def remap_rignot(inFileName,
meshFileName,
meshName,
outFileName,
mappingDirectory='.',
method='conserve',
renormalizationThreshold=None,
inVarName='melt_actual',
mpiTasks=1):
# {{{
"""
Remap the Rignot et al. (2013) melt rates at 1 km resolution to an MPAS
mesh
Parameters
----------
inFileName : str
The original Rignot et al. (2013) melt rates
meshFileName : str
The MPAS mesh
meshName : str
The name of the mesh (e.g. oEC60to30wISC), used in the name of the
mapping file
outFileName : str
The melt rates interpolated to the MPAS mesh with ocean sensible heat
fluxes added on (assuming insulating ice)
mappingDirectory : str
The directory where the mapping file should be stored (if it is to be
computed) or where it already exists (if not)
method : {'bilinear', 'neareststod', 'conserve'}, optional
The method of interpolation used, see documentation for
`ESMF_RegridWeightGen` for details.
renormalizationThreshold : float, optional
The minimum weight of a denstination cell after remapping, below
which it is masked out, or ``None`` for no renormalization and
masking.
inVarName : {'melt_actual', 'melt_steadystate'}
Whether to use the melt rate for the time period covered in Rignot et
al. (2013) with observed thinning/thickening or the melt rates that
would be required if ice shelves were in steady state.
mpiTasks : int, optional
The number of MPI tasks to use to compute the mapping file
"""
ds = xr.open_dataset(inFileName)
lx = np.abs(1e-3 * (ds.xaxis.values[-1] - ds.xaxis.values[0]))
ly = np.abs(1e-3 * (ds.yaxis.values[-1] - ds.yaxis.values[0]))
inGridName = '{}x{}km_1.0km_Antarctic_stereo'.format(lx, ly)
projection = pyproj.Proj('+proj=stere +lat_ts=-71.0 +lat_0=-90 +lon_0=0.0 '
'+k_0=1.0 +x_0=0.0 +y_0=0.0 +ellps=WGS84')
inDescriptor = ProjectionGridDescriptor.read(projection,
inFileName,
xVarName='xaxis',
yVarName='yaxis',
meshName=inGridName)
# convert to the units and variable names expected in MPAS-O
rho_fw = 1000.
s_per_yr = 365. * 24. * 60. * 60.
latent_heat_of_fusion = 3.337e5
ds['prescribedLandIceFreshwaterFlux'] = ds[inVarName] * rho_fw / s_per_yr
ds['prescribedLandIceHeatFlux'] = (latent_heat_of_fusion *
ds['prescribedLandIceFreshwaterFlux'])
ds = ds.drop_vars(['melt_actual', 'melt_steadystate', 'lon', 'lat'])
outDescriptor = MpasMeshDescriptor(meshFileName, meshName)
mappingFileName = '{}/map_{}_to_{}.nc'.format(mappingDirectory, inGridName,
meshName)
remapper = Remapper(inDescriptor, outDescriptor, mappingFileName)
remapper.build_mapping_file(method=method, mpiTasks=mpiTasks)
dsRemap = remapper.remap(ds,
renormalizationThreshold=renormalizationThreshold)
for field in [
'prescribedLandIceFreshwaterFlux', 'prescribedLandIceHeatFlux'
]:
# zero out the field where it's currently NaN
dsRemap[field] = dsRemap[field].where(dsRemap[field].nonnull(), 0.)
dsRemap.attrs['history'] = ' '.join(sys.argv)
write_netcdf(dsRemap, outFileName) # }}}
inGridName = 'oQU240'
# replace with the path to the desired mesh or restart file
# As an example, use:
# https://web.lcrc.anl.gov/public/e3sm/inputdata/ocn/mpas-o/oQU240/ocean.QU.240km.151209.nc
inGridFileName = 'ocean.QU.240km.151209.nc'
inDescriptor = MpasMeshDescriptor(inGridFileName, inGridName)
# modify the size and resolution of the Antarctic grid as desired
outDescriptor = get_polar_descriptor(Lx=6000.,
Ly=6000.,
dx=10.,
dy=10.,
projection='antarctic')
outGridName = outDescriptor.meshName
mappingFileName = 'map_{}_to_{}_conserve.nc'.format(inGridName, outGridName)
remapper = Remapper(inDescriptor, outDescriptor, mappingFileName)
# conservative remapping with 4 MPI tasks (using mpirun)
remapper.build_mapping_file(method='conserve', mpiTasks=4)
outFileName = 'temp_{}.nc'.format(outGridName)
ds = xarray.open_dataset(inGridFileName)
dsOut = xarray.Dataset()
dsOut['temperature'] = ds['temperature']
dsOut = remapper.remap(dsOut)
dsOut.to_netcdf(outFileName)
def _remap(config, modelFolder):
res = get_res(config)
hres = get_horiz_res(config)
modelName = config.get('model', 'name')
inFileNames = {}
outFileNames = {}
bothExist = True
for fieldName in ['temperature', 'salinity']:
inFileNames[fieldName] = \
'{}/{}_{}_interp_z.nc'.format(modelFolder, modelName, fieldName)
outFileNames[fieldName] = \
'{}/{}_{}_{}.nc'.format(modelFolder, modelName, fieldName, res)
if not os.path.exists(outFileNames[fieldName]):
bothExist = False
if bothExist:
return
print(' Remapping to {} grid...'.format(res))
for fieldName in inFileNames:
inFileName = inFileNames[fieldName]
outFileName = outFileNames[fieldName]
if os.path.exists(outFileName):
continue
outGridFileName = 'ismip6/{}_grid.nc'.format(hres)
print(' {}'.format(outFileName))
progressDir = '{}/progress_remap_{}'.format(modelFolder, fieldName)
try:
os.makedirs(progressDir)
except OSError:
pass
ds = xarray.open_dataset(inFileName)
if len(ds.lon.dims) == 1:
inDescriptor = LatLonGridDescriptor.read(inFileName,
latVarName='lat',
lonVarName='lon')
else:
assert (len(ds.lon.dims) == 2)
inDescriptor = LatLon2DGridDescriptor.read(inFileName,
latVarName='lat',
lonVarName='lon')
inDescriptor.regional = True
outDescriptor = get_polar_descriptor_from_file(outGridFileName,
projection='antarctic')
mappingFileName = '{}/map_{}_to_{}.nc'.format(modelName.lower(),
inDescriptor.meshName,
outDescriptor.meshName)
remapper = Remapper(inDescriptor, outDescriptor, mappingFileName)
remapper.build_mapping_file(method='bilinear')
ds = ds.drop_vars(['lat', 'lon'])
nt = ds.sizes['time']
widgets = [
' ',
progressbar.Percentage(), ' ',
progressbar.Bar(), ' ',
progressbar.ETA()
]
print(f' remapping: {fieldName}')
bar = progressbar.ProgressBar(widgets=widgets, maxval=nt).start()
for tIndex in range(nt):
progressFileName = '{}/{}_t_{}.nc'.format(progressDir, modelName,
tIndex)
if os.path.exists(progressFileName):
bar.update(tIndex + 1)
continue
dsIn = ds.isel(time=tIndex)
dsOut = remapper.remap(dsIn, renormalizationThreshold=0.1)
dsOut = dsOut.transpose('z', 'y', 'x')
for attrName in ['units', 'standard_name', 'long_name']:
if attrName in ds[fieldName].attrs:
dsOut[fieldName].attrs[attrName] = \
ds[fieldName].attrs[attrName]
dsOut.z.attrs = ds.z.attrs
dsOut.to_netcdf(progressFileName)
bar.update(tIndex + 1)
bar.finish()
dsOut = xarray.open_mfdataset('{}/{}_t_*.nc'.format(
progressDir, modelName),
combine='nested',
concat_dim='time')
dsOut['z_bnds'] = ds.z_bnds
dsOut.to_netcdf(outFileName)
Ly = int((y[-1] - y[0]) / 1000.)
inMeshName = '{}x{}km_{}km_Antarctic_stereo'.format(Lx, Ly, dx)
projection = get_antarctic_stereographic_projection()
inDescriptor = ProjectionGridDescriptor.create(projection, x, y, inMeshName)
outRes = args.resolution * 1e3
nxOut = int((x[-1] - x[0]) / outRes + 0.5) + 1
nyOut = int((y[-1] - y[0]) / outRes + 0.5) + 1
xOut = x[0] + outRes * numpy.arange(nxOut)
yOut = y[0] + outRes * numpy.arange(nyOut)
outMeshName = '{}x{}km_{}km_Antarctic_stereo'.format(Lx, Ly, args.resolution)
outDescriptor = ProjectionGridDescriptor.create(projection, xOut, yOut,
outMeshName)
mappingFileName = 'map_{}_to_{}_{}.nc'.format(inMeshName, outMeshName,
args.method)
remapper = Remapper(inDescriptor, outDescriptor, mappingFileName)
remapper.build_mapping_file(method=args.method, mpiTasks=args.mpiTasks)
dsOut = remapper.remap(dsIn, renormalizationThreshold=0.01)
dsOut.to_netcdf(args.outFileName)
