IWV_and_ZTD_from_radiosonde.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Radiosonde data to refractivity / IWV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IWV in $\\frac{kg}{m^2}$ is defined as integral along the mean density of water vapor [kg/m^-3] for each layer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$IWV=\\int_{h1}^{h2}\\rho_v dh=\\sum_{i=1}^{n}\\rho_{d,i}r_i\\Delta h_i$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With $\\rho_v$ the mean density of water vapor for each layer in $\\frac{kg}{m^3}$, $dh$ the height difference between each layer, $\\rho_d$ the dry air density in Pa, $r$ the mixing ratio of the water vapor mass to the mass of dry air (dimensionless) and $\\Delta h_i$ the altitude steps in meter. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neglecting the altitude step $i$ for simplicity, the dry air density $\\rho_d$ may be expressed as:\n",
    "$$\\rho_d=\\frac{P}{R_dT}$$\n",
    "Where $P$ is the observed air pressure in Pa, $R_d$ the specific gas constant for dry air: $R_d =287.058\\frac{J}{kg K}$ and $T$ the measured temperature in $K$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mixing ratio $r$ may be expressed as:\n",
    "$$r=0.622\\frac{e}{P-e}$$\n",
    "With $e$ the vapor pressure and P the air pressure. Together with the relative humidity (RH) measurements the vapor pressure is defined as:\n",
    "$$e=\\frac{RH\\cdot e_{sat}}{100}$$\n",
    "and $e_{sat}$, the saturation vapor pressure, as:\n",
    "$$e_{sat}=6.112[Pa]\\cdot exp(\\frac{17.62T}{243.12+T})$$\n",
    "With T in degree Celsius!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The relationship between IWV and Precipitable Water Vapor (PWV) in mm:\n",
    "$$PWV=\\frac{IWV}{\\rho}$$\n",
    "with $\\rho$ defined as the density of liquid water."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The refractivity can be expressed as:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$N=77.6\\cdot \\frac{P}{T} + 3.73\\cdot 10^5 \\frac{e}{T^2}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "with $T$ in K."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example dataset deBilt (Netherlands) 23.06.2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now compute the IWV and Refractivity for a dataset sample in June 2017 from a radiosonde measurement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is extracted from the igra2 archive. This day was dominated by heavy rainfall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pres = np.array([1020, 1000, 997, 925, 850, 830, 751, 716, 700, 673, 633, 574, 538, 500, 468, 400, 399, 324, 300, 279, 274, 250, 229, 227, 223, 212, 203, 200, 167, 152, 150, 133, 100])\n",
    "gph = np.array([2, 173, 201, 845, 1566, 1769, 2598, 2989, 3173, 3499, 3989, 4759, 5260, 5819, 6330, 7495, 7518, 9003, 9534, 10021, 10142, 10751, 11322, 11379, 11494, 11825, 12110, 12207, 13370, 13972, 14057, 14827, 16633])\n",
    "temp = np.array([20.8500000000000, 22.2500000000000, 22.4500000000000, 18.4500000000000, 13.6500000000000, 11.8500000000000, 8.45000000000005, 5.05000000000001, 4.45000000000005, 2.45000000000005, -1.64999999999998, -6.64999999999998, -11.0500000000000, -13.6500000000000, -15.8500000000000, -23.2500000000000, -23.2500000000000, -35.4500000000000, -39.6500000000000, -44.0500000000000, -44.2500000000000, -48.2500000000000, -52.2500000000000, -52.2500000000000, -51.0500000000000, -48.0500000000000, -49.4500000000000, -50.2500000000000, -56.2500000000000, -53.0500000000000, -53.6500000000000, -56.8500000000000, -56.6500000000000])\n",
    "rh = np.array([84, 73.3000000000000, 68.8000000000000, 74.6000000000000, 75.2000000000000, 82.4000000000000, 61.3000000000000, 90.6000000000000, 78.5000000000000, 69.5000000000000, 98.5000000000000, 76.7000000000000, 84.5000000000000, 84.8000000000000, 84.6000000000000, 78.5000000000000, 77.1000000000000, 72.5000000000000, 63.6000000000000, 60.2000000000000, 62.3000000000000, 61.8000000000000, 48.5000000000000, 37.8000000000000, 0.900000000000000, 4.70000000000000, 12.1000000000000, 11.9000000000000, 6.60000000000000, 1.10000000000000, 1.10000000000000, 0.900000000000000, 0.900000000000000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temp - geopotential height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f42c2b260b8>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PB8tPA5eHtT/jIYuAjmaWBnwNeNfdd7r7LuBdYEKwrr27L/LQJ94zYa8V8xIT4rh2WHfe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\nHERE5CgKBxEROYrCQUREjvL/APfwnn4JF9B2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42c4df5dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(temp, gph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pres - geopotential height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f42c2a65978>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b2Tozm+DVf29mmWa20ruNCFrn12aWZmabzOzKoPpwr5ZmZo+VtScRqVitmtZj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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42c7797860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pres, gph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RH - geopotential height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f42c2a53860>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QzEPbD9KbON4dHw0bC4tygi6qnGFjHvHsmXmYnZm1FhGZQnnZEc6cX8CZ8wuG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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42c2a913c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rh, gph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we convert the data to IWV:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alt = gph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "e_sat = 0.611*np.exp((17.2694*temp)/(temp+273.15-35.86))\n",
    "e_sat = e_sat*10\n",
    "vap_pres = rh*e_sat/100\n",
    "Md = 0.0289644\n",
    "R = 8.31432\n",
    "p_dry = (Md/R)*((pres*100)/(temp+273.15))\n",
    "mix_rat = 0.622*(vap_pres/(pres))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "iwv = 0\n",
    "iwv_store = np.array([])\n",
    "for x in range(0,len(e_sat)-1):\n",
    "    cur_iwv = (p_dry[x]*mix_rat[x]*(alt[x+1]-alt[x]))\n",
    "    iwv = iwv + cur_iwv\n",
    "    iwv_store = np.append(iwv_store, cur_iwv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 77.6*(pres/(temp+273.15)) + 3.73*10**5*(vap_pres/(temp+273.15)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ZTD = 0\n",
    "for x in range(0,len(pres)-1):\n",
    "    ZTD = ZTD+ ((N[x]*alt[x+1]) - (N[x]*alt[x]))\n",
    "ZTD = 10**-6*ZTD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IWV along vertical track:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f42c29cce10>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g7aYDPL2jnoNN7by09whvX1rFZUurOO+kCl36EZGcpT4H4HBzO797tZa1mw7wh1frONYe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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42c2b5bf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(iwv_store, gph[0:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IWV accumulated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.055848440752555"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iwv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ZTD from refractivity N:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.4592196291858173"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ZTD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}