Use an FVCOM restart file to seed a model run with spatially varying
versions of otherwise constant variables.
function interp_NEMO2FVCOM(Mobj, nemo, start_date, varlist)
DESCRIPTION:
FVCOM does not yet support spatially varying initial conditions. To
avoid having to run a model for a long time in order for temperature
and salinity to settle within the model from the atmospheric and
boundary forcing, we can use a restart file to cheat. For this, we
need variables interpolated onto the unstructured grid. The
interpolated data can then be written out with write_FVCOM_restart.m.
INPUT:
Mobj = MATLAB mesh structure which must contain:
- Mobj.lon, Mobj.lat and/or Mobj.lonc, Mobj.latc - node
or element centre coordinates (long/lat). Dependent on
the variable being interpolated (u and v need lonc,
latc whilst temperature and salinity need lat and lon).
- Mobj.siglayz - sigma layer depths for all model
nodes.
- Mobj.ts_times - Modified Julian Day times for the
model run.
nemo = Struct output from some other function. Must include
fields:
- nemo.lon, nemo.lat - rectangular arrays.
- nemo.Depth - NEMO depth levels.
start_date = Gregorian start date array (YYYY, MM, DD, hh, mm, ss).
varlist = cell array of fields to use from the NEMO struct.
OUTPUT:
Mobj.restart = struct whose field names are the variables which have
been interpolated (e.g. Mobj.restart.temperature for NEMO
temperature).
EXAMPLE USAGE
interp_NEMO2FVCOM(Mobj, nemo, [2006, 01, 01, 00, 00, 00], ...
{'lon', 'lat', 'time', 'temperature', 'salinity'})
Author(s):
Ricardo Torres (Plymouth Marine Laboratory)
Pierre Cazenave (Plymouth Marine Laboratory)
Revision history
2018-05-23 First version based on interp_HYCOMS2FVCOM.m.
==========================================================================0001 function Mobj = interp_NEMO2FVCOM(Mobj, nemo, start_date, varlist) 0002 % Use an FVCOM restart file to seed a model run with spatially varying 0003 % versions of otherwise constant variables. 0004 % 0005 % function interp_NEMO2FVCOM(Mobj, nemo, start_date, varlist) 0006 % 0007 % DESCRIPTION: 0008 % FVCOM does not yet support spatially varying initial conditions. To 0009 % avoid having to run a model for a long time in order for temperature 0010 % and salinity to settle within the model from the atmospheric and 0011 % boundary forcing, we can use a restart file to cheat. For this, we 0012 % need variables interpolated onto the unstructured grid. The 0013 % interpolated data can then be written out with write_FVCOM_restart.m. 0014 % 0015 % INPUT: 0016 % Mobj = MATLAB mesh structure which must contain: 0017 % - Mobj.lon, Mobj.lat and/or Mobj.lonc, Mobj.latc - node 0018 % or element centre coordinates (long/lat). Dependent on 0019 % the variable being interpolated (u and v need lonc, 0020 % latc whilst temperature and salinity need lat and lon). 0021 % - Mobj.siglayz - sigma layer depths for all model 0022 % nodes. 0023 % - Mobj.ts_times - Modified Julian Day times for the 0024 % model run. 0025 % nemo = Struct output from some other function. Must include 0026 % fields: 0027 % - nemo.lon, nemo.lat - rectangular arrays. 0028 % - nemo.Depth - NEMO depth levels. 0029 % start_date = Gregorian start date array (YYYY, MM, DD, hh, mm, ss). 0030 % varlist = cell array of fields to use from the NEMO struct. 0031 % 0032 % OUTPUT: 0033 % Mobj.restart = struct whose field names are the variables which have 0034 % been interpolated (e.g. Mobj.restart.temperature for NEMO 0035 % temperature). 0036 % 0037 % EXAMPLE USAGE 0038 % interp_NEMO2FVCOM(Mobj, nemo, [2006, 01, 01, 00, 00, 00], ... 0039 % {'lon', 'lat', 'time', 'temperature', 'salinity'}) 0040 % 0041 % Author(s): 0042 % Ricardo Torres (Plymouth Marine Laboratory) 0043 % Pierre Cazenave (Plymouth Marine Laboratory) 0044 % 0045 % Revision history 0046 % 2018-05-23 First version based on interp_HYCOMS2FVCOM.m. 0047 % 0048 %========================================================================== 0049 0050 subname = 'interp_NEMO2FVCOM'; 0051 0052 global ftbverbose 0053 if ftbverbose 0054 fprintf('\nbegin : %s\n', subname) 0055 end 0056 0057 if nargin == 0 0058 error('Not enough input arguments. See HELP %s', subname) 0059 end 0060 0061 % Run jobs on multiple workers if we have that functionality. Not sure if 0062 % it's necessary, but check we have the Parallel Toolbox first. 0063 wasOpened = false; 0064 if license('test', 'Distrib_Computing_Toolbox') 0065 % We have the Parallel Computing Toolbox, so launch a bunch of workers. 0066 % New version for MATLAB 2014a (I think) onwards. 0067 if isempty(gcp('nocreate')) 0068 pool = parpool('local'); 0069 wasOpened = true; 0070 end 0071 end 0072 0073 % Given our input time (in start_date), find the nearest time index for 0074 % the NEMO data. 0075 stime = greg2mjulian(start_date(1), start_date(2), ... 0076 start_date(3), start_date(4), ... 0077 start_date(5), start_date(6)); 0078 [~, tidx] = min(abs(nemo.time - stime)); 0079 0080 for vv = 1:length(varlist) 0081 0082 currvar = varlist{vv}; 0083 0084 switch currvar 0085 case {'lon', 'lat', 'longitude', 'latitude', 't_1', 'time', 'Depth', 'MT'} 0086 continue 0087 0088 otherwise 0089 %-------------------------------------------------------------- 0090 % Interpolate the regularly gridded data onto the FVCOM grid 0091 % (vertical grid first). 0092 %-------------------------------------------------------------- 0093 0094 tic 0095 0096 % Set up all the constants which are based on the model and 0097 % data grids. 0098 [~, fz] = size(Mobj.siglayz); 0099 if strcmpi(currvar, 'u') || strcmpi(currvar, 'v') 0100 plon = nemo.lon(:); 0101 plat = nemo.lat(:); 0102 flon = Mobj.lonc; 0103 flat = Mobj.latc; 0104 fn = numel(flon); 0105 fvtemp = nan(fn, fz); 0106 else 0107 plon = nemo.lon(:); 0108 plat = nemo.lat(:); 0109 flon = Mobj.lon; 0110 flat = Mobj.lat; 0111 fn = numel(flon); 0112 fvtemp = nan(fn, fz); 0113 end 0114 0115 if ftbverbose 0116 fprintf('%s : interpolate %s ... ', subname, currvar) 0117 end 0118 0119 % Calculate resolution of parent coarse model 0120 hdx = max([max(diff(plon(:, 1))), max(diff(plat(1, :)))]); 0121 0122 xlimits = [min(flon) - 3 * hdx, max(flon) + 3 * hdx]; 0123 ylimits = [min(flat) - 3 * hdx, max(flat) + 3 * hdx]; 0124 xidxgood = find(nemo.lon<xlimits(2) & nemo.lon>xlimits(1) & nemo.lat<ylimits(2) & nemo.lat>ylimits(1)); 0125 0126 hdepth = nemo.Depth; 0127 0128 % Do not interpolate 2D variables in depth. 0129 if ~ismatrix(nemo.(currvar)) 0130 hyinterp.(currvar) = grid_vert_interp(Mobj, ... 0131 nemo.lon, nemo.lat, ... 0132 squeeze(nemo.(currvar)(:, :, :, tidx)), ... 0133 hdepth, isnan(nemo.land_mask), 'extrapolate', ... 0134 [flon, flat]); 0135 0136 %---------------------------------------------------------- 0137 % Now we have vertically interpolated data, we can 0138 % interpolate each sigma layer onto the FVCOM unstructured 0139 % grid ready to write out to NetCDF. We'll use the 0140 % triangular interpolation in MATLAB with the natural 0141 % method (gives pretty good results, at least 0142 % qualitatively). 0143 %---------------------------------------------------------- 0144 0145 if ftbverbose 0146 fprintf('horizontally... ') 0147 end 0148 0149 hytempz = hyinterp.(currvar); 0150 0151 parfor zi = 1:fz 0152 % Get the current depth layer's data and mask out the 0153 % NaN values. 0154 hytempzcurrent = hytempz(:, :, zi); 0155 0156 % Strip out NaNs so we can extrapolate with 0157 % TriScatteredInterp. 0158 nanmask = ~isnan(hytempzcurrent); 0159 plonclean = plon(nanmask); 0160 platclean = plat(nanmask); 0161 hytempzclean = hytempzcurrent(nanmask); 0162 % Set up the interpolation object and interpolate the 0163 % current variable to the FVCOM unstructured grid. 0164 ft = scatteredInterpolant(plonclean, platclean, ... 0165 hytempzclean, 'natural', 'none'); 0166 fvtemp(:, zi) = ft(flon, flat); 0167 end 0168 else 0169 % Remove points outside our area of interest and 0170 % interpolate this non-depth-resolved data. 0171 datatmp = nemo.(currvar)(xidxgood); 0172 plon = nemo.lon(xidxgood); 0173 plat = nemo.lat(xidxgood); 0174 % Strip out NaNs so we can extrapolate with 0175 % TriScatteredInterp. 0176 nanmask = ~isnan(datatmp); 0177 plonclean = plon(nanmask); 0178 platclean = plat(nanmask); 0179 hytempzclean = datatmp(nanmask); 0180 0181 ft = scatteredInterpolant(plonclean, platclean, ... 0182 hytempzclean, 'natural', 'none'); 0183 fvtemp = ft(flon, flat); 0184 end 0185 0186 % Unfortunately, TriScatteredInterp won't extrapolate, 0187 % returning instead NaNs outside the original data's extents. 0188 % So, for each NaN position, find the nearest non-NaN value and 0189 % use that instead. The order in which the NaN-nodes are found 0190 % will determine the spatial pattern of the extrapolation. 0191 0192 % We can assume that all layers will have NaNs in the same 0193 % place (horizontally), so just use the surface layer (1) for 0194 % the identification of NaNs. Also store the finite values so 0195 % we can find the nearest real value to the current NaN node 0196 % and use its temperature and salinity values. 0197 fvidx = 1:fn; 0198 fvnanidx = fvidx(isnan(fvtemp(:, 1))); 0199 fvfinidx = fvidx(~isnan(fvtemp(:, 1))); 0200 if isfield(Mobj,'dist') 0201 [~,idx2coast]=sort(Mobj.dist(fvnanidx),'descend'); 0202 fvnanidx = fvnanidx(idx2coast); 0203 end 0204 for ni = 1:length(fvnanidx) 0205 % Find the nearest non-nan data (temp, salinity, u or v) 0206 % value. 0207 xx = flon(fvnanidx(ni)); 0208 yy = flat(fvnanidx(ni)); 0209 0210 [~, di] = min(sqrt((flon(fvfinidx) - xx).^2 + (flat(fvfinidx) - yy).^2)); 0211 fvtemp(fvnanidx(ni), :) = fvtemp(fvfinidx(di), :); 0212 fvfinidx = fvidx(~isnan(fvtemp(:, 1))); 0213 0214 end 0215 0216 clear plon plat flon flat ptempz 0217 0218 Mobj.restart.(currvar) = fvtemp; 0219 0220 te = toc; 0221 0222 if ftbverbose 0223 fprintf('done. (elapsed time = %.2f seconds)\n', te) 0224 end 0225 0226 end 0227 end 0228 0229 % Close the MATLAB pool if we opened it. 0230 if wasOpened 0231 pool.delete 0232 end 0233 0234 if ftbverbose 0235 fprintf('end : %s\n', subname) 0236 end 0237 0238 %% Debugging figure 0239 0240 % close all 0241 % 0242 % ri = 85; % column index 0243 % ci = 95; % row index 0244 % 0245 % [~, idx] = min(sqrt((Mobj.lon - lon(ri, ci)).^2 + (Mobj.lat - lat(ri, ci)).^2)); 0246 % 0247 % % Vertical profiles 0248 % figure 0249 % clf 0250 % 0251 % % The top row shows the temperature/salinity values as plotted against 0252 % % index (i.e. position in the array). I had thought POLCOMS stored its data 0253 % % from seabed to sea surface, but looking at the NetCDF files in ncview, 0254 % % it's clear that the data are in fact stored surface to seabed (like 0255 % % FVCOM). As such, the two plots in the top row should be upside down (i.e. 0256 % % surface values at the bottom of the plot). The two lower rows should have 0257 % % three lines which all match: the raw POLCOMS data, the POLCOMS data for 0258 % % the current time step (i.e. that in 'temperature' and 'salinity') and the 0259 % % interpolated FVCOM data against depth. 0260 % % 0261 % % Also worth noting, the pc.* have the rows and columns flipped, so 0262 % % (ci, ri) in pc.* and (ri, ci) in 'temperature', 'salinity' and 0263 % % 'depth'. Needless to say, the two lines in the lower plots should 0264 % % overlap. 0265 % 0266 % subplot(2,2,1) 0267 % plot(squeeze(pc.ETWD(ci, ri, :, tidx)), 1:size(depth, 3), 'rx:') 0268 % xlabel('Temperature (^{\circ}C)') 0269 % ylabel('Array index') 0270 % title('Array Temperature') 0271 % 0272 % subplot(2,2,2) 0273 % plot(squeeze(pc.x1XD(ci, ri, :, tidx)), 1:size(depth, 3), 'rx:') 0274 % xlabel('Salinity') 0275 % ylabel('Array index') 0276 % title('Array Salinity') 0277 % 0278 % subplot(2,2,3) 0279 % % Although POLCOMS stores its temperature values from seabed to surface, 0280 % % the depths are stored surface to seabed. Nice. Flip the 0281 % % temperature/salinity data accordingly. The lines here should match one 0282 % % another. 0283 % plot(squeeze(pc.ETWD(ci, ri, :, tidx)), squeeze(pc.depth(ci, ri, :, tidx)), 'rx-') 0284 % hold on 0285 % plot(squeeze(temperature(ri, ci, :)), squeeze(depth(ri, ci, :)), '.:') 0286 % % Add the equivalent interpolated FVCOM data point 0287 % plot(fvtemp(idx, :), Mobj.siglayz(idx, :), 'g.-') 0288 % xlabel('Temperature (^{\circ}C)') 0289 % ylabel('Depth (m)') 0290 % title('Depth Temperature') 0291 % legend('pc', 'temp', 'fvcom', 'location', 'north') 0292 % legend('boxoff') 0293 % 0294 % subplot(2,2,4) 0295 % % Although POLCOMS stores its temperature values from seabed to surface, 0296 % % the depths are stored surface to seabed. Nice. Flip the 0297 % % temperature/salinity data accordingly. The lines here should match one 0298 % % another. 0299 % plot(squeeze(pc.x1XD(ci, ri, :, tidx)), squeeze(pc.depth(ci, ri, :, tidx)), 'rx-') 0300 % hold on 0301 % plot(squeeze(salinity(ri, ci, :)), squeeze(depth(ri, ci, :)), '.:') 0302 % % Add the equivalent interpolated FVCOM data point 0303 % plot(fvsalt(idx, :), Mobj.siglayz(idx, :), 'g.-') 0304 % xlabel('Salinity') 0305 % ylabel('Depth (m)') 0306 % title('Depth Salinity') 0307 % legend('pc', 'salt', 'fvcom', 'location', 'north') 0308 % legend('boxoff') 0309 % 0310 % %% Plot the sample location 0311 % figure 0312 % dx = mean(diff(pc.lon)); 0313 % dy = mean(diff(pc.lat)); 0314 % z = depth(:, :, end); % water depth (bottom layer depth) 0315 % z(mask) = 0; % clear out nonsense values 0316 % pcolor(lon - (dx / 2), lat - (dy / 2), z) 0317 % shading flat 0318 % axis('equal', 'tight') 0319 % daspect([1.5, 1, 1]) 0320 % colorbar 0321 % caxis([-150, 0]) 0322 % hold on 0323 % plot(lon(ri, ci), lat(ri, ci), 'ko', 'MarkerFaceColor', 'w') 0324