function PID_fraction_batch(myDate); % Modified for batch operation. Restrictions to output filename and file selection. % Requires a subdirectory: /home/operator/matlab/batch_PID % % Computes the volume-weighted fraction of the different PID classifications. % Does this on a radar scan volume-by-volume basis. Only SUR scans should be % used for this operation. % % This routine computes a pseudo volume for every radar gate, using the depth of the gate % and the beamwidth (assumed to be 1-deg), using a rectangular approximation. In this scheme, % gates near the radar carry very little weight, while those at extreme range are much larger, % and a given PID is weighted over that larger volume. % % All work is done in radar space. Note that it was not felt that gridding of PID would be a % reliable process, and that the calculations presented here are more physically correct. % % In a practical sense, you require a very complete volume scan (good elevation spacing to a % high tilt angle) to adequately cover a volume. You should ignore scans with only a few tilts. % There will always be a "cone of silence" over the radar. % % The S-Pol test pulse is explicitly removed, as well as data above the troposphere. % % Note that your largest particle fraction will almost always be for the type "unclassified." % % This routine runs using S-Pol cfradial format data. The data are under the "partrain" path. % It requires about 3 wallclock seconds to process each radar volume (6 tilts, 360-beams per tilt). % Output is to both the matlab control window (detailed diagnostics), and a summary output to a % data file. The data file should be hand-edited to remove bad volumes (criteria: transmitter off; % multiple NaN values; too few tilts). % % A companion routine (PID_read_plot) is used to plot a single bar graph summary for the data file. % % Written in the field during DYNAMO, R. Rilling NCAR/EOL 9/2011 % % Particle types are: % 1 CL Cloud particles % 2 Drz Drizzle % 3 Lr Light rain % 4 Mr Moderate rain % 5 Hr Heavy rain % 6 Ha Hail % 7 Rh Rain/hail mix % 8 Gsh Graupel/small hail % 9 GRR Graupel/rain mix % 10 Ds Dry snow % 11 Ws Wet snow % 12 Ic Oriented ice crystals (horizontal) % 13 Iic Irregular ice crystals % 14 Sld Super-coolded liquid water drops (cloud drop sized) % 15 Bgs Insects % 16 Brd 2nd trip % 17 Gcl Ground clutter % 18 Unc not-a-number (unclassified, for this code, only) % 19 [ ] not labeled. These gates are test pulse, or above the tropopause and are not counted. % % Note that many PID gates are flagged with a missing_value, indicating low SNR or other threshold criteria. % These will be reclassified to category 18. (Note: I have never found any values of 18 in the original % PID data set) direc = '/scr/pgen2/cfradial/partrain/sband/sur'; set(0,'DefaultTextInterpreter','none'); global rt_direc; rt_direc = '/scr/pgen2/cfradial'; ptype = {'Cl' 'Drz' 'Lr' 'Mr' 'Hr' 'Ha' 'Rh' 'Gsh' 'GRR' 'Ds' 'Ws' 'Ic' 'Iic' 'Sld' 'Bgs' '2nd' 'Gcl' 'Unc'}; gatewidth = .150; % assumed constant for all DYNAMO trop_height = 17.0; % approximated troposphere height % set a beamwidth weighting function; assume a 1-deg beam, h and v. This is an approximation. sinsq = sin(1.0 * pi/180); sinsq = sinsq * sinsq * gatewidth; % beamwidth by beamwidth by voxel (or gate) depth % open an output file. ofile = ['/home/operator/matlab/batch_PID/PID_summary.' myDate '.out' ]; fout = fopen(ofile,'w'); fprintf(fout,'%% Volume Start '); for ii=1:numel(ptype); fprintf(fout,'%5s ',char(ptype{ii})); end; fprintf(fout,'\n'); %f_name = select_files(direc, '/cfrad*.nc'); f_name = get_sorted_file_list(myDate,'partrain','sband',{'sur'}); % make the assumption that Attributes will not change over the next % few volumes (this is a serious assumption!) [ ATT GATT ] = get_hdf5_param_atts( char(f_name(1,:)) ); Flds = determine_radar_fields( ATT ); %sFlds = select_params(Flds); sFlds = {'PID'}; klim = size(f_name,1); for kk=1:klim; SWP = get_and_scale_hdf5_data(char(deblank(f_name(kk,:))), ATT, [sFlds { ... 'time_coverage_end' 'time_coverage_start' 'time' 'range' ... 'sweep_number' 'fixed_angle' 'azimuth' 'elevation' 'antenna_transition'}]); % need a sanity check to ensure that there are multiple elevations, and that the % fixed angle increases monotonically. We don't want volumes with multiples % of the same tilt. % we don't much care about azimuth and elevation, but we want to make sure % that we do not include transition beams. Filter out the transition beams. nbeams = size(SWP.time,1); ngates = size(SWP.range,1); tx_ers = find(SWP.antenna_transition == 1); blim = nbeams - size(tx_ers,1); % initialize an empty array to hold non-transition beam PID. pid is smaller than SWP.PID pid = []; pid(1:size(SWP.PID,1),1:blim) = NaN; % protect against bad range vector size % compute range and beamwidth weighting factors; rwgt is invariant throughout % the scan volume rwgt = ((SWP.range/1000).^2) * sinsq; % sinsq includes the gate depth % there is a glitch in some of the cfrad volumes. Sometimes, the total number of gates % for a beam is greater than the size of the range vector. Kludge a fix for this. % (Mike Dixon has this problem on his "to do" list, 20110927) % This kludge will still not catch all of the problems. The size of SPW.PID can be in error. if( size(SWP.PID,1) > ngates); rwgt(ngates+1:size(SWP.PID,1)) = 0; end; % eliminate transition beams or neg elevation, and clip test pulse and data above tropopause jj = 1; for nn=1:nbeams; if( SWP.antenna_transition(nn) == 1 || SWP.elevation(nn) < 0.0 ) % skip the transition beam continue; end; pid(:,jj) = SWP.PID(:,nn); % chop out the test pulse (based upon known gate numbers) pid((ngates-12):ngates,jj) = 19; % set value to edited-out classification % eliminate data above the tropopause (assumed about 17 km) rmax = trop_height * 1000 / (sin(SWP.elevation(nn) * pi/180)); mgate = round((rmax/150 + .499)); mgate = min([mgate ngates]); pid(mgate:ngates,jj) = 19; % ngates is a test pulse gate, anyhow. jj = jj + 1; end; pid = round(pid); % because of data scaling, SWP.PID was not necessarily an integer % a bit dangerous, but works for this code: reset NaNs to classification 18 a = find(isnan(pid)); pid(a) = 18; pbeams = size(pid,2); tgates = 0; for jj=1:18; ptmp = reshape(pid,numel(pid),1); A = find(ptmp ~= (jj)); B = find(ptmp == (jj)); ptmp(A) = NaN; ptmp = reshape(ptmp,ngates,pbeams); for ii=1:pbeams; ptmp(:,ii) = (~isnan(ptmp(:,ii))) .* rwgt(:); % range weight according to incidence of occurance end; totgate(jj) = numel(B); tot(jj) = sum(nansum(ptmp)); end; volsize = sum(tot); % fprintf('\nStart time %s End Time %s\n\n', ... % deblank([SWP.time_coverage_start{:}]),deblank([SWP.time_coverage_end{:}])); for jj=1:18; percnt(jj) = 100 * (tot(jj)/volsize); % fprintf('%3s, gate_tot = %10d voltot = %10.0f percent = %5.2f\n', ... % ptype{jj}, totgate(jj), tot(jj), percnt(jj)); end; % fprintf('\nrelative volume of all used gates: %10.0f (proportional to cubic km)\n', volsize); % fprintf('number of PID gates used %d (of a possible total of %d)\n\n',... % sum(totgate), numel(ptmp)); vals = sprintf('%6.2f',percnt); angs = sprintf('%5.1f',SWP.fixed_angle); fprintf(fout, '%s %s angs:%s vol:%8.0f\n',deblank([SWP.time_coverage_end{:}]),vals,angs,volsize); end; fclose( fout ); % pseudo test for batch operation, no prompt, no command window % include these lines in all scripts designed to be run from the UNIX prompt interactive_tst = get(0,'ScreenDepth'); if(numel(interactive_tst) == 1 && interactive_tst(1) == 0 ); % fprintf('Found that ScreenDepth is a good test for nodisplay command line option.\n'); exit; end; % if we are running in batch, we mostly set the display to something other than :0 displaynum = getenv('DISPLAY'); if(~strcmp(displaynum,':0')) exit; end;