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.

Commit c4bdd490 authored by Pierre Cazenave's avatar Pierre Cazenave

Add new function to extract CFSv2 reanalysis forcing data from the NOAA OPeNDAP server.

parent 87e6698d
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);
years = unique(years, 'stable');
months = unique(months, 'stable');
ny = length(years);
nm = length(months);
for y = 1:ny
year = years(y);
for m = 1:nm
month = months(m);
% 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.
ncep.tmp2m = [url, sprintf('%04d%02d/tmp2m.gdas.%04d%02d.grb2', year, month, year, month)];
ncep.wnd = [url, sprintf('%04d%02d/wnd10m.gdas.%04d%02d.grb2', year, month, year, month)];
ncep.q2m = [url, sprintf('%04d%02d/q2m.gdas.%04d%02d.grb2', year, month, year, month)];
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)];
ncep.lhtfl = [url, sprintf('%04d%02d/lhtfl.gdas.%04d%02d.grb2', year, month, year, month)];
ncep.dswsfc = [url, sprintf('%04d%02d/dswsfc.gdas.%04d%02d.grb2', year, month, year, month)];
ncep.uswsfc = [url, sprintf('%04d%02d/uswsfc.gdas.%04d%02d.grb2', year, month, year, month)];
ncep.dlwsfc = [url, sprintf('%04d%02d/dlwsfc.gdas.%04d%02d.grb2', year, month, year, month)];
names.tmp2m = 'Temperature';
names.uwnd = 'U-component_of_wind';
names.uwnd = 'V-component_of_wind';
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 = [];
data_attributes.(fields{aa}) = [];
%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');
data_time.time = netcdf.getVar(ncid, varid, 'double');
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.
timevec = datevec((data_time.time / 24) + datenum(year, month, 1, 0, 0, 0));
% 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);
% 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
data1_west.(fields{aa}).(fields{aa}) = netcdf.getVar(ncid, varid, start, count, 'double');
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
data1_east.(fields{aa}).(fields{aa}) = netcdf.getVar(ncid, varid, start, count, 'double');
data1.(fields{aa}).(fields{aa}).(fields{aa}) = ...
cat(1, data1_west.(fields{aa}).(fields{aa}), data1_east.(fields{aa}).(fields{aa}));
% Merge the two sets of data together
structfields = fieldnames(data1_west.(fields{aa}).(fields{aa}));
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).
data1.(fields{aa}).(fields{aa}).(structfields{ii}) = ...
[data1_west.(fields{aa}).(fields{aa}).(structfields{ii});data1_east.(fields{aa}).(fields{aa}).(structfields{ii})];
case fields{aa}
% This is the actual data
data1.(fields{aa}).(fields{aa}).(structfields{ii}) = ...
[data1_west.(fields{aa}).(fields{aa}).(structfields{ii}),data1_east.(fields{aa}).(fields{aa}).(structfields{ii})];
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
tdata = data1_west.(fields{aa}).(fields{aa}).(structfields{ii}) - data1_east.(fields{aa}).(fields{aa}).(structfields{ii});
if range(tdata(:)) == 0
% They're the same data
data1.(fields{aa}).(fields{aa}).(structfields{ii}) = ...
data1_west.(fields{aa}).(fields{aa}).(structfields{ii});
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
clear tdata
end
end
else
% We have a straightforward data extraction
data.(fields{aa}).lon = data_lon.lon(index_lon);
varid = netcdf.inqVarID(ncid,(fields{aa}));
% [varname,xtype,dimids,natts] = netcdf.inqVar(ncid,varid);
% [~,length1] = netcdf.inqDim(ncid,dimids(1))
% [~,length2] = netcdf.inqDim(ncid,dimids(2))
% [~,length3] = netcdf.inqDim(ncid,dimids(3))
start=[min(index_lon)-1,min(index_lat)-1,min(data_time_idx)-1];
count=[length(index_lon),length(index_lat),length(data_time_idx)];
% The air data 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
% getting only the first level (start = 0, count = 1).
try
data1.(fields{aa}).(fields{aa}).(fields{aa}) = netcdf.getVar(ncid,varid,start,count,'double');
catch
start=[min(index_lon)-1,min(index_lat)-1,0,min(data_time_idx)-1];
count=[length(index_lon),length(index_lat),1,length(data_time_idx)];
data1.(fields{aa}).(fields{aa}).(fields{aa}) = netcdf.getVar(ncid,varid,start,count,'double');
end
end
datatmp = squeeze(data1.(fields{aa}).(fields{aa}).(fields{aa}));
datatmp = (datatmp * data_attributes.(fields{aa}).(fields{aa}).scale_factor) + data_attributes.(fields{aa}).(fields{aa}).add_offset;
% 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;
data.(fields{aa}).data = datatmp;
data.(fields{aa}).time = data.time;
data.(fields{aa}).unpacked_valid_range = ...
data_attributes.(fields{aa}).(fields{aa}).unpacked_valid_range;
% data.(fields{aa}).time = cat(1, data.(fields{aa}).time, squeeze(data1.(fields{aa}).(fields{aa}).time));
% data.(fields{aa}).lat = squeeze(data1.(fields{aa}).(fields{aa}).lat);
% data.(fields{aa}).lon = squeeze(data1.(fields{aa}).(fields{aa}).lon);
% Replace values outside the specified actual range with NaNs. For the
% majority of the variables, this shouldn't ever really generate values
% of NaN since the coverage is global (land and sea). This did crop up
% as a problem with the pevpr data (which is land only). In some ways,
% if something fails later on (e.g. the interpolation) because there's
% NaNs, that should be a wakeup call to check what's going on with the
% data.
if isfield(data_attributes.(fields{aa}).(fields{aa}), 'actual_range')
actual_min = data_attributes.(fields{aa}).(fields{aa}).actual_range(1);
actual_max = data_attributes.(fields{aa}).(fields{aa}).actual_range(2);
mask = data.(fields{aa}).data < actual_min | data.(fields{aa}).data > actual_max;
data.(fields{aa}).data(mask) = NaN;
end
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.
if isfield(data, 'ulwrf') && isfield(data, 'uswrf') && isfield(data, 'dlwrf') && isfield(data, 'dswrf')
vars = {'nswrs', 'nlwrs'};
up = {'uswrf', 'ulwrf'};
down = {'dswrf', 'dlwrf'};
for i = 1:length(vars)
data.(vars{i}).data = data.(up{i}).data - data.(down{i}).data;
data.(vars{i}).time = data.(up{i}).time;
data.(vars{i}).lon = data.(up{i}).lon;
data.(vars{i}).lat = data.(up{i}).lat;
end
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')
data.prate.data = data.prate.data/1000;
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
%
if isfield(data, 'prate')
Llv = 2.5*10^6;
rho = 1025; % using a typical value for seawater.
Et = data.lhtfl.data/Llv/rho;
data.P_E.data = data.prate.data - Et;
% Evaporation and precipitation need to have the same sign for FVCOM (ocean
% losing water is negative in both instances). So, flip the evaporation
% here.
data.Et.data = -Et;
end
% Get the fields we need for the subsequent interpolation Find the position
% of a sensibly sized array (i.e. not 'topo', 'rhum' or 'pres').
for vv = 1:length(fields)
if ~isempty(varlist) && max(strcmp(fields{vv}, varlist)) ~= 1
continue
end
switch fields{vv}
% Set ii in each instance in case we've been told to only use
% one of the three alternatively gridded data.
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;
% Create a land mask from the pevpr data (if it's been extracted).
if isfield(data, 'pevpr')
% Find any value less than or equal to the valid maximum across all
% time steps.
data.land_mask = max(data.pevpr.data <= data.pevpr.unpacked_valid_range(2), [], 3);
end
% Convert temperature to degrees Celsius (from Kelvin)
if isfield(data, 'air')
data.air.data = data.air.data - 273.15;
end
% Make sure all the data we have downloaded is the same shape as the
% longitude and latitude arrays. This is complicated by the fact the NCEP
% surface products (e.g. rhum, pres) are on a different grid from the rest
% (e.g. uwnd).
for aa = 1:length(fields)
% if strcmpi(fields{aa}, 'dswrf') || strcmpi(fields{aa}, 'dlwrf') || strcmpi(fields{aa}, 'uswrf') || strcmpi(fields{aa}, 'ulwrf')
% % But only if we haven't been given a list of variables to fetch.
% if nargin ~= 3
% continue
% end
% end
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
error('Unable to resize data arrays to match position data orientation. Are these data NCEP surface data (i.e. on a different horizontal grid?)')
else
if ftbverbose
fprintf('Matching %s data dimensions to position arrays\n', fields{aa})
end
end
end
else
warning('Variable %s requested but not downloaded?', fields{aa})
end
end
end
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
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment