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target_cat_only.m
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target_cat_only.m
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function [] = target_cat_only_lambdaatt_cloud_new(thedate, site, iswrite)
if nargin < 1
help target_cat_only;
return;
end
clear plot
%=========set directories===
%Pathes and output file names
cat_path = [get_data_dir '/' site '/products/classification/' thedate(1:4) '/'];
path_calibrated = [get_data_dir '/' site '/calibrated/pollyxt/' thedate(1:4) '/'];
path_aclass = [get_data_dir '/' site '/products/mwl_aclass/' thedate(1:4) '/'];
%cat_path = [get_data_dir '/experimental/' site '/products/classification/' thedate(1:4) '/'];
%path_calibrated = [get_data_dir '/experimental/' site '/calibrated/pollyxt/' thedate(1:4) '/'];
%path_aclass = [get_data_dir '/experimental/' site '/products/mwl_aclass/' thedate(1:4) '/'];
%=======set file names======
file_calibrated = [path_calibrated thedate, '_' site '_pollyxt.nc'];
file_aclass = [path_aclass thedate '_' site '_mwl_aclass.nc'];
not_show_bad_values=0;
%================read data====================
%Load and concatenate the netcdf files
disp(['Loading Polly data from file : ' file_calibrated]);
filename = [file_calibrated];
[data,attribute] = load_nc_struct(filename);
min_heightbin = 1;
if isfield(data,'quality_mask_1064')
data.att_beta_0355(data.quality_mask_0355 > 0) = NaN;
data.att_beta_0532(data.quality_mask_0532 > 0) = NaN;
data.att_beta_1064(data.quality_mask_1064 > 0) = NaN;
data.att_beta_0387(data.quality_mask_0387 > 0) = NaN;
data.att_beta_0407(data.quality_mask_0407 > 0) = NaN;
data.att_beta_0607(data.quality_mask_0607 > 0) = NaN;
data.volume_depolarization(data.quality_mask_volume_depolarization > 0) = NaN;
end
% switch lower(site)
% case 'leipzig'
% minheight = 500;
% case 'krauthausen'
% minheight = 500;
% case 'melpitz'
% minheight = 500;
% end
%
%====================Preparation for target mask
%======Correct incomplete overlap====================
%overlap_0355_ascii = dlmread('pollyXT_ift_20130418-0000-0145-55sr-355.o34','\t',1,1);
%overlap_0532_ascii = dlmread('pollyXT_ift_20130418-0000-0145-55sr-532.o34','\t',1,1);
%starts reading at row offset R1 and column offset C1. For example, the offsets R1=0, C1=0 specify the first value in the file.
%To specify row and column offsets without specifying a delimiter, use an empty string as a placeholder, for example, M = dlmread(filename,'',2,1)
%overlap_0355 = 1./(imresize(overlap_0355_ascii(:,1),(size(data.range))));
%overlap_0532 = 1./(imresize(overlap_0532_ascii(:,1),(size(data.range))));
%data.att_beta_0355=data.att_beta_0355.*(overlap_0355*ones(size(data.att_beta_0355),1)')';
%data.att_beta_0387=data.att_beta_0387.*(overlap_0355*ones(size(data.att_beta_0387),1)')';
%data.att_beta_0532=data.att_beta_0532.*(overlap_0532*ones(size(data.att_beta_0532),1)')';
%data.att_beta_0607=data.att_beta_0607.*(overlap_0532*ones(size(data.att_beta_0607),1)')';
%data.att_beta_1064=data.att_beta_1064.*(overlap_0532*ones(size(data.att_beta_1064),1)')';
%======Interpolate to surface==
%attention, when ever signal ratios are implemented this interpolation must
%be rueckgaengig gemacht erden or the respective variables renames so that
%for the signal ratios no interpolation is performed
interpolheight=500;
index = find(data.height(:,1) < interpolheight);
maxindex=(max(index)+1);
interpol_before=1
if interpol_before==1
data.att_beta_1064(:,index(:,1))=data.att_beta_1064(:,maxindex)*(ones(1,(maxindex-1)));
data.att_beta_0532(:,index(:,1))=data.att_beta_0532(:,maxindex)*(ones(1,(maxindex-1)));
data.att_beta_0355(:,index(:,1))=data.att_beta_0355(:,maxindex)*(ones(1,(maxindex-1)));
data.att_beta_0387(:,index(:,1))=data.att_beta_0387(:,maxindex)*(ones(1,(maxindex-1)));
data.att_beta_0607(:,index(:,1))=data.att_beta_0607(:,maxindex)*(ones(1,(maxindex-1)));
end
% old, but loops ar enot effcient in matlab so removed
%by statement above
%for i=1:maxindex
% aa=i
%data.att_beta_1064(:,i)=data.att_beta_1064(:,maxindex);
%end
%======smooth temporally====================
smooth_t=10%20 % xx profiles are summed up , i.e. 10 means in case of polly that 10 30 sec profiles are summed so that the temporal resolution is 5 min.
smooth_v=1%9
if smooth_t>1
data.att_beta_1064 = smooth_temp(data.att_beta_1064,smooth_t)./smooth_t;
data.att_beta_0532 = smooth_temp(data.att_beta_0532,smooth_t)./smooth_t;
data.att_beta_0355 = smooth_temp(data.att_beta_0355,smooth_t)./smooth_t;
data.att_beta_0387 = smooth_temp(data.att_beta_0387,smooth_t)./smooth_t;
data.att_beta_0607 = smooth_temp(data.att_beta_0607,smooth_t)./smooth_t;
data.volume_depolarization = smooth_temp(data.volume_depolarization,smooth_t)./smooth_t;
data.time = smooth_temp_axis(data.time,smooth_t);
end
if smooth_v>1
data.att_beta_1064 = smooth_vert(data.att_beta_1064,smooth_v)./smooth_v;
data.att_beta_0532 = smooth_vert(data.att_beta_0532,smooth_v)./smooth_v;
data.att_beta_0355 = smooth_vert(data.att_beta_0355,smooth_v)./smooth_v;
data.att_beta_0387 = smooth_vert(data.att_beta_0387,smooth_v)./smooth_v;
data.att_beta_0607 = smooth_vert(data.att_beta_0607,smooth_v)./smooth_v;
data.volume_depolarization = smooth_vert(data.volume_depolarization,smooth_v)./smooth_v;
data.range = smooth_vert_axis(data.range,smooth_v);
data.height = smooth_vert_axis(data.height,smooth_v);
end
%=============================
% Noise reduction
%=============================
% Can improve this by looking at running mean of data..
% create index array [ntimes,nheights] where all noisy pixels are set to 1 and all valid pixels are set to 0
%--
% despeckle
% despeckle remove quite a lot of valid pixels
%--
%index = despeckle(data.beta_0355);
%data.beta_0355 = remove_inner_pixels(data.beta, index,'despeckle');
%index = despeckle(data.depolarization);
%data.depolarization = remove_inner_pixels(data.depolarization, index,'despeckle');
%=============================
% Finished Noise reduction
%=============================
lidar_ratio =55;%50
ang_exp =1;%1.34;
dz=abs(data.range(2)-data.range(1))
%=============================
% Molecular and particle scattering
%============================
%%---- theoretical molecular scattering profiles ----%%
model = load_model(thedate, site); %load model
%1064
[mol_return, mol_T, mol_beta] = molecular_scat_model_polly(data.range, 1064, [], model); % new EARLINET formulas, mol values at 532 nm
atten_molecular_1064 = ones(length(data.time),1) * (mol_T'); % generate field of molecular transmission
molecular_beta_1064 = ones(length(data.time),1) * (mol_beta'); % generate field of molecular backscattering
data.corr_beta_1064 = data.att_beta_1064 ./ (atten_molecular_1064.*atten_molecular_1064); % correct calibrated bsc for molecular attenuation, output m^-1 sr-^1
data.par_beta_1064 = data.corr_beta_1064 - molecular_beta_1064;
index = find(data.par_beta_1064 < 0);
data.par_beta_1064(index) = 0;
if interpol_before==0
data.par_beta_1064(:,index(:,1))=data.par_beta_1064(:,maxindex)*(ones(1,(maxindex-1)));
end
%calculate particle beta with neglected particle attenuation
data.par_ex_1064 = (data.par_beta_1064.*lidar_ratio);
atten_particle_1064 = exp(-1.*nancumsum(data.par_ex_1064,2).*dz);
data.par_beta_1064 = data.att_beta_1064 ./ (atten_molecular_1064.*atten_molecular_1064.*atten_particle_1064.*atten_particle_1064)-molecular_beta_1064;%new!!!!
time=(data.time)';
%size(time);
%355
[mol_return, mol_T, mol_beta] = molecular_scat_model_polly(data.range, 355, [], model); % new EARLINET formulas, mol values at 355 nm
atten_molecular_0355 = ones(length(data.time),1) * (mol_T');
molecular_beta_355 = ones(length(data.time),1) * (mol_beta'); % generate field of molecular backscattering
data.corr_beta_0355 = data.att_beta_0355 ./ (atten_molecular_0355.*atten_molecular_0355); % correct calibrated bsc for molecular attenuation, output m^-1 sr-^1
data.par_beta_0355 = data.corr_beta_0355 - molecular_beta_355;
index = find(data.par_beta_0355 < 0);
data.par_beta_0355(index) = 0;
if interpol_before==0
data.par_beta_0355(:,index(:,1))=data.par_beta_0355(:,maxindex)*(ones(1,(maxindex-1)));
end
%calculate particle beta with neglected particle attenuation
data.par_ex_0355 = (data.par_beta_0355.*lidar_ratio);
atten_particle_0355 = exp(-1.*nancumsum(data.par_ex_0355,2).*dz);
%plot(data.par_ex_0355(200,1:150),data.range(1:150))
%data.par_beta_0355 = data.par_beta_0355./(atten_particle_0355.*atten_particle_0355); %old
data.par_beta_0355 = data.att_beta_0355 ./ (atten_molecular_0355.*atten_molecular_0355.*atten_particle_0355.*atten_particle_0355)-molecular_beta_355;%new!!!!
%387
[mol_return, mol_T, mol_beta] = molecular_scat_model_polly(data.range, 607, [], model);
atten_molecular_0387 = ones(length(data.time),1) * (mol_T'); % generate field of molecular transmission
molecular_beta_0387 = ones(length(data.time),1) * (mol_beta');
%532
[mol_return, mol_T, mol_beta] = molecular_scat_model_polly(data.range, 532, [], model); % new EARLINET formulas, mol values at 532 nm
atten_molecular_0532 = ones(length(data.time),1) * (mol_T'); % generate field of molecular transmission
%atten_particle_0532 = exp(-1.*nancumsum(data.par_ex_0532,2).*dz);
molecular_beta_532 = ones(length(data.time),1) * (mol_beta'); % generate field of molecular backscattering
data.corr_beta_0532 = data.att_beta_0532 ./ (atten_molecular_0532.*atten_molecular_0532); % correct calibrated bsc for molecular attenuation, output m^-1 sr-^1
data.par_beta_0532 = data.corr_beta_0532 - molecular_beta_532;
index = find(data.par_beta_0532 < 0);
data.par_beta_0532(index) = 0;
if interpol_before==0
data.par_beta_0532(:,index(:,1))=data.par_beta_0532(:,maxindex)*(ones(1,(maxindex-1)));
end%calculate particle beta with neglected particle attenuation
data.par_ex_0532 = (data.par_beta_0532.*lidar_ratio);
atten_particle_0532 = exp(-1.*nancumsum(data.par_ex_0532,2).*dz);
% data.par_beta_0532 =
% data.par_beta_0532./(atten_particle_0532.*atten_particle_0532); old
data.par_beta_0532 = data.att_beta_0532 ./ (atten_molecular_0532.*atten_molecular_0532.*atten_particle_0532.*atten_particle_0532)-molecular_beta_532;%new!!!!
%Particle depolarization
dmol=0.0072%0.0125%0.0072;%53; %for 1 nm filters
data.pardepol = (data.volume_depolarization + 1) ./ (molecular_beta_532 .* (dmol*ones(length(data.time), length(data.range)) - data.volume_depolarization) ./ (data.par_beta_0532 .* (1 + dmol) ) +1 )-1; %"particle depol" at 532 nm
%607
[mol_return, mol_T, mol_beta] = molecular_scat_model_polly(data.range, 607, [], model);
atten_molecular_0607 = ones(length(data.time),1) * (mol_T'); % generate field of molecular transmission
molecular_beta_0607 = ones(length(data.time),1) * (mol_beta');
AOD_0355 = (trapz(data.par_ex_0355,2)).*dz;
AOD_0532 = (trapz(data.par_ex_0532,2)).*dz;
AOD_1064 = (trapz(data.par_ex_1064,2)).*dz;
save('aodvrgl.txt','time','AOD_0355','AOD_0532','AOD_1064','-ascii','-double', '-tabs');
%Angström exponents
data.ang355532 =log(data.par_beta_0532 ./ data.par_beta_0355) ./ log(355/532);
%index = find((data.par_beta_0355<1e-7) | (data.par_beta_0532<1e-7));
%data.ang355532(index) = nan;
data.ang5321064 =log(data.par_beta_1064 ./ data.par_beta_0532) ./ log(532/1064);
%index = find((data.par_beta_0532<1e-7) | (data.par_beta_1064<1e-7));
%data.ang5321064(index) = nan;
data.ang3551064 =log(data.par_beta_1064 ./ data.par_beta_0355) ./ log(355/1064);
%index = find((data.par_beta_0355<1e-7) | (data.par_beta_1064<1e-7));
%data.ang3551064(index) = nan;
%=============================
% Finished Molecular and Particle Scattering
%=============================
%======================================
%Mass concentration
%======================================
%
% depol_d = 0.31;
% depol_nd= 0.05;
% rho_d = 2.6;
% conversion_factor_dust= 0.65E-6;
% S_d = 50;
% data.dust_beta_0532=zeros(size(data.par_beta_0532));
% data.dust_beta_0532 = data.par_beta_0532.*(((data.pardepol-depol_nd).*(1+depol_d))./((depol_d-depol_nd).*(1+data.pardepol)));
% index2=find(data.pardepol <= depol_nd);
% data.dust_beta_0532(index2)=0;
% data.dust_mass_conc=1000000000000.*rho_d.*conversion_factor_dust.*data.dust_beta_0532*S_d;
% fff=size(data.dust_mass_conc)
% jjj=data.dust_mass_conc(1,25)
%
%=============================================================
%Read Cloudnet classification
%=============================================================
% filedate = sprintf('%04d%02d%02d', str2double(thedate(1:4)), str2double(thedate(5:6)), str2double(thedate(7:8)));
% filename = [cat_path filedate '_' site '_classification.nc']
%
% [cloudnet cloudnet_att] = load_nc_struct(filename);
% aaaa=cloudnet.target_classification;
% aaab=cloudnet.time;
% aaac=cloudnet.height;
%=============================
% Aerosol Typing
%=============================
% Flag data - cloud, aerosol, molecular, noise
% 0: Noise
% 1: molecular
% 2: cloud/aerosol but not typed
% 3: cloud (liquid, near base before ms kicks in)
% 4: aerosol
% 5: ice containing cloud
% 6: dust
%start mask with everything is noise
mask = zeros(size(data.att_beta_0355));
% signal present in 355--> molecular
index = find(~isnan(data.att_beta_0355 ));
size(index)
mask(index) = 1;
%-----------------------------------------------------------------------
%new mask
% beta 1064 "sees" something
index = find(data.par_beta_1064 > 1e-8) ; % m^-1 sr-^1 %untyped aerosol
mask(index) =2;
%size(index)
% Aerosol
index = find(data.par_beta_1064 > 2e-7 & data.ang5321064 >= 0.75 & data.pardepol < 0.07);% | isnan(data.pardepol) ));%%small aerosol vorher 1.5 und ang355532
mask(index) = 3;
index = find(data.par_beta_1064 > 2e-7 & data.par_beta_0532 > 2e-7 & data.pardepol < 0.2 & data.pardepol >= 0.07);% & abs(data.ang5321064) <=0.5); %polluted dust
mask(index) = 5;
index = find(data.par_beta_1064 > 2e-7 & data.par_beta_0532 > 2e-7 & data.pardepol >= 0.2);% & abs(data.ang5321064) <= 0.5 );%& data.pardepol <= 0.35); %dust
mask(index) = 6;
index = find(data.par_beta_1064 > 2e-7 & abs(data.ang5321064) <.75 & data.pardepol < 0.07);% | isnan(data.pardepol)));%spherical,large aerosol
mask(index) = 4;
% Liquid layers have low depolarization but high backscatter
%index = find(data.par_beta_1064 > 2e-5); %& abs(data.ang5321064) <= 0.5); %untyped cloud
%mask(index) = 7;
%index = find(data.par_beta_1064 > 2e-5 & data.pardepol <= 0.05); %most prob drops
%mask(index) = 9;
%index = find(data.par_beta_1064 > 5e-5 & data.pardepol <= 0.05 & abs(data.ang5321064) <= 0.5); %drops
droplet_bit = get_droplet_bit(data.height, data.par_beta_1064);
mask(find(droplet_bit==1)) = 7;
mask(find(droplet_bit==1 & data.pardepol <= 0.05)) = 9;
mask(find(droplet_bit==1 & data.pardepol <= 0.05 & abs(data.ang5321064) <= 0.5)) = 8;
%Ice
index = find(data.par_beta_1064 > 2e-7 & data.par_beta_0532 > 2e-7 & data.volume_depolarization >= 0.3); %most prob ice
mask(index) = 11;
index = find(data.par_beta_1064 > 2e-7 & data.par_beta_0532 > 2e-7 & data.pardepol >= 0.45); %& abs(data.ang5321064) <= 0.8); %ice vorher 1e-6 threshold
mask(index) = 10;
%data.flag = mask;
%post-processing
maskx=size(mask,1);
masky=size(mask,2);
%if liquid cloud found, set everything above which ias not cloud anymore to
%0--> no siognal
for i=1:maskx
index=find(((mask(i,:)>6)&(mask(i,:)<10)),1,'first');
index2=find((mask(i,(index+1):masky)<7 | mask(i,(index+1):masky)>9)); %changed from <9 to >9, +1 added
mask(i,((index+1)+index2-1))=0;%+1 added
end
%for i=1:maskx
% index=find(((mask(i,:)>6)&(mask(i,:)<10)),1,'first');
% index2=find((mask(i,(index+1):masky)<8 | mask(i,(index+1):masky)>9),1,'first'); %changed from <9 to >9, +1 added
% mask(i,((index+1)+index2-1):masky)=0;%+1 added
%end
%set mask to no signal below interpol heigth
index = find(data.height<interpolheight);
for i=1:maskx
mask(i,index) = 0;
data.par_beta_0355(i,index) = NaN;
data.par_beta_0532(i,index) = NaN;
data.par_beta_1064(i,index) = NaN;
end
%=============================
% NetCDF Output
%(active, if argument 'iswrite'=1)
%=============================
if iswrite
%%reduce data arrays to max_heightbins
max_height = 10000; %m
%%-- Correct missing values --%%
missing_value = -999;
%%--- setup netcdf file ---%%
if ~isempty(data.time)
if ~exist(path_aclass,'dir')
status=mkdir(path_aclass);
if ~status; disp(['Could not create directory in ' path_aclass]); return; end;
end
disp(['Storing mwl target classification to' file_aclass]);
g = netcdf(file_aclass,'clobber');
%%--- write dimensions to netcdf file ---%%
g('time') = length(data.time);
g('range') = length(data.range)
g('channels') = 3; %added
%%--- write variables and attributes to netcdf file ---%%
g{'time'} = ncfloat('time');
g{'time'}(:) = data.time;
g{'time'}.units = ['hours since ' thedate ' 00:00:00 +00:00'] ;
g{'time'}.long_name = 'Decimal hours from midnight UTC to the middle of each ray' ;
g{'time'}.axis = 'T' ;
g{'time'}.missing_value = missing_value;
g{'time'}.FillValue_ = missing_value;
g{'range'} = ncfloat('range');
g{'range'}(:) = data.range;
g{'range'}.units = 'm' ;
g{'range'}.long_name = 'Range from lidar to centre of range bin' ;
g{'height'} = ncfloat('range');
g{'height'}(:) = data.range*cos(5.*pi./180);
g{'height'}.units = 'm' ;
g{'height'}.long_name = 'Height of centre of range bin above lidar' ;
g{'height'}.axis = 'Z' ;
g{'latitude'} = ncfloat;
g{'latitude'}(:) = data.latitude;
g{'latitude'}.units = 'degrees_north' ;
g{'latitude'}.long_name = 'Latitude of lidar' ;
g{'latitude'}.standard_name = 'latitude' ;
g{'longitude'} = ncfloat;
g{'longitude'}(:) = data.longitude;
g{'longitude'}.units = 'degrees_east' ;
g{'longitude'}.long_name = 'Longitude of lidar' ;
g{'longitude'}.standard_name = 'longitude' ;
g{'altitude'} = ncfloat;
g{'altitude'}(:) = data.altitude;
g{'altitude'}.units = 'm' ;
g{'altitude'}.long_name = 'Height of instrument above mean sea level' ;
g{'lidar_beam_divergence'} = ncfloat;
g{'lidar_beam_divergence'}(:) = 0.2;
g{'lidar_beam_divergence'}.long_name = 'Lidar laser beam divergence';
g{'lidar_beam_divergence'}.units = 'mrad';
g{'lidar_telescope_field_of_view'} = ncfloat;
g{'lidar_telescope_field_of_view'}(:) = 2;
g{'lidar_telescope_field_of_view'}.long_name = 'Lidar telescope field of view';
g{'lidar_telescope_field_of_view'}.units = 'mrad';
g{'angstroem'} = ncfloat;
g{'angstroem'}(:) = ang_exp;
g{'angstroem'}.units = '1' ;
g{'angstroem'}.long_name = 'Angstroem exponent used for the calculation of the quasi backscatter coefficients' ;
g{'lidar_ratio'} = ncfloat;
g{'lidar_ratio'}(:) = lidar_ratio;
g{'lidar_ratio'}.units = 'sr' ;
g{'lidar_ratio'}.long_name = 'lidar ratio at 1064 nm used to obtain the quasi particle exctinction coefficient' ;
g{'flag'} = ncbyte('time','range');
g{'flag'}(:) = mask;
g{'flag'}.long_name = 'Lidar target classification';
g{'flag'}.plot_range = ncbyte([0 11]);
g{'flag'}.legend_key_red =[255 , 0.9*255 , 0.6*255 , 221 , 231 , 136 , 0 , 120 , 058 , 180 , 017 , 134]/255;
g{'flag'}.legend_key_green=[255 , 0.9*255 , 0.6*255 , 204 , 109 , 034 , 0, 028 , 137 , 221 , 119 , 187]/255;
g{'flag'}.legend_key_blue =[255 , 0.9*255 , 0.6*255 , 119 , 046 , 000 , 0 , 129 , 201 , 247 , 051 , 106]/255;
g{'flag'}.definition= ...
['0: No signal' 10 ...
'1: Clean atmosphere' 10 ...
'2: Non-typed particles/low conc.' 10 ...
'3: Aerosol: small' 10 ...
'4: Aerosol: large, spherical' 10 ...
'5: Aerosol: mixture, partly non-spherical' 10 ...
'6: Aerosol: large, non-spherical' 10 ...
'7: Cloud: non-typed' 10 ...
'8: Cloud: water droplets' 10 ...
'9: Cloud: likely water droplets' 10 ...
'10: Cloud: ice crystals' 10 ...
'11: Cloud: likely ice crystals' ];
g{'flag'}.long_definition= ...
['0: No signal' 10 ...
'1: Clean atmosphere' 10 ...
'2: Non-typed particles/low conc.' 10 ...
'3: Aerosol: small' 10 ...
'4: Aerosol: large, spherical' 10 ...
'5: Aerosol: mixture, partly non-spherical' 10 ...
'6: Aerosol: large, non-spherical' 10 ...
'7: Cloud: non-typed' 10 ...
'8: Cloud: water droplets' 10 ...
'9: Cloud: likely water droplets' 10 ...
'10: Cloud: ice crystals' 10 ...
'11: Cloud: likely ice crystals' ];
%
% Prepare for saving
%
if not_show_bad_values==1
index = find((mask==0) | (mask==1) | (mask==2)); %find bins of no signal or clear sky
data.ang355532(index) = NaN; % do not save particle properties for those pixels
data.ang5321064(index) = NaN;
data.ang3551064(index) = NaN;
data.pardepol(index) = NaN;
%data.par_beta_0355(index)=NaN;
%data.par_beta_0532(index)=NaN;
%data.par_beta_1064(index)=NaN;
end
g=store_NC_mol_beta(g,'mol_beta_0355' , '355 nm', molecular_beta_355, missing_value);
g=store_NC_mol_beta(g,'mol_beta_0532' , '532 nm', molecular_beta_532, missing_value);
g=store_NC_mol_beta(g,'mol_beta_1064' , '1064 nm', molecular_beta_1064, missing_value);
g=store_NC_beta(g,'beta_0355' , 'Quasi particle bsc at 355 nm', data.par_beta_0355, missing_value);
g=store_NC_beta(g,'beta_0532' , 'Quasi particle bsc at 532 nm', data.par_beta_0532, missing_value);
g=store_NC_beta(g,'beta_1064' , 'Quasi particle bsc at 1064 nm', data.par_beta_1064, missing_value);
g=store_NC_ang(g,'ang355532' , 'Angstroem Exponent (355 - 532)' , data.ang355532, missing_value);
g=store_NC_ang(g,'ang5321064' , 'Angstroem Exponent (532 - 1064)', data.ang5321064, missing_value);
g=store_NC_ang(g,'ang3551064' , 'Angstroem Exponent (355 - 1064)', data.ang3551064, missing_value);
g=store_NC_depol(g,'particle_depolarization', 'Quasi particle depolarization ratio (532 nm)', data.pardepol, missing_value);
g=store_NC_depol(g,'volume_depolarization', 'Calibrated volume depolarization (532 nm)', data.volume_depolarization, missing_value);
% g=store_NC_mass_conc(g,'dust_mass_conc', 'Dust mass concetration after Poliphon', data.dust_mass_conc, missing_value);
%%--- write global attributes to netcdf file ---%%
g.Conventions = 'CF-1.0';
g.system = attribute.global.system;
g.source = g.system;
g.location = attribute.global.location;
g.institution = attribute.global.institution;
g.title = attribute.global.title;
history = '';
g.day = ncshort(str2double(thedate(7:8)));
g.month = ncshort(str2double(thedate(5:6)));
g.year = ncshort(str2double(thedate(1:4)));
g=close(g);
else
disp(['No data to write for ' file_aclass]);
end
end
function g = store_NC_background(g,variable,title,data,missing_value)
g{variable} = ncfloat('time');
g{variable}(:) = data;
g{variable}.units = ' ' ;
g{variable}.long_name = [variable '(' title ')'];
g{variable}.missing_value = missing_value ;
g{variable}.FillValue_ = missing_value ;
function g = store_NC_beta(g,variable,title, data,missing_value)
g{variable} = ncfloat('time','range');
g{variable}(:) = data;
g{variable}.units = 'sr-1 Mm-1' ;
g{variable}.units_html = 'sr<sup>-1</sup> Mm<sup>-1</sup>' ;
%g{variable}.long_name = ['Quasi particle backscatter coefficient (' title ')'];
g{variable}.long_name = [variable ' (' title ')'];
g{variable}.missing_value = missing_value ;
g{variable}.FillValue_ = missing_value ;
if strcmp(variable,'beta_1064')
g{variable}.plot_range = [0 5e-6];%was two
elseif strcmp(variable,'beta_0532')
g{variable}.plot_range = [0 10e-6];
else
g{variable}.plot_range = [0 1e-5];
end
g{variable}.plot_scale = 'linear';
function g = store_NC_mass_conc(g,variable,title, data,missing_value)
g{variable} = ncfloat('time','range');
g{variable}(:) = data;
g{variable}.units = 'mug cm⁻3' ;
g{variable}.units_html = 'mug<sup></sup> cm<sup>-3</sup>' ;
%g{variable}.long_name = ['Quasi particle backscatter coefficient (' title ')'];
g{variable}.long_name = [variable ' (' title ')'];
g{variable}.missing_value = missing_value ;
g{variable}.FillValue_ = missing_value ;
g{variable}.plot_range = [0 400];
g{variable}.plot_scale = 'linear';
function g = store_NC_mol_beta(g,variable,title, data,missing_value)
g{variable} = ncfloat('time','range');
g{variable}(:) = data;
g{variable}.units = 'sr-1 m-1' ;
g{variable}.units_html = 'sr<sup>-1</sup> m<sup>-1</sup>' ;
g{variable}.long_name = ['Molecular backscatter coefficient (' title ')'];
g{variable}.long_name = [variable ' (' title ')'];
g{variable}.missing_value = missing_value ;
g{variable}.FillValue_ = missing_value ;
g{variable}.plot_range = [1e-7 1e-4];
g{variable}.plot_scale = 'logarithmic';
function g = store_NC_depol(g,variable, title, data,missing_value)
g{variable} = ncfloat('time','range');
g{variable}(:) = data;
g{variable}.units = '1' ;
g{variable}.units_html = '1' ;
g{variable}.long_name = [variable ' (' title ')'];
g{variable}.missing_value = missing_value ;
g{variable}.FillValue_ = missing_value ;
g{variable}.plot_range = [0 0.5];
g{variable}.plot_scale = 'linear';
function g = store_NC_ang(g,variable,title, data,missing_value)
g{variable} = ncfloat('time','range');
g{variable}(:) = data;
g{variable}.units = '1';
g{variable}.units_html = '1' ;
g{variable}.long_name = [variable ' (' title ')'];
g{variable}.missing_value = missing_value ;
g{variable}.FillValue_ = missing_value ;
g{variable}.plot_range = [0 2];
g{variable}.plot_scale = 'linear';
function index = despeckle(data)
[a,b] = size(data);
index = find(~isfinite(data(1:a-2,2:b-1)) & ~isfinite(data(3:a,2:b-1)) & ~isfinite(data(2:a-1,1:b-2)) & ~isfinite(data(2:a-1,3:b)));
function param_out = remove_inner_pixels(param, index, type, value)
if nargin<3
type='line';
value = NaN;
end
if nargin<4
value = NaN;
end
switch type
case 'line'
param_inner = param(2:end-1,:);
param_inner(index) = value;
param_out = param;
param_out(2:end-1,:) = param_inner;
case 'despeckle'
param_inner = param(2:end-1,2:end-1);
param_inner(index) = value;
param_out = param;
param_out(2:end-1,2:end-1) = param_inner;
end
function droplet_bit = get_droplet_bit(height, beta)
beta0 = beta;
beta0(find(~isfinite(beta0))) = 0;
droplet_bit = zeros(size(beta));
size(beta)
dheight = median(diff(height));
nrays = size(beta,1);
ngates = length(height);
threshold_beta = 2e-5;
min_jump = 10; % Factor of 20
jump_distance = 250; % m
jump_gates = ceil(jump_distance / dheight);
final_gate = ngates-jump_gates;
search_gates_below = ceil(100/dheight);
search_gates_above = ceil(300/dheight);
%radar_search_gates_above = ceil(2000/dheight);
diff_factor = 0.25;
%step_gates = 2;
%min_temperature = 273.16 - 40; % No droplets colder than this
%return;
disp('Calculating location of liquid water cloud droplets')
for ii = 1:nrays
%base_liquid = Nan
%top_liquid = Nana
start_gate = 2;
profile = beta0(ii,:);
while start_gate <= final_gate
ihighbeta = min(find(profile(start_gate:(length(profile)-search_gates_above)) > threshold_beta)) ...
+ start_gate - 1;
% Peak beta might be a little higher: find the highest beta in
% this vicinity - but would this be the best approach?
% [] = max(profile(ihighbeta+[0:search_gates_above])
%ihighbeta = ihighbeta +
if isempty(ihighbeta)
%if (start_gate > 2)
% Lidar extinguished - find some radar-only liquid clouds
%top_gate = min(find(cold_bit(ii,:)));
% else
% if (profile(1) > threshold_beta)
% droplet_bit(ii,1) = 1;
% top_gate = min(find(cold_bit(ii,:)));
% % do we need start_gate = 1 here?
% if (top_gate > start_gate)
% top_gate = max(find(~isfinite(Z(ii,start_gate:top_gate))))+start_gate-1;
% if ~isempty(top_gate)
% droplet_bit(ii, find(isfinite(Z(ii,start_gate:top_gate)))+start_gate-1) = 1;
% end
% end
% end
%end
%ii
break
end
if min(profile(ihighbeta+[1:jump_gates]) ./ profile(ihighbeta)) < 1./min_jump
% Candidate liquid water
% Find the base
search_start = max(1, ihighbeta-search_gates_below);
diff_profile = diff(profile(search_start:ihighbeta));
max_diff = max(diff_profile);
base_liquid = min(find(diff_profile > max_diff*diff_factor))+search_start;
% Find the top
% First see if profile goes to zero
top_liquid = min(find(~profile(ihighbeta+[1:search_gates_above])))+ihighbeta-1;
if isempty(top_liquid)
diff_profile = diff(profile(ihighbeta+[0:search_gates_above]));
max_diff = max(-diff_profile);
top_liquid = max(find(-diff_profile > max_diff*diff_factor))+ihighbeta-1;
end
droplet_bit(ii, base_liquid:top_liquid) = 1;
end
start_gate = ihighbeta+1;
% Set to wet
end
% Previously we did this which seems very inefficient: repeated
% analysis of the same pixels:
% start_gate+step_gates;
% This seems more sensible:
end
%droplet_bit(find(temperature < min_temperature)) = 0;