{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Multifrequency and Doppler Radar Example\n", "====================================" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If this is not what you are looking for go back to the [list of notebooks](../simple_usage.rst)\n", "\n", "In this example we will look at the multifrequency spectrum calculator first. And then we will derive the multifrequency (X-Ka-W) characteristic of some particles included in snowScatt by assuming an inverse exponential Particle Size Distribution (PSD)\n", "\n", "Multifrequency Doppler\n", "===================\n", "\n", "\n", "Initialize the calculations by loading some basic modules. And define some useful functions.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import datetime\n", "begin = datetime.datetime.now()\n", "\n", "import snowScatt\n", "\n", "from snowScatt.instrumentSimulator.radarMoments import Ze\n", "from snowScatt.instrumentSimulator.radarSpectrum import dopplerSpectrum\n", "from snowScatt.instrumentSimulator.radarSpectrum import sizeSpectrum\n", "\n", "\n", "def Nexp(D, lam):\n", " return np.exp(-lam*D)/lam # basic implementation of an inverse exponential size distribution\n", "\n", "def dB(x):\n", " return 10.0*np.log10(x)\n", "\n", "def Bd(x):\n", " return 10.0**(0.1*x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set the parameters of the calculation. The frequencies (in Herz), the vector of sizes for which the snow properties (backscatter, fallspeed and concentration) are gonna be calculated.\n", "Set additional parameters for the computation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Execution 0:00:00.566509\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "frequency = np.array([9.6e9, 35.6e9, 94.0e9]) # frequencies\n", "freq_label = ['X-band', 'Ka-band', 'W-band'] # frequencies\n", "temperature = 270.0\n", "\n", "Dmax = np.linspace(0.1e-3, 20.0e-3, 100) # list of sizes [meters]\n", "lams = 1.0/1.0e-3 # lambda parameters of the PSD [meters**-1]\n", "\n", "particle = 'Leinonen15tabA00'\n", "fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 4))\n", "concentration = 1000.0 # number m**-1 m**-3\n", "PSD = (concentration*(np.array(Nexp(Dmax, lams))))[np.newaxis, ...]\n", "\n", "for idf, freq in enumerate(frequency):\n", " wl = snowScatt._compute._c/freq\n", " spec0, vel = dopplerSpectrum(Dmax, PSD, wl, particle, temperature=temperature)\n", " spec1 = sizeSpectrum(Dmax, PSD, wl, particle, temperature=temperature)\n", "\n", " Zx = Ze(Dmax, PSD, wl, particle, temperature=temperature)\n", "\n", " ax0.plot(vel, dB(spec0[0, :]*np.gradient(vel)))\n", " ax1.plot(Dmax*1.0e3, dB(spec1[0, :]*np.gradient(Dmax)), label=freq_label[idf])\n", "for ax in (ax0, ax1):\n", " ax.grid()\n", " ax.set_ylabel('spectral power')\n", " ax.set_ylim([-90, -70])\n", "ax1.legend()\n", "ax0.set_xlabel('velocity [m/s]')\n", "ax1.set_xlabel('size [mm]')\n", "fig.tight_layout()\n", "print('Execution ', datetime.datetime.now()-begin)\n", "\n", "Z0 = dB(np.nansum((spec0*np.gradient(vel)), axis=-1))\n", "Z1 = dB(np.nansum((spec1*np.gradient(Dmax)), axis=-1)) # chack that Z0 and Z1 are equal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Triple-Frequency Signature\n", "======================\n", "In this example we will assume a series of inverse exponential PSDs with different scale parameters lambda.\n", "We will analyze how the multifrequency radar signature varies as a function of lambda for different particle types. In particular we will use Leinonen and Szyrmer subsequent riming model (B) with various ELWP values and also some particles from Ori et al. (2014) for comparison purposes" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Execution 0:00:04.699611\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "frequency = np.array([13.6e9, 35.6e9, 94.0e9]) # frequencies\n", "temperature = 270.0\n", "Nangles = 721\n", "\n", "Dmax = np.linspace(0.1e-3, 20.0e-3, 1000) # list of sizes\n", "lams = 1.0/np.linspace(0.01e-3, 11.0e-3, 100)\n", "\n", "rime = ['00', '01', '02', '05', '10', '20']\n", "particles = 'Leinonen15tabB'\n", "fig, (ax0) = plt.subplots(1, 1)\n", "# \n", "PSD = np.stack([np.array(Nexp(Dmax, l)) for l in lams])\n", "for r in rime:\n", " particle = particles + r\n", " bck = pd.DataFrame(index=Dmax, columns=frequency)\n", " for fi, freq in enumerate(frequency):\n", " wl = snowScatt._compute._c/freq\n", " eps = snowScatt.refractiveIndex.water.eps(temperature, freq, 'Turner')\n", " K2 = snowScatt.refractiveIndex.utilities.K2(eps)\n", " ssCbck, ssvel = snowScatt.backscatVel(diameters=Dmax,\n", " wavelength=wl,\n", " properties=particle,\n", " temperature=temperature)\n", " bck[freq] = wl**4*ssCbck/(K2*np.pi**5)\n", "\n", " Zx = np.array([dB((1.0e18*bck.iloc[:, 0]*Nexp(Dmax, l)*np.gradient(Dmax)).sum()) for l in lams ])\n", " Zk = np.array([dB((1.0e18*bck.iloc[:, 1]*Nexp(Dmax, l)*np.gradient(Dmax)).sum()) for l in lams ])\n", " Zw = np.array([dB((1.0e18*bck.iloc[:, 2]*Nexp(Dmax, l)*np.gradient(Dmax)).sum()) for l in lams ])\n", " \n", " ax0.plot(Zk-Zw, Zx-Zk, label='L-B'+r)\n", "\n", "particle = 'Ori_collColumns' # This is from Ori et al (2014). SSRGA parameters are derived from size resolved tables with 1 mm resolution\n", "bck = pd.DataFrame(index=Dmax, columns=frequency)\n", "for fi, freq in enumerate(frequency):\n", " wl = snowScatt._compute._c/freq\n", " eps = snowScatt.refractiveIndex.water.eps(temperature, freq, 'Turner')\n", " K2 = snowScatt.refractiveIndex.utilities.K2(eps)\n", " ssCbck, ssvel = snowScatt.backscatVel(diameters=Dmax,\n", " wavelength=wl,\n", " properties=particle,\n", " temperature=temperature)\n", " bck[freq] = wl**4*ssCbck/(K2*np.pi**5)\n", "Zx = np.array([dB((1.0e18*bck.iloc[:, 0]*Nexp(Dmax, l)*np.gradient(Dmax)).sum()) for l in lams ])\n", "Zk = np.array([dB((1.0e18*bck.iloc[:, 1]*Nexp(Dmax, l)*np.gradient(Dmax)).sum()) for l in lams ])\n", "Zw = np.array([dB((1.0e18*bck.iloc[:, 2]*Nexp(Dmax, l)*np.gradient(Dmax)).sum()) for l in lams ])\n", "ax0.plot(Zk-Zw, Zx-Zk, label='Ori-table', lw=4)\n", "\n", "particle = 'Oea14' # This is also from Ori et al. 2014 but this time we do not consider the size-dependent evolution of the SSRGA parameters. \n", "# The effect of monomers at small sizes and initial stages of aggregation will impact the multifrequency signature\n", "bck = pd.DataFrame(index=Dmax, columns=frequency)\n", "for fi, freq in enumerate(frequency):\n", " wl = snowScatt._compute._c/freq\n", " eps = snowScatt.refractiveIndex.water.eps(temperature, freq, 'Turner')\n", " K2 = snowScatt.refractiveIndex.utilities.K2(eps)\n", " ssCbck, ssvel = snowScatt.backscatVel(diameters=Dmax,\n", " wavelength=wl,\n", " properties=particle,\n", " temperature=temperature)\n", " bck[freq] = wl**4*ssCbck/(K2*np.pi**5)\n", "Zx = np.array([dB((1.0e18*bck.iloc[:, 0]*Nexp(Dmax, l)*np.gradient(Dmax)).sum()) for l in lams ])\n", "Zk = np.array([dB((1.0e18*bck.iloc[:, 1]*Nexp(Dmax, l)*np.gradient(Dmax)).sum()) for l in lams ])\n", "Zw = np.array([dB((1.0e18*bck.iloc[:, 2]*Nexp(Dmax, l)*np.gradient(Dmax)).sum()) for l in lams ])\n", "ax0.plot(Zk-Zw, Zx-Zk, label='Ori-constant', lw=4)\n", "\n", "for ax in [ax0,]:\n", " ax.legend()\n", " ax.grid()\n", " ax.set_xlabel('Ka - W [dB]')\n", " ax.set_ylabel('Ku - Ka [dB]')\n", "fig.savefig('triple_frequency.png', dpi=300)\n", "print('Execution ', datetime.datetime.now()-begin)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Go back to the [list of notebooks](../simple_usage.rst) for further snowScatt examples" ] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }