Single Column Forcings.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "from netCDF4 import Dataset\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (18.0, 10.0) # Large figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# gx3v7 output; daily averages\n",
    "casename='C1850ECO.T62_g37.ciso.marbl_dev_levy'\n",
    "rundir='/glade2/scratch2/mlevy/%s/run' % casename\n",
    "date='0001-01-02'\n",
    "#print '%s/%s.pop.h.%s.nc' % (rundir, casename, casename)\n",
    "hist=Dataset('%s/%s.pop.h.%s.nc' % (rundir, casename, date), 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Longitude: 143.3\n",
      "Latitude: -45.6946538378\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Dictionaries to store data and metadata\n",
    "data_to_write = dict()\n",
    "metadata = dict()\n",
    "\n",
    "# Pick an (i,j)\n",
    "# (17,18) is Southern Ocean (28.1 E, 50.576 S); has 56 levels\n",
    "# (19,50) is near SOFS site SW of Tasmania (143.3 E, 45.695 S); has 54 levels\n",
    "lat_in = 19\n",
    "lon_in = 50\n",
    "print \"Longitude:\", hist.variables['TLONG'][lat_in,lon_in]\n",
    "print \"Latitude:\", hist.variables['TLAT'][lat_in,lon_in]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimum depth from surface to midpoint of layer: 5.0\n",
      "maximum depth from surface to midpoint of layer: 5374.999817\n",
      "minimum depth from surface to top of layer: 0.0\n",
      "maximum depth from surface to top of layer: 5499.990364\n",
      "Number of active levels: 54\n",
      "Number of levels: 60\n"
     ]
    }
   ],
   "source": [
    "###############\n",
    "# DOMAIN INFO #\n",
    "###############\n",
    "\n",
    "# Note: LHS uses MARBL naming convention, everything converted to mks units\n",
    "data_to_write['km'] = hist.dimensions['z_t'].size\n",
    "metadata['km'] = dict()\n",
    "metadata['km']['longname'] = 'Number of levels'\n",
    "\n",
    "data_to_write['kw'] = data_to_write['km']+1 # just need a new dimension for zw, which is size km+1\n",
    "metadata['kw'] = dict()\n",
    "metadata['kw']['longname'] = 'Number of interfaces'\n",
    "\n",
    "data_to_write['kmt'] = hist.variables['KMT'][lat_in,lon_in]\n",
    "metadata['kmt'] = dict()\n",
    "metadata['kmt']['longname'] = 'Number of active levels'\n",
    "\n",
    "data_to_write['zt'] = hist.variables['z_t'][:]/100.\n",
    "metadata['zt'] = dict()\n",
    "metadata['zt']['longname'] = 'depth from surface to midpoint of layer'\n",
    "metadata['zt']['units'] = 'm'\n",
    "\n",
    "data_to_write['zw'] = np.append(hist.variables['z_w'][:]/100.,\n",
    "                                hist.variables['z_w_bot'][data_to_write['km']-1]/100.)\n",
    "metadata['zw'] = dict()\n",
    "metadata['zw']['longname'] = 'depth from surface to top of layer'\n",
    "metadata['zw']['units'] = 'm'\n",
    "\n",
    "data_to_write['delta_z'] = hist.variables['dz'][:]/100.\n",
    "metadata['delta_z'] = dict()\n",
    "metadata['delta_z']['longname'] = 'thickness of layer'\n",
    "metadata['delta_z']['units'] = 'm'\n",
    "\n",
    "for domain_var in ['zt', 'zw']:\n",
    "    print \"minimum %s: %s\" % (metadata[domain_var]['longname'], np.min(data_to_write[domain_var]))\n",
    "    print \"maximum %s: %s\" % (metadata[domain_var]['longname'], np.max(data_to_write[domain_var]))\n",
    "\n",
    "for domain_var in ['kmt', 'km']:\n",
    "    print \"%s: %s\" % (metadata[domain_var]['longname'], data_to_write[domain_var])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dust Flux: 4.12074033795e-13 (kg/m^2)/s\n",
      "Solar Short-Wave Heat Flux: 239.414673416 W/m^2\n"
     ]
    }
   ],
   "source": [
    "#######################################\n",
    "# Interior Forcings from history file #\n",
    "#######################################\n",
    "\n",
    "data_to_write['dustflux'] = hist.variables['DUST_FLUX'][0,lat_in, lon_in]*10.\n",
    "metadata['dustflux'] = dict()\n",
    "metadata['dustflux']['longname'] = 'Dust Flux'\n",
    "metadata['dustflux']['units'] = '(kg/m^2)/s'\n",
    "\n",
    "data_to_write['surf_shortwave'] = hist.variables['SHF_QSW'][0,lat_in, lon_in]\n",
    "metadata['surf_shortwave'] = dict()\n",
    "metadata['surf_shortwave']['longname'] = 'Solar Short-Wave Heat Flux'\n",
    "metadata['surf_shortwave']['units'] = 'W/m^2'\n",
    "\n",
    "data_to_write['potemp'] = hist.variables['TEMP'][0,:,lat_in, lon_in]\n",
    "metadata['potemp'] = dict()\n",
    "metadata['potemp']['longname'] = 'Potential Temperature'\n",
    "metadata['potemp']['units'] = 'degC'\n",
    "\n",
    "data_to_write['salinity'] = hist.variables['SALT'][0,:,lat_in, lon_in]/1000.\n",
    "metadata['salinity'] = dict()\n",
    "metadata['salinity']['longname'] = 'Salinity'\n",
    "metadata['salinity']['units'] = 'kg/kg'\n",
    "\n",
    "for surface_forcing in ['dustflux', 'surf_shortwave']:\n",
    "    print \"%s: %s %s\" % (metadata[surface_forcing]['longname'],\n",
    "                         data_to_write[surface_forcing],\n",
    "                         metadata[surface_forcing]['units'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dust Flux: 4.12074033795e-13 (kg/m^2)/s\n",
      "Sea Surface Temperature: 12.5062636761 degC\n",
      "Sea Surface Salinity: 0.0348654364904 kg/kg\n"
     ]
    }
   ],
   "source": [
    "######################################\n",
    "# Surface Forcings from history file #\n",
    "######################################\n",
    "\n",
    "data_to_write['dust_flux'] = data_to_write['dustflux']\n",
    "metadata['dust_flux'] = dict(metadata['dustflux'])\n",
    "\n",
    "data_to_write['sst'] = data_to_write['potemp'][0]\n",
    "metadata['sst'] = dict(metadata['potemp'])\n",
    "metadata['sst']['longname'] = 'Sea Surface Temperature'\n",
    "\n",
    "data_to_write['sss'] = data_to_write['salinity'][0]\n",
    "metadata['sss'] = dict(metadata['salinity'])\n",
    "metadata['sss']['longname'] = 'Sea Surface Salinity'\n",
    "\n",
    "for surface_forcing in ['dust_flux', 'sst', 'sss']:\n",
    "    print \"%s: %s %s\" % (metadata[surface_forcing]['longname'],\n",
    "                         data_to_write[surface_forcing],\n",
    "                         metadata[surface_forcing]['units'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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DhUNTuXF8Dwanq/moiIhIW2r2WvaU1bApv4Kc/Ao25juzNPbu/+IsjayuUVw5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\nJEmSJEkaGhY6JEmSJEnS0PgbPsA1c1Bq6ZEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aaad7c3b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Potential Temperature\n",
    "for n, interior_forcing in enumerate(['potemp', 'salinity']):\n",
    "    plt.subplot(1,2,n+1);\n",
    "    plt.plot(data_to_write[interior_forcing], data_to_write['zt']);\n",
    "    plt.title(\"%s in the Column\" % metadata[interior_forcing]['longname']);\n",
    "    plt.xlabel(\"%s (%s)\" % (metadata[interior_forcing]['longname'], metadata['potemp']['units']));\n",
    "    plt.ylim(max(data_to_write['zt']), min(data_to_write['zt']));\n",
    "    if n == 0:\n",
    "        # Only want y-axis label on left-most plot\n",
    "        plt.ylabel(\"depth (m)\");\n",
    "    if interior_forcing == 'potemp':\n",
    "        plt.xlim(0., 13.);\n",
    "    if interior_forcing == 'salinity':\n",
    "        plt.xlim(0.034, 0.035);\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}