plot_palm_mean_profile.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config IPCompleter.greedy=True\n",
    "from lesanalysis import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set test and reference cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "palmcase = '/Users/qingli/data/palm/MSM97-LT'\n",
    "palmcase_ref = '/Users/qingli/data/palm/MSM97-ST'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputfile_pfl     = palmcase+'/DATA_1D_PR_NETCDF'\n",
    "inputfile_pfl_ref = palmcase_ref+'/DATA_1D_PR_NETCDF'\n",
    "data_pfl          = PALMData1DPR(inputfile_pfl)\n",
    "data_pfl_ref      = PALMData1DPR(inputfile_pfl_ref)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# flags\n",
    "f_ref = True\n",
    "f_norm = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gravitational acceleration (m/s^2)\n",
    "g = 9.81\n",
    "# Latitude\n",
    "lat = 45.0\n",
    "# Coriolis parameter (1/s)\n",
    "f = 4*np.pi/86400*np.sin(lat/180*np.pi)\n",
    "# friction velocity (m/s)\n",
    "ustar = 6.1e-3\n",
    "# surface temperature flux (K m/s)\n",
    "Q0 = 1.19e-6\n",
    "# reference density\n",
    "rho_0 = 1027.0\n",
    "# depth of the domain (m)\n",
    "depth = -120.0\n",
    "# spin-up time (s)\n",
    "tspinup = 21600.\n",
    "# averaging period (s)\n",
    "# deltat = 2*np.pi/f # last inertial period\n",
    "deltat = 3600. # last hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# range of z\n",
    "if f_norm:\n",
    "    ylabel_str = r'$z/h_b$'\n",
    "    ymin = -1.6\n",
    "    ymax = 0\n",
    "else:\n",
    "    ylabel_str = 'Depth (m)'\n",
    "    ymin = depth\n",
    "    ymax = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time period for average: 82805.80000000008 s - 86402.0 s\n",
      "Time period for average (Ref): 82810.90000000004 s - 86409.99999999997 s\n"
     ]
    }
   ],
   "source": [
    "# mean profile\n",
    "time = data_pfl.dataset.variables['time'][:]\n",
    "ttarget = time[-1]-deltat\n",
    "assert ttarget>tspinup, 'Run time too short for average over the last inertial period.'\n",
    "tidx_start = np.argmin(np.abs(time-ttarget))\n",
    "tidx_end = -1\n",
    "print('Time period for average: {} s - {} s'.format(time[tidx_start], time[tidx_end]))\n",
    "\n",
    "if f_ref:\n",
    "    time_ref = data_pfl_ref.dataset.variables['time'][:]\n",
    "    ttarget_ref = time_ref[-1]-deltat\n",
    "    assert ttarget_ref>tspinup, 'Reference run time too short for average over the last inertial period.'\n",
    "    tidx_start_ref = np.argmin(np.abs(time_ref-ttarget_ref))\n",
    "    tidx_end_ref = -1\n",
    "    print('Time period for average (Ref): {} s - {} s'.format(time_ref[tidx_start_ref], time_ref[tidx_end_ref]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial temperature and salinity profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "fig, axarr = plt.subplots(1, 2, sharey='row')\n",
    "data_pfl.read_profile('pt', tidx_start=0, tidx_end=1).plot_mean(\n",
    "                           axis=axarr[0], color='k', xlabel=r'$\\theta_0$ (K)', ylabel='Depth (m)')\n",
    "data_pfl.read_profile('sa', tidx_start=0, tidx_end=1).plot_mean(\n",
    "                           axis=axarr[1], color='k', xlabel=r'$S$ (psu)', ylabel='off')\n",
    "\n",
    "if f_ref:\n",
    "    data_pfl_ref.read_profile('pt', tidx_start=0, tidx_end=1).plot_mean(\n",
    "                               axis=axarr[0], color='k', \n",
    "                               xlabel='off', ylabel='off', linestyle='--')\n",
    "    data_pfl_ref.read_profile('sa', tidx_start=0, tidx_end=1).plot_mean(\n",
    "                               axis=axarr[1], color='k',\n",
    "                               xlabel='off', ylabel='off', linestyle='--')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temperature and salinity profiles at the end of the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "fig, axarr = plt.subplots(1, 2, sharey='row')\n",
    "data_pfl.read_profile('pt', tidx_start=-1).plot_mean(\n",
    "                           axis=axarr[0], color='k', xlabel=r'$\\theta_0$ (K)', ylabel='Depth (m)')\n",
    "data_pfl.read_profile('sa', tidx_start=-1).plot_mean(\n",
    "                           axis=axarr[1], color='k', xlabel=r'$S$ (psu)', ylabel='off')\n",
    "\n",
    "if f_ref:\n",
    "    data_pfl_ref.read_profile('pt', tidx_start=-1).plot_mean(\n",
    "                               axis=axarr[0], color='k',\n",
    "                               xlabel='off', ylabel='off', linestyle='--')\n",
    "    data_pfl_ref.read_profile('sa', tidx_start=-1).plot_mean(\n",
    "                               axis=axarr[1], color='k',\n",
    "                               xlabel='off', ylabel='off', linestyle='--')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean boundary layer depth defined by the depth where N^2 reaches its maximum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h_b =  43.00 m\n",
      "h_b_ref =  40.50 m\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normalizaing factor\n",
    "if f_norm:\n",
    "    norm = 1/f**2\n",
    "    xlabel_str = r'$N^2/f^2$'\n",
    "else:\n",
    "    norm = 1\n",
    "    xlabel_str = r'$N^2$ (s$^{-2}$)'\n",
    "\n",
    "# plot\n",
    "prho = data_pfl.read_profile('prho', tidx_start=tidx_start, tidx_end=tidx_end)\n",
    "NN_data = -g*(prho.data[:,1:]-prho.data[:,0:-1])/(prho.z[1:]-prho.z[0:-1])/rho_0\n",
    "NN_z = 0.5*(prho.z[1:]+prho.z[0:-1])\n",
    "NN = LESProfile(data=NN_data, data_name=r'$N^2$', data_units=r's$^{-2}$', z=NN_z, time=time[tidx_start:tidx_end])\n",
    "zidx = np.argmax(NN.data, axis=1)\n",
    "hb = np.abs(NN.z[zidx].mean())\n",
    "print('h_b = {:6.2f} m'.format(hb))\n",
    "if f_norm:\n",
    "    znorm = 1/hb\n",
    "else:\n",
    "    znorm = 1\n",
    "NN.plot_mean(norm=norm, znorm=znorm, color='k', xlabel=xlabel_str, ylabel=ylabel_str, ylim=[ymin, ymax])\n",
    "\n",
    "if f_ref:\n",
    "    prho = data_pfl_ref.read_profile('prho', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref)\n",
    "    NN_data = -g*(prho.data[:,1:]-prho.data[:,0:-1])/(prho.z[1:]-prho.z[0:-1])/rho_0\n",
    "    NN_z = 0.5*(prho.z[1:]+prho.z[0:-1])\n",
    "    NN_ref = LESProfile(data=NN_data, data_name=r'$N^2$', data_units=r's$^{-2}$', z=NN_z, time=time[tidx_start_ref:tidx_end_ref])\n",
    "    zidx = np.argmax(NN_ref.data, axis=1)\n",
    "    hb_ref = np.abs(NN_ref.z[zidx].mean())\n",
    "    print('h_b_ref = {:6.2f} m'.format(hb_ref))\n",
    "    if f_norm:\n",
    "        znorm_ref = 1/hb_ref\n",
    "    else:\n",
    "        znorm_ref = 1\n",
    "    NN_ref.plot_mean(norm=norm, znorm=znorm_ref, color='k', xlabel='off', ylabel='off', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Temperature variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normalizing factor\n",
    "if f_norm:\n",
    "    norm = 1/(Q0/ustar)**2\n",
    "    xlabel_str = r'$\\overline{{\\theta^\\prime}^2}/\\Theta_0^2$'\n",
    "else:\n",
    "    norm = 1\n",
    "    xlabel_str = r'$\\overline{{\\theta^\\prime}^2}$ (K$^2$)'\n",
    "\n",
    "# plot\n",
    "data_pfl.read_profile('pt*2', tidx_start=tidx_start, tidx_end=tidx_end).plot_mean(\n",
    "                              norm=norm, znorm=znorm, color='k',\n",
    "                              xlabel=xlabel_str,\n",
    "                              ylabel=ylabel_str, ylim=[ymin, ymax])\n",
    "\n",
    "if f_ref:\n",
    "    data_pfl_ref.read_profile('pt*2', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref).plot_mean(\n",
    "                                      norm=norm, znorm=znorm_ref, color='k',\n",
    "                                      xlabel='off', ylabel='off', linestyle='--')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temperature fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normalizing factor\n",
    "if f_norm:\n",
    "    norm = 1/Q0\n",
    "    xlabel_str = r'$\\overline{w^\\prime \\theta^\\prime}/Q_0$'\n",
    "else:\n",
    "    norm = 1\n",
    "    xlabel_str = r'$\\overline{w^\\prime \\theta^\\prime}$ (K m s$^{-1}$)'\n",
    "\n",
    "# plot\n",
    "data_pfl.read_profile('w*pt*', tidx_start=tidx_start, tidx_end=tidx_end).plot_mean(\n",
    "                               norm=norm, znorm=znorm, color='k',\n",
    "                               xlabel=xlabel_str,\n",
    "                               ylabel=ylabel_str, ylim=[ymin, ymax], label='Resolved')\n",
    "data_pfl.read_profile('w\"pt\"', tidx_start=tidx_start, tidx_end=tidx_end).plot_mean(\n",
    "                               norm=norm, znorm=znorm, color='r',\n",
    "                               xlabel='off', ylabel='off', label='SGS')\n",
    "plt.legend(loc=3)\n",
    "\n",
    "if f_ref:\n",
    "    data_pfl_ref.read_profile('w*pt*', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref).plot_mean(\n",
    "                                       norm=norm, znorm=znorm_ref, color='k',\n",
    "                                       xlabel='off', ylabel='off', linestyle='--')\n",
    "    data_pfl_ref.read_profile('w\"pt\"', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref).plot_mean(\n",
    "                                       norm=norm, znorm=znorm_ref, color='r',\n",
    "                                       xlabel='off', ylabel='off', linestyle='--')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ref: Stokes drift not found. Skip.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normalizaing factor\n",
    "if f_norm:\n",
    "    norm = 1/ustar\n",
    "    xlabel_str = r'$\\overline{u}/u_*$'\n",
    "else:\n",
    "    norm = 1\n",
    "    xlabel_str = r'$\\overline{u}$ (m s$^{-1}$)'\n",
    "\n",
    "data_pfl.read_profile('u', tidx_start=tidx_start, tidx_end=tidx_end).plot_mean(\n",
    "                             norm=norm, znorm=znorm, color='b',\n",
    "                             xlabel=xlabel_str,\n",
    "                             ylabel=ylabel_str, ylim=[ymin, ymax], label='$\\overline{u}$')\n",
    "data_pfl.read_profile('v', tidx_start=tidx_start, tidx_end=tidx_end).plot_mean(\n",
    "                             norm=norm, znorm=znorm, color='r',\n",
    "                             xlabel='off', ylabel='off', label='$\\overline{v}$')\n",
    "try:\n",
    "    data_pfl.read_profile('u_stk', tidx_start=tidx_start, tidx_end=tidx_end).plot_mean(\n",
    "                                   norm=norm, znorm=znorm, color='c',\n",
    "                                   xlabel='off', ylabel='off', label='$u^S$')\n",
    "    data_pfl.read_profile('v_stk', tidx_start=tidx_start, tidx_end=tidx_end).plot_mean(\n",
    "                                   norm=norm, znorm=znorm, color='m',\n",
    "                                   xlabel='off', ylabel='off', label='$v^S$')\n",
    "except ValueError:\n",
    "    print('Stokes drift not found. Skip.')\n",
    "    \n",
    "plt.legend(loc=4)\n",
    "\n",
    "if f_ref:\n",
    "    data_pfl_ref.read_profile('u', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref).plot_mean(\n",
    "                                 norm=norm, znorm=znorm_ref, color='b',\n",
    "                                 xlabel='off', ylabel='off', linestyle='--')\n",
    "    data_pfl_ref.read_profile('v', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref).plot_mean(\n",
    "                                 norm=norm, znorm=znorm_ref, color='r',\n",
    "                                 xlabel='off', ylabel='off', linestyle='--')\n",
    "    try:\n",
    "        data_pfl_ref.read_profile('u_stk', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref).plot_mean(\n",
    "                                     norm=norm, znorm=znorm_ref, color='c',\n",
    "                                     xlabel='off', ylabel='off', linestyle='--')\n",
    "        data_pfl_ref.read_profile('v_stk', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref).plot_mean(\n",
    "                                     norm=norm, znorm=znorm_ref, color='m',\n",
    "                                     xlabel='off', ylabel='off', linestyle='--')\n",
    "    except ValueError:\n",
    "        print('Ref: Stokes drift not found. Skip.')\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Momentum fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normalizaing factor\n",
    "if f_norm:\n",
    "    norm = 1/ustar**2\n",
    "    xlabel_str1 = r'$\\overline{u^\\prime w^\\prime}/u_*^2$'\n",
    "    xlabel_str2 = r'$\\overline{v^\\prime w^\\prime}/u_*^2$'\n",
    "else:\n",
    "    norm = 1\n",
    "    xlabel_str1 = r'$\\overline{u^\\prime w^\\prime}$ (m$^2$ s$^{-2}$)'\n",
    "    xlabel_str2 = r'$\\overline{v^\\prime w^\\prime}$ (m$^2$ s$^{-2}$)'\n",
    "\n",
    "# subplots\n",
    "fig, axarr = plt.subplots(1, 2, sharey='row')\n",
    "data_pfl.read_profile('wu', tidx_start=tidx_start, tidx_end=tidx_end).plot_mean(\n",
    "                            axis=axarr[0], norm=norm, znorm=znorm, color='k',\n",
    "                            xlabel=xlabel_str1, ylabel=ylabel_str, ylim=[ymin, ymax])\n",
    "data_pfl.read_profile('wv', tidx_start=tidx_start, tidx_end=tidx_end).plot_mean(\n",
    "                            axis=axarr[1], norm=norm, znorm=znorm, color='k',\n",
    "                            xlabel=xlabel_str2, ylabel='off')\n",
    "\n",
    "if f_ref:\n",
    "    data_pfl_ref.read_profile('wu', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref).plot_mean(\n",
    "                                    axis=axarr[0], norm=norm, znorm=znorm_ref,\n",
    "                                    xlabel='off', ylabel='off', color='k', linestyle='--')\n",
    "    data_pfl_ref.read_profile('wv', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref).plot_mean(\n",
    "                                    axis=axarr[1], norm=norm, znorm=znorm_ref, \n",
    "                                    xlabel='off', ylabel='off', color='k', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Velocity variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normalizing factor\n",
    "if f_norm:\n",
    "    norm = 1/ustar**2\n",
    "    xlabel_str1 = r'$\\overline{{u^\\prime}^2}/u_*^2$'\n",
    "    xlabel_str2 = r'$\\overline{{v^\\prime}^2}/u_*^2$'\n",
    "    xlabel_str3 = r'$\\overline{{w^\\prime}^2}/u_*^2$'\n",
    "else:\n",
    "    norm = 1\n",
    "    xlabel_str1 = r'$\\overline{{u^\\prime}^2}$ (m$^2$ s$^{-2}$)'\n",
    "    xlabel_str2 = r'$\\overline{{v^\\prime}^2}$ (m$^2$ s$^{-2}$)'\n",
    "    xlabel_str3 = r'$\\overline{{w^\\prime}^2}$ (m$^2$ s$^{-2}$)'\n",
    "\n",
    "# subplots\n",
    "fig, axarr = plt.subplots(1, 3, sharey='row')\n",
    "data_pfl.read_profile('u*2', tidx_start=tidx_start, tidx_end=tidx_end).plot_mean(\n",
    "                             axis=axarr[0], norm=norm, znorm=znorm, color='k',\n",
    "                             xlabel=xlabel_str1, ylabel=ylabel_str, ylim=[ymin, ymax])\n",
    "data_pfl.read_profile('v*2', tidx_start=tidx_start, tidx_end=tidx_end).plot_mean(\n",
    "                             axis=axarr[1], norm=norm, znorm=znorm, color='k',\n",
    "                             xlabel=xlabel_str2, ylabel='off')\n",
    "data_pfl.read_profile('w*2', tidx_start=tidx_start, tidx_end=tidx_end).plot_mean(\n",
    "                             axis=axarr[2], norm=norm, znorm=znorm, color='k',\n",
    "                             xlabel=xlabel_str3, ylabel='off')\n",
    "\n",
    "if f_ref:\n",
    "    data_pfl_ref.read_profile('u*2', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref).plot_mean(\n",
    "                                     axis=axarr[0], norm=norm, znorm=znorm_ref, color='k',\n",
    "                                     xlabel='off',ylabel='off', linestyle='--')\n",
    "    data_pfl_ref.read_profile('v*2', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref).plot_mean(\n",
    "                                     axis=axarr[1], norm=norm, znorm=znorm_ref, color='k',\n",
    "                                     xlabel='off',ylabel='off', linestyle='--')\n",
    "    data_pfl_ref.read_profile('w*2', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref).plot_mean(\n",
    "                                     axis=axarr[2], norm=norm, znorm=znorm_ref, color='k',\n",
    "                                     xlabel='off',ylabel='off', linestyle='--')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TKE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normalizing factor\n",
    "if f_norm:\n",
    "    norm = 1/ustar**2\n",
    "    xlabel_str = r'$TKE/u_*^2$'\n",
    "else:\n",
    "    norm = 1\n",
    "    xlabel_str = r'$TKE$ (m$^2$ s$^{-2}$)'\n",
    "\n",
    "e_res = data_pfl.read_profile('e*', tidx_start=tidx_start, tidx_end=tidx_end).plot_mean(\n",
    "                                    norm=norm, znorm=znorm, color='k',\n",
    "                                    xlabel=xlabel_str, ylabel=ylabel_str,\n",
    "                                    ylim=[ymin, ymax], label='Resolved')\n",
    "e_sgs = data_pfl.read_profile('e', tidx_start=tidx_start, tidx_end=tidx_end)\n",
    "e_sgs.z[0] = np.nan # fix the invalid depth of e at the bottom\n",
    "e_sgs.plot_mean(norm=norm, znorm=znorm, color='r', xlabel='off', ylabel='off', label='SGS')\n",
    "plt.legend(loc=4)\n",
    "\n",
    "if f_ref:\n",
    "    e_res = data_pfl_ref.read_profile('e*', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref).plot_mean(\n",
    "                                            norm=norm, znorm=znorm_ref, color='k',\n",
    "                                            xlabel='off', ylabel='off', linestyle='--')\n",
    "    e_sgs = data_pfl_ref.read_profile('e', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref)\n",
    "    e_sgs.z[0] = np.nan # fix the invalid depth of e at the bottom\n",
    "    e_sgs.plot_mean(norm=norm, znorm=znorm_ref, color='r', xlabel='off', ylabel='off', linestyle='--')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vertial velocity skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normalizing factor\n",
    "norm = 1\n",
    "\n",
    "data_pfl.read_profile('Sw', tidx_start=tidx_start, tidx_end=tidx_end).plot_mean(\n",
    "                            norm=norm, znorm=znorm, color='k',\n",
    "                            xlabel=r'$\\overline{{w^\\prime}^3} / \\left(\\overline{{w^\\prime}^2}\\right)^{3/2}$',\n",
    "                            ylabel=ylabel_str, ylim=[ymin, ymax])\n",
    "if f_ref:\n",
    "    data_pfl_ref.read_profile('Sw', tidx_start=tidx_start_ref, tidx_end=tidx_end_ref).plot_mean(\n",
    "                                    norm=norm, znorm=znorm_ref, color='k',\n",
    "                                    xlabel='off', ylabel='off', linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}