Due to a shift in policy, from 0900 GMT on Wednesday 14th July 2021, we will be disabling ssh access to the server for external users. External users who wish to continue to access code repositories on the server will need to switch to using https. This can be accomplished in the following way: 1) On the repo on gitlab, use the clone dialogue and select ‘Clone with HTTPS’ to get the address of the repo; 2) From within the checkout of your repo run: $ git remote set-url origin HTTPS_ADDRESS. Here, replace HTTPS_ADDRESS with the address you have just copied from GitLab. Pulls and pushes will now require you to enter a username and password rather than using a ssh key. If you would prefer not to enter a password each time, you might consider caching your login credentials.

get_CFS_forcing.m 23.2 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
function data = get_CFS_forcing(Mobj, modelTime, varargin)
% Get the required parameters from CFSv2 products to force FVCOM.
%
% data = get_CFS_forcing(Mobj, modelTime)
%
% DESCRIPTION:
%   Using OPeNDAP, extract the necessary parameters to create an FVCOM
%   forcing file. Requires the air_sea toolbox.
%
% INPUT:
%   Mobj - MATLAB mesh object. Must contain fields:
%       lon, lat    - array of longitude and latitudes.
%       have_lonlat - boolean to signify whether coordinates are spherical
%                   or cartesian.
%   modelTime - Modified Julian Date start and end times
%   varargin - parameter/value pairs
%       - list of variables to extract:
%           'varlist', {'nshf', 'uwnd', 'vwnd'}
%
% OUTPUT:
%   data - struct of the data necessary to force FVCOM. These can be
%   interpolated onto an unstructured grid in Mobj using grid2fvcom.m.
%
% The parameters which can be obtained from the NCEP data are:
%     - u wind component (uwnd)
%     - v wind component (vwnd)
%     - Downward longwave radiation surface (dlwrf)
%     - Net shortwave radiation surface (nswrs = uswrf - dswrf)
%     - Air temperature (air)
%     - Relative humidity (rhum)
%     - Precipitation rate (prate)
%     - Surface pressure (pres or press)
%     - Latent heat flux (lhtfl)
%     - Potential evaporation rate (pevpr)
%
% In addition to these, the momentum flux (tau) is calculated from wind
% data. Precipitation is converted from kg/m^2/s to m/s. Evaporation (Et)
% is calculated from the mean daily latent heat net flux (lhtfl) at the
% surface. Precipitation-evaporation is also created (P_E).
%
% EXAMPLE USAGE:
%   To download the default set of data (see list above):
%
%       forcing = get_CFS_forcing(Mobj, [51345, 51376]);
%
%   To only download wind data:
%
%       forcing = get_CFS_forcing(Mobj, [51345, 51376], 'varlist', {'uwnd', 'vwnd'});
%
% Author(s)
%   Pierre Cazenave (Plymouth Marine Laboratory)
%   Ricardo Torres (Plymouth Marine Laboratory)
%   Rory O'Hara Murray (Marine Scotland Science)
%
% Revision history:
%   2015-05-19 First version based on get_NCEP_forcing.m.
%
%==========================================================================

subname = 'get_CFS_forcing';

global ftbverbose;
if ftbverbose
    fprintf('\nbegin : %s\n', subname)
end

% Parse the input arguments
varlist = [];
if nargin > 2
    for a = 1:2:nargin - 2
        switch varargin{a}
            case 'varlist'
                varlist = varargin{a + 1};
        end
    end
end
if ftbverbose
    fprintf('Extracting CFSv2 data.\n')
end

% Get the extent of the model domain (in spherical)
if ~Mobj.have_lonlat
    error('Need spherical coordinates to extract the forcing data')
else
    % Add a buffer of one grid cell in latitude and two in longitude to
    % make sure the model domain is fully covered by the extracted data.
    [dx, dy] = deal(0.5, 0.5); % approximate CFSv2 resolution in degrees
    extents = [min(Mobj.lon(:)) - (2 * dx), ...
        max(Mobj.lon(:)) + (2 * dx), ...
        min(Mobj.lat(:)) - dy, ...
        max(Mobj.lat(:)) + dy];
end

% if modelTime(end) - modelTime(1) > 365
%     error('Can''t (yet) process more than a year at a time.')
% end
[yyyy, mm, dd, HH, MM, SS] = mjulian2greg(modelTime);
dates = datenum([yyyy; mm; dd; HH; MM; SS]');
serial = dates(1):dates(2);
[years, months, ~, ~, ~, ~] = datevec(serial);
101 102 103 104 105 106 107
[months, idx] = unique(months, 'stable');
years = years(idx);
nt = length(months);

    for t = 1:nt
        month = months(t);
        year = years(y);
108 109 110 111 112
        % Set up a struct of the remote locations in which we're
        % interested.

        url = 'http://nomads.ncdc.noaa.gov/thredds/dodsC/cfsr1hr/';
        % Get the forcing data.
113 114 115
        ncep.dlwsfc  = [url, sprintf('%04d%02d/dlwsfc.gdas.%04d%02d.grb2', year, month, year, month)];
        ncep.dswsfc  = [url, sprintf('%04d%02d/dswsfc.gdas.%04d%02d.grb2', year, month, year, month)];
        ncep.lhtfl   = [url, sprintf('%04d%02d/lhtfl.gdas.%04d%02d.grb2', year, month, year, month)];
116 117
        ncep.prate   = [url, sprintf('%04d%02d/prate.gdas.%04d%02d.grb2', year, month, year, month)];
        ncep.pressfc = [url, sprintf('%04d%02d/pressfc.gdas.%04d%02d.grb2', year, month, year, month)];
118 119
        ncep.q2m     = [url, sprintf('%04d%02d/q2m.gdas.%04d%02d.grb2', year, month, year, month)];
        ncep.tmp2m   = [url, sprintf('%04d%02d/tmp2m.gdas.%04d%02d.grb2', year, month, year, month)];
120
        ncep.uswsfc  = [url, sprintf('%04d%02d/uswsfc.gdas.%04d%02d.grb2', year, month, year, month)];
121 122 123 124 125 126 127 128 129
        ncep.uwnd     = [url, sprintf('%04d%02d/wnd10m.gdas.%04d%02d.grb2', year, month, year, month)];
        ncep.vwnd     = [url, sprintf('%04d%02d/wnd10m.gdas.%04d%02d.grb2', year, month, year, month)];

        names.dlwsfc = 'Downward_Long-Wave_Rad_Flux';
        names.dswsfc = 'Downward_Short-Wave_Rad_Flux';
        names.lhtfl = 'Latent_heat_net_flux';
        names.prate = 'Precipitation_rate';
        names.pressfc = 'Pressure';
        names.q2m = 'Specific_humidity';
130
        names.tmp2m = 'Temperature';
131
        names.uswsfc = 'Upward_Short-Wave_Rad_Flux';
132
        names.uwnd = 'U-component_of_wind';
133
        names.vwnd = 'V-component_of_wind';
134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161
        
        fields = fieldnames(ncep);

        for aa = 1:length(fields)        
            % We've been given a list of variables to do, so skip those that
            % aren't in the list.
            if ~isempty(varlist) && max(strcmp(fields{aa}, varlist)) ~= 1
                continue
            end

            if ftbverbose
                fprintf('getting ''%s'' data... ', fields{aa})
            end

            data.(fields{aa}).data = [];
            data.(fields{aa}).time = [];
            data.(fields{aa}).lat = [];
            data.(fields{aa}).lon = [];

            %ncid_info = ncinfo(ncep.(fields{aa}));
            ncid = netcdf.open(ncep.(fields{aa}));

            % If you don't know what it contains, start by using the
            % 'netcdf.inq' operation:
            %[numdims,numvars,numglobalatts,unlimdimid] = netcdf.inq(ncid);
            % Time is in hours since the start of the month. We want
            % sensible times, so we'll have to offset at some point.
            varid = netcdf.inqVarID(ncid, 'time');
162 163 164 165 166 167 168 169
            data_time = netcdf.getVar(ncid, varid, 'double');
            if max(data_time) == 6
                % Precipitation data has times as 0-6 repeated for n days.
                % We need a sensible set of hours since the start of the
                % month for subsequent subsampling in time. Make the
                % sensible time array here.
                data_time = 0:length(data_time) - 1;
            end
170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195

            varid = netcdf.inqVarID(ncid,'lon');
            data_lon.lon = netcdf.getVar(ncid,varid,'double');
            varid = netcdf.inqVarID(ncid,'lat');
            data_lat.lat = netcdf.getVar(ncid,varid,'double');
            % Some of the NCEP Reanalysis 2 data are 4D, but with a single
            % vertical level (e.g. uwnd, vwnd, air, rhum).
            data_level_idx = [];
            try % not all data have a 'level', so fail gracefully here.
                varid = netcdf.inqVarID(ncid, 'level');
                data_level.level = netcdf.getVar(ncid, varid, 'double');
                if length(data_level.level) > 1
                    % Assume we've got rhum and we want humidity at the sea
                    % surface (1013 millibars (or hPa)). As such, ZQQ must be
                    % 0.0 in the FVCOM model namelist. Find the closest level
                    % to pressure at 1 standard atmosphere.
                    [~, data_level_idx] = min(abs(data_level.level - 1013));
                end
            catch
                true;
            end
            if isempty(data_level_idx) % default to the first
                data_level_idx = 1;
            end

            % Time is in hours relative to the start of the month.
196
            timevec = datevec((data_time / 24) + datenum(year, month, 1, 0, 0, 0));
197 198 199 200 201 202 203 204 205 206 207 208 209

            % Get the data time and convert to Modified Julian Day.
            data.time = greg2mjulian(...
                timevec(:, 1), ...
                timevec(:, 2), ...
                timevec(:, 3), ...
                timevec(:, 4), ...
                timevec(:, 5), ...
                timevec(:, 6));
            % Clip the time to the given range
            data_time_mask = data.time >= modelTime(1) & data.time <= modelTime(end);
            data_time_idx = 1:size(data.time, 1);
            data_time_idx = data_time_idx(data_time_mask);
210 211 212 213 214 215 216 217 218 219 220 221
            if ~isempty(data_time_idx) % for the prate data mainly
                data.time = data.time(data_time_mask);
            else
                % Reset the index to its original size. This is for data
                % with only a single time stamp which falls outside the
                % model time (as is the case with the precipitation data,
                % for some reason). Only reset it when the length of the
                % input time is equal to 1.
                if length(data.time) == 1
                    data_time_idx = 1:size(data.time, 1);
                end
            end
222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265

            % Check the times
            %[yyyy,mm,dd,hh,MM,ss] = mjulian2greg(data.time(1))
            %[yyyy,mm,dd,hh,MM,ss] = mjulian2greg(data.time(end))
            % Get the data in two goes, once for the end of the grid (west of
            % Greenwich), once for the beginning (east of Greenwich), and then
            % stick the two bits together.
            clear index_lon index_lat
            if extents(1) < 0 && extents(2) < 0
                % This is OK, we can just shunt the values by 360.
                extents(1) = extents(1) + 360;
                extents(2) = extents(2) + 360;
                index_lon = find(data_lon.lon > extents(1) & data_lon.lon < extents(2));
            elseif extents(1) < 0 && extents(2) > 0
                % This is the tricky one. We'll do two passes to extract the
                % western chunk first (extents(1)+360 to 360), then the eastern
                % chunk (0-extents(2)).
                index_lon{1} = find(data_lon.lon >= extents(1) + 360);
                index_lon{2} = find(data_lon.lon <= extents(2));
            else
                % Dead easy, we're in the eastern hemisphere, so nothing too
                % strenuous here.
                index_lon = find(data_lon.lon > extents(1) & data_lon.lon < extents(2));
            end

            % Latitude is much more straightforward
            index_lat = find(data_lat.lat > extents(3) & data_lat.lat < extents(4));
            data.(fields{aa}).lat = data_lat.lat(index_lat);

            % Get the data
            if iscell(index_lon)
                data.(fields{aa}).lon = data_lon.lon(cat(1,index_lon{:}));

                varid = netcdf.inqVarID(ncid, names.(fields{aa}));

                [~, ~, dimids, ~] = netcdf.inqVar(ncid,varid);
                if length(dimids) == 4
                    start = [min(index_lon{1}), min(index_lat), data_level_idx, min(data_time_idx)] - 1;
                    count = [length(index_lon{1}), length(index_lat), length(data_level_idx), length(data_time_idx)];
                elseif length(dimids) == 3
                    start = [min(index_lon{1}), min(index_lat), min(data_time_idx)] - 1;
                    count = [length(index_lon{1}), length(index_lat), length(data_time_idx)];
                end

266
                data_west.(fields{aa}).(fields{aa}) = netcdf.getVar(ncid, varid, start, count, 'double');
267 268 269 270 271 272 273 274

                if length(dimids) == 4
                    start = [min(index_lon{2}), min(index_lat), data_level_idx, min(data_time_idx)] - 1;
                    count = [length(index_lon{2}), length(index_lat), length(data_level_idx), length(data_time_idx)];
                elseif length(dimids) == 3
                    start = [min(index_lon{2}), min(index_lat), min(data_time_idx)] - 1;
                    count = [length(index_lon{2}), length(index_lat), length(data_time_idx)];
                end
275
                data_east.(fields{aa}).(fields{aa}) = netcdf.getVar(ncid, varid, start, count, 'double');
276

277 278
                scratch.(fields{aa}).(fields{aa}).(fields{aa}) = ...
                    cat(1, data_west.(fields{aa}).(fields{aa}), data_east.(fields{aa}).(fields{aa}));
279 280

                % Merge the two sets of data together
281
                structfields = fieldnames(data_west.(fields{aa}));
282 283 284 285 286 287 288
                for ii = 1:length(structfields)
                    switch structfields{ii}
                        case 'lon'
                            % Only the longitude and the actual data need
                            % sticking together, but each must be done
                            % along a different axis (lon is a vector, the
                            % data is an array).
289 290
                            scratch.(fields{aa}).(structfields{ii}) = ...
                                [data_west.(fields{aa}).(structfields{ii}); data_east.(fields{aa}).(structfields{ii})];
291
                        case fields{aa}
292 293 294
                            % This is the actual data.
                            scratch.(fields{aa}).(structfields{ii}) = ...
                                [rot90(data_west.(fields{aa}).(structfields{ii})), rot90(data_east.(fields{aa}).(structfields{ii}))];
295 296 297 298 299 300 301 302 303 304
                        otherwise
                            % Assume the data are the same in both arrays.
                            % A simple check of the range of values in the
                            % difference between the two arrays should show
                            % whether they're the same or not. If they are,
                            % use the western values, otherwise, warn about
                            % the differences. It might be the data are
                            % relatively unimportant anyway (i.e. not used
                            % later on).
                            try
305
                                tdata = data_west.(fields{aa}).(structfields{ii}) - data_east.(fields{aa}).(structfields{ii});
306 307
                                if range(tdata(:)) == 0
                                    % They're the same data
308 309
                                    scratch.(fields{aa}).(structfields{ii}) = ...
                                        data_west.(fields{aa}).(structfields{ii});
310 311 312 313 314 315
                                else
                                    warning('Unexpected data field and the west and east halves don''t match. Skipping.')
                                end
                            catch
                                warning('Unexpected data field and the west and east halves don''t match. Skipping.')
                            end
316
                            clearvars tdata
317 318
                    end
                end
319
                clearvars data_west data_east
320 321 322 323
            else
                % We have a straightforward data extraction
                data.(fields{aa}).lon = data_lon.lon(index_lon);

324
                varid = netcdf.inqVarID(ncid, (fields{aa}));
325
                % [varname,xtype,dimids,natts] = netcdf.inqVar(ncid,varid);
326 327 328 329 330 331 332 333 334
                % [~, length1] = netcdf.inqDim(ncid, dimids(1))
                % [~, length2] = netcdf.inqDim(ncid, dimids(2))
                % [~, length3] = netcdf.inqDim(ncid, dimids(3))
                start = [min(index_lon), min(index_lat), min(data_time_idx)] - 1;
                count = [length(index_lon), length(index_lat), length(data_time_idx)];
                % The air data (NCEP version of this script) was failing
                % with a three long start and count array, so try first
                % without (to retain original behaviour for other
                % potentially unaffected variables) but fall back to
335 336
                % getting only the first level (start = 0, count = 1).
                try
337
                    scratch.(fields{aa}).(fields{aa}).(fields{aa}) = netcdf.getVar(ncid,varid,start,count,'double');
338
                catch
339 340 341
                    start = [min(index_lon), min(index_lat), 1, min(data_time_idx)] - 1;
                    count = [length(index_lon), length(index_lat), 1, length(data_time_idx)];
                    scratch.(fields{aa}).(fields{aa}) = netcdf.getVar(ncid, varid, start, count, 'double');
342 343 344
                end

            end
345
            clearvars data_time*
346

347
            datatmp = squeeze(scratch.(fields{aa}).(fields{aa}));
348 349 350 351 352

            % Fix the longitude ranges for all data.
            data.(fields{aa}).lon(data.(fields{aa}).lon > 180) = ...
                data.(fields{aa}).lon(data.(fields{aa}).lon > 180) - 360;

353 354 355 356 357 358 359
            % data.(fields{aa}).data = datatmp;
            data.(fields{aa}).data = cat(3, data.(fields{aa}).data, datatmp);
            % data.(fields{aa}).time = data.time;
            data.(fields{aa}).time = cat(1, data.(fields{aa}).time, data.time);
            % data.(fields{aa}).time = cat(1, data.(fields{aa}).time, squeeze(scratch.(fields{aa}).(fields{aa}).time));
            % data.(fields{aa}).lat = squeeze(scratch.(fields{aa}).(fields{aa}).lat);
            % data.(fields{aa}).lon = squeeze(scratch.(fields{aa}).(fields{aa}).lon);
360 361 362 363 364 365 366 367 368 369 370

            if ftbverbose
                if isfield(data, fields{aa})
                    fprintf('done.\n')
                else
                    fprintf('error!\n')
                end
            end
        end

        % Calculate the net long and shortwave radiation fluxes.
371 372 373 374 375
        if isfield(data, 'uswsfc') && isfield(data, 'dswsfc')
            data.nswsfc.data = data.uswsfc.data - data.dswsfc.data;
            data.nswsfc.time = data.uswsfc.time;
            data.nswsfc.lon = data.uswsfc.lon;
            data.nswsfc.lat = data.uswsfc.lat;
376 377 378 379 380 381 382 383
        end

        % Now we have some data, we need to create some additional parameters
        % required by FVCOM.

        % Convert precipitation from kg/m^2/s to m/s (required by FVCOM) by
        % dividing by freshwater density (kg/m^3).
        if isfield(data, 'prate')
384
            data.prate.data = data.prate.data / 1000;
385 386 387 388 389 390 391 392 393 394 395 396
        end

        % Evaporation can be approximated by:
        %
        %   E(m/s) = lhtfl/Llv/rho
        %
        % where:
        %
        %   lhtfl   = "Mean daily latent heat net flux at the surface"
        %   Llv     = Latent heat of vaporization (approx to 2.5*10^6 J kg^-1)
        %   rho     = 1025 kg/m^3
        %
397 398
        if isfield(data, 'prate') && isfield(data, 'lhtfl')
            Llv = 2.5 * 10^6;
399
            rho = 1025; % using a typical value for seawater.
400
            Et = data.lhtfl.data / Llv / rho;
401
            data.P_E.data = data.prate.data - Et;
402 403 404
            % Evaporation and precipitation need to have the same sign for
            % FVCOM (ocean losing water is negative in both instances). So,
            % flip the evaporation here.
405 406 407
            data.Et.data = -Et;
        end

408 409 410
        % Get the fields we need for the subsequent interpolation. Find the
        % position of a sensibly sized array (i.e. not 'topo', 'rhum' or
        % 'pres').
411 412 413 414 415 416
        for vv = 1:length(fields)
            if ~isempty(varlist) && max(strcmp(fields{vv}, varlist)) ~= 1
                continue
            end

            switch fields{vv}
417 418
                % Set ii in each instance in case we've been told to only
                % use one of the three alternatively gridded data.
419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438
                case 'topo'
                    ii = vv;
                    continue
                case 'rhum'
                    ii = vv;
                    continue
                case {'pres', 'press'}
                    ii = vv;
                    continue
                otherwise
                    % We've got one, so stop looking.
                    ii = vv;
                    break
            end
        end
        data.lon = data.(fields{ii}).lon;
        data.lon(data.lon > 180) = data.lon(data.lon > 180) - 360;
        data.lat = data.(fields{ii}).lat;

        % Convert temperature to degrees Celsius (from Kelvin)
439 440
        if isfield(data, 'tmp2m')
            data.tmp2m.data = data.tmp2m.data - 273.15;
441 442
        end

443 444
        % Make sure all the data we have downloaded are the same shape as
        % the longitude and latitude arrays.
445 446 447 448 449 450 451 452 453 454 455 456 457 458 459
        for aa = 1:length(fields)
            if ~isempty(varlist) && max(strcmp(fields{aa}, varlist)) ~= 1
                % We've been given a list of variables to extract, so skip those
                % that aren't in that list
                continue
            else
                if isfield(data, fields{aa})
                    [px, py] = deal(length(data.(fields{aa}).lon), length(data.(fields{aa}).lat));
                    [ncx, ncy, ~] = size(data.(fields{aa}).data);
                    if ncx ~= px || ncy ~= py
                        data.(fields{aa}).data = permute(data.(fields{aa}).data, [2, 1, 3]);

                        % Check everything's OK now.
                        [ncx, ncy, ~] = size(data.(fields{aa}).data);
                        if ncx ~= px || ncy ~= py
460
                            error('Unable to resize data arrays to match position data orientation. Are these on a different horizontal grid?')
461 462
                        else
                            if ftbverbose
463
                                fprintf('Matching %s data dimensions and position arrays\n', fields{aa})
464 465 466 467
                            end
                        end
                    end
                else
468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505
                    warning('Variable %s requested but not downloaded.', fields{aa})
                end
            end
        end
    end
    % Concatenate each year's worth of data.
    for aa = 1:length(fields)
        if exist('forcing', 'var') && isfield(forcing, (fields{aa}))
            forcing.(fields{aa}).data = cat(3, forcing.(fields{aa}).data, data.(fields{aa}).data);
            forcing.(fields{aa}).time = cat(1, forcing.(fields{aa}).time, data.(fields{aa}).time);
            forcing.(fields{aa}).lon = data.(fields{aa}).lon;
            forcing.(fields{aa}).lat = data.(fields{aa}).lat;
        else
            forcing = data;
        end
    end
end

% Now we have the data, we need to fix the averaging to be hourly instead
% of n-hourly, where n varies from 0 to 6. See
% http://rda.ucar.edu/datasets/ds094.1/#docs/FAQs_hrly_timeseries.html with
% the question "How can the individual one-hour averages be computed?".
fields = fieldnames(data);
for f = 1:length(fields)
    if isfield(data.(fields{f}), 'data')
        [~, ~, nt] = size(data.(fields{f}).data);
        fixed = data.(fields{f}).data;

        for t = 1:6:nt
            % Fix the next 5 hours of data. Assume 0th hour is just the
            % original data - since the formula multiplies by the n-1 hour,
            % if we want the first hour's worth of data, then the second
            % term in the formula with multiply by zero, so the formula is
            % essentially only using the first term, which is just the data
            % at n (i.e. 0).
            for n = 1:5
                if t + n <= nt
                    fixed(:, :, t + n) = (n * data(:, :, t + n)) - ((n - 1) * data(:, :, t + n - 1));
506 507 508
                end
            end
        end
509 510
        data.(fields{f}).data = fixed;
        clearvars fixed
511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529
    end
end

% Have a look at some data.
% [X, Y] = meshgrid(data.lon, data.lat);
% for i=1:size(data.uwnd.data, 3)
%     figure(1)
%     clf
%     uv = sqrt(data.uwnd.data(:, :, i).^2 + data.vwnd.data(:, :, i).^2);
%     pcolor(X, Y, uv')
%     shading flat
%     axis('equal','tight')
%     pause(0.1)
% end

if ftbverbose
    fprintf('end   : %s\n', subname)
end