\n",
    "                           dqdt_diff_latmean[hour1,:,:]*86400.*1000., cmap='RdBu',\\\n",
    "                           levels=levels, extend='both')\n",
    "\n",
    "ax.set_ylim(1013.25,700)\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xlim(xlong_min+67.+360.,xlong_max+360.-7)\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_yticks([700,800,900,1000])\n",
    "ax.text(s='(a) dq/dt', x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='hour '+'{:02d}'.format(hour1_string), x=0.95, y=0.91, \\\n",
    "        ha='right', va='top', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "#mp.colorbar(contour_plot)\n",
    "ax_list.append(ax)\n",
    "\n",
    "ax = fig.add_subplot(232)\n",
    "contour_plot = ax.contourf(xx_midpoints+360., \\\n",
    "                           ptot_400_time_latmean_midpoints/100., \\\n",
    "                           diff_conv_latmean[hour1,:,:]*86400.*1000., cmap='RdBu', \\\n",
    "                           levels=levels, extend='both')\n",
    "\n",
    "ax.set_ylim(1013.25,700)\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xlim(xlong_min+67.+360.,xlong_max+360.-7)\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_yticks([700,800,900,1000])\n",
    "ax.text(s='(b) moisture convergence', x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='hour '+'{:02d}'.format(hour1_string), x=0.95, y=0.91, \\\n",
    "        ha='right', va='top', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "#mp.colorbar(contour_plot)\n",
    "ax_list.append(ax)\n",
    "\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(233)\n",
    "contour_plot = ax.contourf(xx_midpoints+360., \\\n",
    "                           ptot_400_time_latmean_midpoints/100., \\\n",
    "                           diff_adv_latmean[hour1,:,:]*86400.*1000., cmap='RdBu', \\\n",
    "                           levels=levels, extend='both')\n",
    "\n",
    "ax.set_ylim(1013.25,700)\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xlim(xlong_min+67.+360.,xlong_max+360.-7)\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_yticks([700,800,900,1000])\n",
    "ax.text(s='(c) moisture advection', x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='hour '+'{:02d}'.format(hour1_string), x=0.95, y=0.91, \\\n",
    "        ha='right', va='top', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "#mp.colorbar(contour_plot)\n",
    "ax_list.append(ax)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(234)\n",
    "contour_plot = ax.contourf(xx_midpoints+360., \\\n",
    "                           ptot_400_time_latmean_midpoints/100., \\\n",
    "                           dqdt_diff_latmean[hour2,:,:]*86400.*1000., cmap='RdBu', \\\n",
    "                           levels=levels, extend='both')\n",
    "\n",
    "ax.set_ylim(1013.25,700)\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xlim(xlong_min+67.+360.,xlong_max+360.-7)\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_yticks([700,800,900,1000])\n",
    "ax.text(s='(d) dq/dt', x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='hour '+'{:02d}'.format(hour2_string), x=0.95, y=0.91, \\\n",
    "        ha='right', va='top', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "ax_list.append(ax)\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(235)\n",
    "contour_plot = ax.contourf(xx_midpoints+360., \\\n",
    "                           ptot_400_time_latmean_midpoints/100., \\\n",
    "                           diff_conv_latmean[hour2,:,:]*86400.*1000., cmap='RdBu', \\\n",
    "                           levels=levels, extend='both')\n",
    "\n",
    "ax.set_ylim(1013.25,700)\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xlim(xlong_min+67.+360.,xlong_max+360.-7)\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_yticks([700,800,900,1000])\n",
    "ax.text(s='(e) moisture convergence', x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='hour '+'{:02d}'.format(hour2_string), x=0.95, y=0.91, \\\n",
    "        ha='right', va='top', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "#mp.colorbar(contour_plot)\n",
    "ax_list.append(ax)\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(236)\n",
    "contour_plot = ax.contourf(xx_midpoints+360., \\\n",
    "                           ptot_400_time_latmean_midpoints/100., \\\n",
    "                           diff_adv_latmean[hour2,:,:]*86400.*1000., cmap='RdBu', \\\n",
    "                           levels=levels, extend='both')\n",
    "\n",
    "ax.set_ylim(1013.25,700)\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xlim(xlong_min+67.+360.,xlong_max+360.-7)\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_yticks([700,800,900,1000])\n",
    "ax.text(s='(f) moisture advection', x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "ax.text(s='hour '+'{:02d}'.format(hour2_string), x=0.95, y=0.91, \\\n",
    "        ha='right', va='top', \\\n",
    "        transform=ax.transAxes, fontsize=fontsize)\n",
    "#mp.colorbar(contour_plot)\n",
    "ax_list.append(ax)\n",
    "\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "axpos_bl = ax_list[3].get_position()\n",
    "axpos_br = ax_list[5].get_position()\n",
    "\n",
    "cbar_ax = fig.add_axes([axpos_bl.x0, axpos_bl.y0-0.175, axpos_br.x1-axpos_bl.x0, 0.045])\n",
    "cbar = fig.colorbar(contour_plot, cax=cbar_ax, orientation='horizontal')\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_label('g kg$^{\\,-1}$ day$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('dqdt_and_q_convergence_and_advection_hours_'+\\\n",
    "            '{:02d}'.format(hour1_string)+\\\n",
    "            'and'+'{:02d}'.format(hour2_string)+'.png',\\\n",
    "            dpi=600, bbox_inches='tight')"
   ]
  }
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