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Commit eaf013a6 authored by Pierre Cazenave's avatar Pierre Cazenave

New function based on the old replace_FVCOM_restart_vars.m but with more...

New function based on the old replace_FVCOM_restart_vars.m but with more general reuse in mind. Instead of writing to NetCDF directly, this functino now exports the interpolated variables to the MATLAB mesh object and the NetCDF writing has been exported to write_FVCOM_restart.m
parent 41e46aed
function Mobj = interp_AMM2FVCOM(Mobj, ts, start_date, varlist)
% Use an FVCOM restart file to seed a model run with spatially varying
% versions of otherwise constant variables (temperature and salinity only
% for the time being).
%
% function interp_AMM2FVCOM(Mobj, ts, start_date, fv_restart, varlist)
%
% DESCRIPTION:
% FVCOM does not yet support spatially varying temperature and salinity
% inputs as 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 temperature and salinity
% (potentially other variables too) 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.siglayz - sigma layer depths for all model
% nodes.
% - Mobj.lon, Mobj.lat - node coordinates (long/lat).
% ts = Cell array of POLCOMS AMM NetCDF file(s) in which 4D
% variables of temperature and salinity (called 'ETWD' and 'x1XD') exist.
% Its/their shape should be (y, x, sigma, time).
% start_date = Gregorian start date array (YYYY, MM, DD, hh, mm, ss).
% varlist = cell array of variables to extract from the NetCDF files.
%
% OUTPUT:
% Mobj.restart = struct whose field names are the variables which have
% been interpolated (e.g. Mobj.restart.ETWD for POLCOMS daily mean
% temperature).
%
% EXAMPLE USAGE
% interp_AMM2FVCOM(Mobj, '/tmp/ts.nc', '2006-01-01 00:00:00', ...
% {'lon', 'lat', 'ETWD', 'x1XD', 'time'})
%
% Author(s):
% Pierre Cazenave (Plymouth Marine Laboratory)
%
% Revision history
% 2013-02-08 First version.
%
%==========================================================================
subname = 'interp_AMM2FVCOM';
global ftbverbose;
if ftbverbose
fprintf('\n')
fprintf(['begin : ' subname '\n'])
end
%--------------------------------------------------------------------------
% Extract the NetCDF data specified in varlist
%--------------------------------------------------------------------------
% Data format:
%
% amm.ETWD.data and amm.x1XD.data are y, x, sigma, time
%
amm = get_AMM_netCDF(ts, varlist);
% Number of sigma layers.
[fn, fz] = size(Mobj.siglayz);
% Make rectangular arrays for the nearest point lookup.
[lon, lat] = meshgrid(amm.lon.data, amm.lat.data);
% Convert the current times to Modified Julian Day (this is a bit ugly).
amm.time.all = strtrim(regexp(amm.time.units, 'since', 'split'));
amm.time.datetime = strtrim(regexp(amm.time.all{end}, ' ', 'split'));
amm.time.ymd = str2double(strtrim(regexp(amm.time.datetime{1}, '-', 'split')));
amm.time.hms = str2double(strtrim(regexp(amm.time.datetime{2}, ':', 'split')));
Mobj.ts_times = greg2mjulian(...
amm.time.ymd(1), ...
amm.time.ymd(2), ...
amm.time.ymd(3), ...
amm.time.hms(1), ...
amm.time.hms(2), ...
amm.time.hms(3)) + (amm.time.data / 3600 / 24);
% Given our intput time (in start_date), find the nearest time
% index for the regularly gridded data.
stime = greg2mjulian(start_date(1), start_date(2), ...
start_date(3), start_date(4), ...
start_date(5), start_date(6));
[~, tidx] = min(abs(Mobj.ts_times - stime));
%--------------------------------------------------------------------------
% Interpolate the regularly gridded data onto the FVCOM grid (vertical grid
% first).
%--------------------------------------------------------------------------
if ftbverbose
fprintf('%s : interpolate POLCOMS onto FVCOM''s vertical grid... ', subname)
end
% Permute the arrays to be x by y rather than y by x.
temperature = permute(squeeze(amm.ETWD.data(:, :, :, tidx)), [2, 1, 3]);
salinity = permute(squeeze(amm.x1XD.data(:, :, :, tidx)), [2, 1, 3]);
depth = permute(squeeze(amm.depth.data(:, :, :, tidx)), [2, 1, 3]);
mask = depth(:, :, end) >= 0; % land is positive.
amm.tempz = grid_vert_interp(Mobj, lon, lat, temperature, depth, mask);
amm.salz = grid_vert_interp(Mobj, lon, lat, salinity, depth, mask);
if ftbverbose
fprintf('done.\n')
end
%--------------------------------------------------------------------------
% Now we have vertically interpolated data, we can interpolate each sigma
% layer onto the FVCOM unstructured grid ready to write out to NetCDF.
% We'll use the triangular interpolation in MATLAB with the natural method
% (gives pretty good results, at least qualitatively).
%--------------------------------------------------------------------------
if ftbverbose
fprintf('%s : interpolate POLCOMS onto FVCOM''s horizontal grid... ', subname)
end
fvtemp = nan(fn, fz);
fvsalt = nan(fn, fz);
for zi = 1:fz
% Set up the interpolation object.
ft = TriScatteredInterp(lon(:), lat(:), reshape(amm.tempz(:, :, zi), [], 1), 'natural');
fs = TriScatteredInterp(lon(:), lat(:), reshape(amm.salz(:, :, zi), [], 1), 'natural');
% Interpolate temperature and salinity onto the unstructured grid.
fvtemp(:, zi) = ft(Mobj.lon, Mobj.lat);
fvsalt(:, zi) = fs(Mobj.lon, Mobj.lat);
end
% Unfortunately, TriScatteredInterp won't extrapolate, returning instead
% NaNs outside the original data's extents. So, for each NaN position, find
% the nearest non-NaN value and use that instead. The order in which the
% NaN-nodes are found will determine the spatial pattern of the
% extrapolation.
% We can assume that all layers will have NaNs in the same place
% (horizontally), so just use the surface layer (1) for the identification
% of NaNs. Also store the finite values so we can find the nearest real
% value to the current NaN node and use its temperature and salinity
% values.
fvidx = 1:fn;
fvnanidx = fvidx(isnan(fvtemp(:, 1)));
fvfinidx = fvidx(~isnan(fvtemp(:, 1)));
for ni = 1:length(fvnanidx);
% Current position
xx = Mobj.lon(fvnanidx(ni));
yy = Mobj.lat(fvnanidx(ni));
% Find the nearest non-nan temperature and salinity value.
[~, di] = min(sqrt((Mobj.lon(fvfinidx) - xx).^2 + (Mobj.lat(fvfinidx) - yy).^2));
% Replace the temperature and salinity values at all depths at the
% current NaN position with the closest non-nan value.
fvtemp(fvnanidx(ni), :) = fvtemp(fvfinidx(di), :);
fvsalt(fvnanidx(ni), :) = fvsalt(fvfinidx(di), :);
end
if ftbverbose
fprintf('done.\n')
end
Mobj.restart.temp = fvtemp;
Mobj.restart.salinity = fvsalt;
%--------------------------------------------------------------------------
% Get the restart data and replace with the interpolated data.
%--------------------------------------------------------------------------
% if ftbverbose
% fprintf('%s : export interpolated data to NetCDF:\n', subname)
% end
%
% % Create the struct with fieldnames which match the variables to be
% % replaced in the FVCOM restart file.
% fvdata.temperature = fvtemp;
% fvdata.salt = fvsalt;
%
% [pp, nn, ee] = fileparts(fv_restart);
% out_restart = fullfile(pp, [nn, '_interp', ee]);
% write_FVCOM_restart(fv_restart, out_restart, fvdata)
%
% if ftbverbose
% fprintf('%s : export complete.\n', subname)
% end
if ftbverbose
fprintf(['end : ' subname '\n'])
end
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