multi_vertcross_LWC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba664ec5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jeeva\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:63: UserWarning: The following kwargs were not used by contour: 'projection'\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from metpy.cbook import get_test_data\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.colors as colors\n",
    "from matplotlib.cm import get_cmap\n",
    "from matplotlib.colors import from_levels_and_colors\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "from netCDF4 import Dataset\n",
    "\n",
    "from wrf import (getvar, to_np, get_cartopy, latlon_coords, vertcross,\n",
    "                        cartopy_xlim, cartopy_ylim, interpline, CoordPair)\n",
    "\n",
    "ysu1 = Dataset(\"wrf_ysu_24112020_06.nc\")\n",
    "myj1 = Dataset(\"wrf_myj_24112020_06.nc\")\n",
    "qnse1 = Dataset(\"wrf_QNSE_24112020_06.nc\")\n",
    "mynn1 = Dataset(\"wrf_mynn3_24112020_06.nc\")\n",
    "\n",
    "# Define the cross section lat long\n",
    "cros_start = CoordPair(lat=47.68, lon=16.69)\n",
    "cros_end = CoordPair(lat=47.41, lon=21.98)\n",
    "\n",
    "start_point = CoordPair(lat=47.68, lon=16.69)\n",
    "end_point = CoordPair(lat=47.41, lon=21.98)\n",
    "\n",
    "fig, axarr = plt.subplots(nrows=2, ncols=2, figsize=(15, 8), constrained_layout=False)\n",
    "axlist = axarr.flatten()\n",
    "\n",
    "#===================================================================================================#\n",
    "#               YSU Vertical cross section\n",
    "#===================================================================================================\n",
    "#Get the WRF variables\n",
    "ysu_ht = getvar(ysu1,\"z\")#,timeidx=52)\n",
    "ysu_ter = getvar(ysu1,\"ter\")#,timeidx=52)\n",
    "# cloud mixing ratio\n",
    "ysu_lwc = getvar(ysu1,\"QCLOUD\")#,timeidx=52)\n",
    "\n",
    "ysu_z_cros = vertcross(ysu_lwc,ysu_ht,wrfin=ysu1,\n",
    "                  start_point=cros_start,\n",
    "                  end_point=cros_end,\n",
    "                  latlon=True,\n",
    "                  meta=True)\n",
    "dbz_cros_filled = np.ma.copy(to_np(ysu_z_cros))\n",
    "\n",
    "for i in range(dbz_cros_filled.shape[-1]):\n",
    "    column_vals = dbz_cros_filled[:,i]\n",
    "    first_idx = int(np.transpose((column_vals > -10).nonzero())[0])\n",
    "    dbz_cros_filled[0:first_idx,i] = dbz_cros_filled[first_idx,i]\n",
    "\n",
    "ter_line = interpline(ysu_ter,wrfin=ysu1, start_point=cros_start,\n",
    "                     end_point=cros_end)\n",
    "# get the lat lon points\n",
    "lats, lons = latlon_coords(ysu_lwc)\n",
    "# get the projection \n",
    "cart_proj = get_cartopy(ysu_lwc)\n",
    "# creating figure\n",
    "\n",
    "xs = np.arange(0,ysu_z_cros.shape[-1],1)\n",
    "ys = to_np(ysu_z_cros.coords[\"vertical\"])\n",
    "mp1 = axlist[0].contourf(xs,ys,to_np(dbz_cros_filled),\n",
    "                                cmap = \"jet\", projection=False)\n",
    "\n",
    "# cb_lwc = fig.colorbar(lwc_countours,ax=ax_cros)\n",
    "# cb_lwc.ax.tick_params(labelsize=8)\n",
    "\n",
    "#filling the mountain area\n",
    "ht_fill1 = axlist[0].fill_between(xs,0,to_np(ter_line),\n",
    "                              facecolor=\"whitesmoke\")\n",
    "\n",
    "# #set the s-ticks to use latititude and longitude\n",
    "# coord_pairs = to_np(ysu_z_cros.coords[\"xy_loc\"])\n",
    "# x_ticks = np.arange(coord_pairs.shape[0])\n",
    "# x_labels = [pair.latlon_str(fmt=\"{:.2f}, {:.2f}\") for pair in to_np(coord_pairs)]\n",
    "# #set the desired number of xticks below\n",
    "# num_ticks = 6\n",
    "# thin = int((len(x_ticks)/num_ticks)+0.5)\n",
    "# # print(x_labels[::thin])\n",
    "# axlist[0].set_xticks(x_ticks[::thin])\n",
    "# axlist[0].set_xticklabels(x_labels[::thin], rotation=20, fontsize=8)\n",
    "\n",
    "axlist[0].tick_params(left = True, right = False , labelleft = True ,\n",
    "                labelbottom = False, bottom = False)\n",
    "# set the range of y-axis\n",
    "axlist[0].set_ylim([0,1000])\n",
    "\n",
    "# add title \n",
    "axlist[0].set_title(\"YSU\",{\"fontsize\" :12})\n",
    "\n",
    "#===================================================================================================#\n",
    "#               myj Vertical cross section\n",
    "#===================================================================================================\n",
    "#Get the WRF variables\n",
    "myj_ht = getvar(myj1,\"z\")#,timeidx=52)\n",
    "myj_ter = getvar(myj1,\"ter\")#,timeidx=52)\n",
    "# cloud mixing ratio\n",
    "myj_lwc = getvar(myj1,\"QCLOUD\")#,timeidx=52)\n",
    "\n",
    "myj_z_cros = vertcross(myj_lwc,myj_ht,wrfin=myj1,\n",
    "                  start_point=cros_start,\n",
    "                  end_point=cros_end,\n",
    "                  latlon=True,\n",
    "                  meta=True)\n",
    "dbz_cros_filled = np.ma.copy(to_np(myj_z_cros))\n",
    "\n",
    "for i in range(dbz_cros_filled.shape[-1]):\n",
    "    column_vals = dbz_cros_filled[:,i]\n",
    "    first_idx = int(np.transpose((column_vals > -10).nonzero())[0])\n",
    "    dbz_cros_filled[0:first_idx,i] = dbz_cros_filled[first_idx,i]\n",
    "\n",
    "ter_line = interpline(myj_ter,wrfin=myj1, start_point=cros_start,\n",
    "                     end_point=cros_end)\n",
    "# get the lat lon points\n",
    "lats, lons = latlon_coords(myj_lwc)\n",
    "# get the projection \n",
    "cart_proj = get_cartopy(myj_lwc)\n",
    "# creating figure\n",
    "\n",
    "xs = np.arange(0,myj_z_cros.shape[-1],1)\n",
    "ys = to_np(myj_z_cros.coords[\"vertical\"])\n",
    "mp2 = axlist[1].contourf(xs,ys,to_np(dbz_cros_filled),\n",
    "                                cmap = \"jet\")\n",
    "\n",
    "# cb_lwc = fig.colorbar(lwc_countours,ax=ax_cros)\n",
    "# cb_lwc.ax.tick_params(labelsize=8)\n",
    "\n",
    "#filling the mountain area\n",
    "ht_fill = axlist[1].fill_between(xs,0,to_np(ter_line),\n",
    "                              facecolor=\"whitesmoke\")\n",
    "\n",
    "# #set the s-ticks to use latititude and longitude\n",
    "# coord_pairs = to_np(myj_z_cros.coords[\"xy_loc\"])\n",
    "# x_ticks = np.arange(coord_pairs.shape[0])\n",
    "# x_labels = [pair.latlon_str(fmt=\"{:.2f}, {:.2f}\") for pair in to_np(coord_pairs)]\n",
    "# #set the desired number of xticks below\n",
    "# num_ticks = 6\n",
    "# thin = int((len(x_ticks)/num_ticks)+0.5)\n",
    "# # print(x_labels[::thin])\n",
    "# axlist[1].set_xticks(x_ticks[::thin])\n",
    "# axlist[1].set_xticklabels(x_labels[::thin], rotation=20, fontsize=8)\n",
    "\n",
    "axlist[1].tick_params(left = True, right = False , labelleft = True ,\n",
    "                labelbottom = False, bottom = False)\n",
    "\n",
    "# set the range of y-axis\n",
    "axlist[1].set_ylim([0,1000])\n",
    "\n",
    "# add title \n",
    "axlist[1].set_title(\"MYJ\",{\"fontsize\" :12})\n",
    "\n",
    "#===================================================================================================#\n",
    "#               QNSE Vertical cross section\n",
    "#===================================================================================================\n",
    "#Get the WRF variables\n",
    "QNSE_ht = getvar(qnse1,\"z\")#,timeidx=52)\n",
    "QNSE_ter = getvar(qnse1,\"ter\")#,timeidx=52)\n",
    "# cloud mixing ratio\n",
    "QNSE_lwc = getvar(qnse1,\"QCLOUD\")#,timeidx=52)\n",
    "\n",
    "QNSE_z_cros = vertcross(QNSE_lwc,QNSE_ht,wrfin=qnse1,\n",
    "                  start_point=cros_start,\n",
    "                  end_point=cros_end,\n",
    "                  latlon=True,\n",
    "                  meta=True)\n",
    "dbz_cros_filled = np.ma.copy(to_np(QNSE_z_cros))\n",
    "\n",
    "for i in range(dbz_cros_filled.shape[-1]):\n",
    "    column_vals = dbz_cros_filled[:,i]\n",
    "    first_idx = int(np.transpose((column_vals > -10).nonzero())[0])\n",
    "    dbz_cros_filled[0:first_idx,i] = dbz_cros_filled[first_idx,i]\n",
    "\n",
    "ter_line = interpline(QNSE_ter,wrfin=qnse1, start_point=cros_start,\n",
    "                     end_point=cros_end)\n",
    "# get the lat lon points\n",
    "lats, lons = latlon_coords(QNSE_lwc)\n",
    "# get the projection \n",
    "cart_proj = get_cartopy(QNSE_lwc)\n",
    "# creating figure\n",
    "\n",
    "xs = np.arange(0,QNSE_z_cros.shape[-1],1)\n",
    "ys = to_np(QNSE_z_cros.coords[\"vertical\"])\n",
    "mp3 = axlist[2].contourf(xs,ys,to_np(dbz_cros_filled),\n",
    "                                cmap = \"jet\")\n",
    "\n",
    "# cb_lwc = fig.colorbar(lwc_countours,ax=ax_cros)\n",
    "# cb_lwc.ax.tick_params(labelsize=8)\n",
    "\n",
    "#filling the mountain area\n",
    "ht_fill = axlist[2].fill_between(xs,0,to_np(ter_line),\n",
    "                              facecolor=\"whitesmoke\")\n",
    "\n",
    "#set the s-ticks to use latititude and longitude\n",
    "coord_pairs = to_np(QNSE_z_cros.coords[\"xy_loc\"])\n",
    "x_ticks = np.arange(coord_pairs.shape[0])\n",
    "x_labels = [pair.latlon_str(fmt=\"{:.2f}, {:.2f}\") for pair in to_np(coord_pairs)]\n",
    "#set the desired number of xticks below\n",
    "num_ticks = 6\n",
    "thin = int((len(x_ticks)/num_ticks)+0.5)\n",
    "# print(x_labels[::thin])\n",
    "axlist[2].set_xticks(x_ticks[::thin])\n",
    "axlist[2].set_xticklabels(x_labels[::thin], rotation=20, fontsize=8)\n",
    "# axlist[2].tick_params(left = True, right = False , labelleft = True ,\n",
    "#                 labelbottom = False, bottom = False)\n",
    "# # set the range of y-axis\n",
    "axlist[2].set_ylim([0,1000])\n",
    "\n",
    "# add title \n",
    "axlist[2].set_title(\"QNSE\",{\"fontsize\" :12})\n",
    "\n",
    "#===================================================================================================#\n",
    "#               mynn Vertical cross section\n",
    "#===================================================================================================\n",
    "#Get the WRF variables\n",
    "mynn_ht = getvar(mynn1,\"z\")#,timeidx=52)\n",
    "mynn_ter = getvar(mynn1,\"ter\")#,timeidx=52)\n",
    "# cloud mixing ratio\n",
    "mynn_lwc = getvar(mynn1,\"QCLOUD\")#,timeidx=52)\n",
    "\n",
    "mynn_z_cros = vertcross(mynn_lwc,mynn_ht,wrfin=mynn1,\n",
    "                  start_point=cros_start,\n",
    "                  end_point=cros_end,\n",
    "                  latlon=True,\n",
    "                  meta=True)\n",
    "dbz_cros_filled = np.ma.copy(to_np(mynn_z_cros))\n",
    "\n",
    "for i in range(dbz_cros_filled.shape[-1]):\n",
    "    column_vals = dbz_cros_filled[:,i]\n",
    "    first_idx = int(np.transpose((column_vals > -10).nonzero())[0])\n",
    "    dbz_cros_filled[0:first_idx,i] = dbz_cros_filled[first_idx,i]\n",
    "\n",
    "ter_line = interpline(mynn_ter,wrfin=mynn1,\n",
    "                      start_point=cros_start,\n",
    "                      end_point=cros_end)\n",
    "# get the lat lon points\n",
    "lats, lons = latlon_coords(mynn_lwc)\n",
    "# get the projection \n",
    "cart_proj = get_cartopy(mynn_lwc)\n",
    "# creating figure\n",
    "\n",
    "xs = np.arange(0,mynn_z_cros.shape[-1],1)\n",
    "ys = to_np(mynn_z_cros.coords[\"vertical\"])\n",
    "mp4 = axlist[3].contourf(xs,ys,to_np(dbz_cros_filled), cmap = \"jet\")\n",
    "\n",
    "# cb_lwc = fig.colorbar(lwc_countours,ax=ax_cros)\n",
    "# cb_lwc.ax.tick_params(labelsize=8)\n",
    "\n",
    "#filling the mountain area\n",
    "ht_fill = axlist[3].fill_between(xs,0,to_np(ter_line),facecolor=\"whitesmoke\")\n",
    "\n",
    "#set the x-ticks to use latititude and longitude\n",
    "coord_pairs = to_np(mynn_z_cros.coords[\"xy_loc\"])\n",
    "x_ticks = np.arange(coord_pairs.shape[0])\n",
    "x_labels = [pair.latlon_str(fmt=\"{:.2f}, {:.2f}\") for pair in to_np(coord_pairs)]\n",
    "#set the desired number of xticks below\n",
    "num_ticks = 6\n",
    "thin = int((len(x_ticks)/num_ticks)+0.5)\n",
    "# print(x_labels[::thin])\n",
    "axlist[3].set_xticks(x_ticks[::thin])\n",
    "axlist[3].set_xticklabels(x_labels[::thin], rotation=20, fontsize=8)\n",
    "\n",
    "# set the range of y-axis\n",
    "axlist[3].set_ylim([0,1000])\n",
    "\n",
    "# add title \n",
    "axlist[3].set_title(\"MYNN 3\",{\"fontsize\" :12})\n",
    "\n",
    "# cb_lwc = fig.colorbar(lwc_countours,ax=ax_cros)\n",
    "# cb_lwc.ax.tick_params(labelsize=8)\n",
    "\n",
    "axbar = [axlist[0],axlist[1],axlist[2],axlist[3]]#,axlist[4],axlist[5],axlist[6],axlist[7]]\n",
    "cbar = fig.colorbar(mp1, ax=axbar,shrink=0.8)\n",
    "cbar.minorticks_on()\n",
    "cbar.set_label('QCLOUD(kg/kg)',fontsize=12)\n",
    "plt.savefig(\"multi_QCLOUD_Vertcross.jpg\",dpi=300)\n",
    "fig.suptitle('QCLOUD Vertical cross section',x=0.45, y=.95,fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b1ac02a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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