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Mesosphere-Stratosphere-Troposphere(MST) Radar Data Analysis

In this example we  analyse data  from MST (mesosphere-stratosphere-troposphere) radar observations. MST radars used to do observation of the dynamics of the lower and middle atmosphere to study winds, waves, turbulence and instabilities generate irregularities in the atmosphere. The reflected radar signals from the random irregularities are collected by the receiver antenna. The return signal strength is highly depending on the refractive index which is a function of atmospheric parameters such as humidity, temperature, and pressure and electron density. Hence those parameters will highly affect the signal to noise ratio (SNR). Input data file structure {UT, Altitude,Signal amplitude (linear),Signal-to-noise ratio (SNR), dB, Zonal wind, m/s, Meridional wind, m/s}

clear all; clc; close all;
% Data files name ID
fl = {'150 m height resolutions';
      '1200 m height resolutions';
      'Barker Coding';
      'Complementary Coding';
      'Uncoded Data';
    };
% Files for three different observation days
fid  = { 'TXT_20061211_test1.fca','TXT_20071010_test1.fca',...
                    'TXT_20081014_test1.fca';   % 150 m height resolutions
        'TXT_20061211_test2.fca','TXT_20071010_test2.fca',...
                    'TXT_20081014_test2.fca';   % 1200 m height resolutions
       'TXT_20061211_test3.fca','TXT_20071010_test3.fca',...
                    'TXT_20081014_test3.fca';   % Barker coding
        'TXT_20061211_test4.fca','TXT_20071010_test4.fca',...
                    'TXT_20081014_test4.fca';   % Complementary coding
        'TXT_20061211_test5.fca','TXT_20071010_test5.fca',...
                    'TXT_20081014_test5.fca';   % Uncoded data
};
dates = ['2006 Dec 11'; '2007 Oct 10'; '2008 Dec 14' ];

SNR value as a function of universal time and altitude for three different observation days

fs = size(fid,1);
fd = size(fid,2);
for jd = 1:fd
    for id = 1:fs
        clear SNR;
        fname = fid{id,jd};
        data    = load(fname);
        time    = unique(data(:,1));
        alt     = unique(data(:,2));
        size_t  = size(time,1);
        size_a  = size(alt,1);
        % Signal-to-noise ratio (SNR), dB
        for i = 1:size_a
            for j = 1:size_t
                    SNR(i,j)= data(i+((j-1)*size_a),4);
            end
        end
        % Plot
        if(id > 2 )
            FigHandle = figure(2);
            hold on;
            ii = id - 2;
            subplot(3,3,ii+(jd-1)*3);
            colormap(jet);
            pcolor(time,alt,SNR);
            shading flat;
            caxis([-25,30]);
            if(id == 5)
             colorbar;
            end
            if(id == 3)
                ylabel('Altitude [km]');
            end
            if((id == 4))
                xlabel(['UT/Date: ',dates(jd,:) ]);
            end
                if((jd == 1)&&(id == 4) )
                    title(['Signal-to-Noise Ratio(SNR) [dB]', fl(id)]);
                else
                       if(jd == 1)
                           title(fl(id));
                       end
                end
            set(FigHandle, 'Position', [100, 0, 800, 800]);
           else
                FigHandle = figure(1);
                hold on;
                subplot(3,2,id+(jd-1)*2);
                colormap(jet);
                pcolor(time,alt,SNR);
                shading flat;
                caxis([-25,30]);
                if(id == 2)
                    colorbar;
                end
                if(id == 1)
                    ylabel('Altitude [km]');
                end
                xlabel(['UT/Date: ',dates(jd,:) ]);
                if((jd == 1))
                    title(['Signal-to-Noise Ratio(SNR) [dB]', fl(id)]);
                end
            set(FigHandle, 'Position', [100, 0, 600, 800]);
        end
    end
endSR_mod2_01 SR_mod2_02

Magnitude and direction of horizontal winds

The figures below provide the horizontal wind variation over the altitude and universal time for three different days. The direction of the wind is mostly from west to east. The results are expected as the wind in this layer of atmosphere moves west to east because of the Coriolis acceleration due to force caused by the rotation of the earth.

clear all; clc; close all;
dates = ['2006 Dec 11'; '2007 Oct 10'; '2008 Dec 14' ];
% Data files name ID
fid = {'TXT_20061211_test4.fca','TXT_20071010_test4.fca',...
                    'TXT_20081014_test4.fca' };     % Complementary coding
% Radar with Complementary coding
for id = 1:3
data   = load(fid{id});
time   = unique(data(:,1));
alt    = unique(data(:,2));
size_t = size(time,1);
size_a = size(alt,1);
% Signal-to-noise ratio (SNR), dB
for i = 1:size_a
    for j = 1:size_t
            zWind(i,j)= data(i+((j-1)*size_a),5); % Zonal Wind, East
            mWind(i,j)= data(i+((j-1)*size_a),6); % Meridional Wind, North
    end
end
hWind = sqrt(zWind.^2 + zWind.^2);       % Horizontal Wind [m/s]
%dWind=  atan2(zWind,mWind)*180/pi + 180; % Wind Direction angle, Zero in North direction
a_max = 60;
a_min = 30;
FigHandle = figure(2 + id);
set(FigHandle, 'Position', [100, 0, 800, 400]);
subplot(2,2,[1 3]);
colormap(cool);
pcolor(time,alt(1:a_max),hWind(1:a_max,:));
shading flat;
colorbar;
xlabel(['UT/Date:',dates(id,:)]);
ylabel('Altitude [km]');
title('Horizontal wind speed [m/s], Complementary coded signal');
subplot(2,2,[2 4]);
hold on;
whitebg([0.0 .0 .2]);
quiver(time,alt(1:a_max),zWind(1:a_max,:),mWind(1:a_max,:),'r');
%contour(time,alt(1:a_max),hWind(1:a_max,:));
xlabel(['UT/Date:',dates(id,:)]);
title('Horizontal wind direction(top - North,right - East)');
axis([min(time),max(time),min(alt(1:a_max)),max(alt(1:a_max))]);
hold off;
end

 

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1 Comment

  1. these are some great and important information.

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