def handle(infiles, tables, user_input_path, **kwargs):
"""
Transform MPASO timeMonthly_avg_activeTracers_temperature into CMIP.tosga
Parameters
----------
infiles : dict
a dictionary with namelist, mesh and time series file names
tables : str
path to CMOR tables
user_input_path : str
path to user input json file
Returns
-------
varname : str
the name of the processed variable after processing is complete
"""
if kwargs.get('simple'):
msg = f"{VAR_NAME} is not supported for simple conversion"
print_message(msg)
return
msg = 'Starting {name}'.format(name=__name__)
logging.info(msg)
meshFileName = infiles['MPAS_mesh']
timeSeriesFiles = infiles['MPASO']
dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False)
cellMask2D, _ = mpas.get_cell_masks(dsMesh)
variableList = [
'timeMonthly_avg_activeTracers_temperature', 'xtime_startMonthly',
'xtime_endMonthly'
]
ds = xarray.Dataset()
with mpas.open_mfdataset(timeSeriesFiles, variableList) as dsIn:
thetao = dsIn.timeMonthly_avg_activeTracers_temperature
tos = thetao.isel(nVertLevels=0).squeeze(drop=True).where(cellMask2D)
areaCell = dsMesh.areaCell.where(cellMask2D)
ds[VAR_NAME] = ((tos * areaCell).sum(dim='nCells') /
areaCell.sum(dim='nCells'))
ds = mpas.add_time(ds, dsIn)
ds.compute()
ds.compute()
mpas.setup_cmor(VAR_NAME, tables, user_input_path, component='ocean')
# create axes
axes = [{'table_entry': 'time', 'units': ds.time.units}]
try:
mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS)
except Exception:
return ""
return VAR_NAME
def handle(infiles, tables, user_input_path, **kwargs):
"""
Transform MPASO timeMonthly_avg_layerThickness and
timeMonthly_avg_activeTracers_temperature into CMIP.thetaoga
Parameters
----------
infiles : dict
a dictionary with namelist, mesh and time series file names
tables : str
path to CMOR tables
user_input_path : str
path to user input json file
Returns
-------
varname : str
the name of the processed variable after processing is complete
"""
msg = 'Starting {name}'.format(name=__name__)
logging.info(msg)
meshFileName = infiles['MPAS_mesh']
timeSeriesFiles = infiles['MPASO']
dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False)
_, cellMask3D = mpas.get_cell_masks(dsMesh)
variableList = [
'timeMonthly_avg_layerThickness',
'timeMonthly_avg_activeTracers_temperature', 'xtime_startMonthly',
'xtime_endMonthly'
]
ds = xarray.Dataset()
with mpas.open_mfdataset(timeSeriesFiles, variableList) as dsIn:
layerThickness = dsIn.timeMonthly_avg_layerThickness.where(cellMask3D)
thetao = dsIn.timeMonthly_avg_activeTracers_temperature.where(
cellMask3D)
vol = layerThickness * dsMesh.areaCell
volo = (vol).sum(dim=['nVertLevels', 'nCells'])
ds[VAR_NAME] = (vol * thetao).sum(dim=['nVertLevels', 'nCells']) / volo
ds = mpas.add_time(ds, dsIn)
ds.compute()
mpas.setup_cmor(VAR_NAME, tables, user_input_path, component='ocean')
# create axes
axes = [{'table_entry': 'time', 'units': ds.time.units}]
try:
mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS)
except Exception:
return ""
return VAR_NAME
def handle(infiles, tables, user_input_path, **kwargs):
"""
Transform MPASO timeMonthly_avg_dThreshMLD into CMIP.mlotst
Parameters
----------
infiles : dict
a dictionary with namelist, mesh and time series file names
tables : str
path to CMOR tables
user_input_path : str
path to user input json file
Returns
-------
varname : str
the name of the processed variable after processing is complete
"""
msg = 'Starting {name}'.format(name=__name__)
logging.info(msg)
meshFileName = infiles['MPAS_mesh']
mappingFileName = infiles['MPAS_map']
timeSeriesFiles = infiles['MPASO']
dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False)
cellMask2D, _ = mpas.get_cell_masks(dsMesh)
variableList = [
'timeMonthly_avg_dThreshMLD', 'xtime_startMonthly', 'xtime_endMonthly'
]
ds = xarray.Dataset()
with mpas.open_mfdataset(timeSeriesFiles, variableList) as dsIn:
ds[VAR_NAME] = dsIn.timeMonthly_avg_dThreshMLD.where(cellMask2D)
ds = mpas.add_time(ds, dsIn)
ds.compute()
ds = mpas.add_mask(ds, cellMask2D)
ds = mpas.remap(ds, mappingFileName)
mpas.setup_cmor(VAR_NAME, tables, user_input_path, component='ocean')
# create axes
axes = [{
'table_entry': 'time',
'units': ds.time.units
}, {
'table_entry': 'latitude',
'units': 'degrees_north',
'coord_vals': ds.lat.values,
'cell_bounds': ds.lat_bnds.values
}, {
'table_entry': 'longitude',
'units': 'degrees_east',
'coord_vals': ds.lon.values,
'cell_bounds': ds.lon_bnds.values
}]
try:
mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS)
except Exception:
return ""
return VAR_NAME
def handle(infiles, tables, user_input_path, **kwargs):
"""
Transform MPASO timeMonthly_avg_layerThickness into CMIP.masscello
Parameters
----------
infiles : dict
a dictionary with namelist, mesh and time series file names
tables : str
path to CMOR tables
user_input_path : str
path to user input json file
Returns
-------
varname : str
the name of the processed variable after processing is complete
"""
if kwargs.get('simple'):
msg = f"{VAR_NAME} is not supported for simple conversion"
print_message(msg)
return
msg = 'Starting {name}'.format(name=__name__)
logging.info(msg)
namelistFileName = infiles['MPASO_namelist']
meshFileName = infiles['MPAS_mesh']
mappingFileName = infiles['MPAS_map']
timeSeriesFiles = infiles['MPASO']
namelist = mpas.convert_namelist_to_dict(namelistFileName)
config_density0 = float(namelist['config_density0'])
dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False)
_, cellMask3D = mpas.get_cell_masks(dsMesh)
variableList = [
'timeMonthly_avg_layerThickness', 'xtime_startMonthly',
'xtime_endMonthly'
]
ds = xarray.Dataset()
with mpas.open_mfdataset(timeSeriesFiles, variableList) as dsIn:
ds[VAR_NAME] = config_density0 * \
dsIn.timeMonthly_avg_layerThickness.where(cellMask3D, 0.)
ds = mpas.add_time(ds, dsIn)
ds.compute()
ds = mpas.add_depth(ds, dsMesh)
ds.compute()
ds = mpas.remap(ds, mappingFileName)
# set masked values (where there are no MPAS grid cells) to zero
ds[VAR_NAME] = ds[VAR_NAME].where(
ds[VAR_NAME] != netCDF4.default_fillvals['f4'], 0.)
mpas.setup_cmor(VAR_NAME, tables, user_input_path, component='ocean')
# create axes
axes = [{
'table_entry': 'time',
'units': ds.time.units
}, {
'table_entry': 'depth_coord',
'units': 'm',
'coord_vals': ds.depth.values,
'cell_bounds': ds.depth_bnds.values
}, {
'table_entry': 'latitude',
'units': 'degrees_north',
'coord_vals': ds.lat.values,
'cell_bounds': ds.lat_bnds.values
}, {
'table_entry': 'longitude',
'units': 'degrees_east',
'coord_vals': ds.lon.values,
'cell_bounds': ds.lon_bnds.values
}]
try:
mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS)
except Exception:
return ""
return VAR_NAME
def handle(infiles, tables, user_input_path, **kwargs):
"""
Transform MPASO timeMonthly_avg_frazilLayerThicknessTendency into
CMIP.hfsifrazil
Parameters
----------
infiles : dict
a dictionary with namelist, mesh and time series file names
tables : str
path to CMOR tables
user_input_path : str
path to user input json file
Returns
-------
varname : str
the name of the processed variable after processing is complete
"""
msg = 'Starting {name}'.format(name=__name__)
logging.info(msg)
timeSeriesFiles = infiles['MPASO']
mappingFileName = infiles['MPAS_map']
meshFileName = infiles['MPAS_mesh']
namelistFileName = infiles['MPASO_namelist']
namelist = mpas.convert_namelist_to_dict(namelistFileName)
config_density0 = float(namelist['config_density0'])
config_frazil_heat_of_fusion = \
float(namelist['config_frazil_heat_of_fusion'])
dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False)
_, cellMask3D = mpas.get_cell_masks(dsMesh)
variableList = [
'timeMonthly_avg_frazilLayerThicknessTendency', 'xtime_startMonthly',
'xtime_endMonthly'
]
ds = xarray.Dataset()
with mpas.open_mfdataset(timeSeriesFiles, variableList) as dsIn:
ds[VAR_NAME] = -config_density0 * config_frazil_heat_of_fusion * \
dsIn.timeMonthly_avg_frazilLayerThicknessTendency
ds = mpas.add_time(ds, dsIn)
ds.compute()
ds = mpas.add_mask(ds, cellMask3D)
ds = mpas.add_depth(ds, dsMesh)
ds.compute()
ds = mpas.remap(ds, mappingFileName)
mpas.setup_cmor(VAR_NAME, tables, user_input_path, component='ocean')
# create axes
axes = [{
'table_entry': 'time',
'units': ds.time.units
}, {
'table_entry': 'depth_coord',
'units': 'm',
'coord_vals': ds.depth.values,
'cell_bounds': ds.depth_bnds.values
}, {
'table_entry': 'latitude',
'units': 'degrees_north',
'coord_vals': ds.lat.values,
'cell_bounds': ds.lat_bnds.values
}, {
'table_entry': 'longitude',
'units': 'degrees_east',
'coord_vals': ds.lon.values,
'cell_bounds': ds.lon_bnds.values
}]
try:
mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS, positive='down')
except Exception:
return ""
return VAR_NAME
def handle(infiles, tables, user_input_path, **kwargs):
"""
Transform MPASO timeMonthly_avg_layerThickness into CMIP.thkcello
Parameters
----------
infiles : dict
a dictionary with namelist, mesh and time series file names
tables : str
path to CMOR tables
user_input_path : str
path to user input json file
Returns
-------
varname : str
the name of the processed variable after processing is complete
"""
if kwargs.get('simple'):
msg = f"{VAR_NAME} is not supported for simple conversion"
print_message(msg)
return
msg = 'Starting {name}'.format(name=__name__)
logging.info(msg)
meshFileName = infiles['MPAS_mesh']
mappingFileName = infiles['MPAS_map']
timeSeriesFiles = infiles['MPASO']
dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False)
earth_radius = dsMesh.attrs['sphere_radius']
_, cellMask3D = mpas.get_cell_masks(dsMesh)
variableList = [
'timeMonthly_avg_layerThickness', 'xtime_startMonthly',
'xtime_endMonthly'
]
ds = xarray.Dataset()
with mpas.open_mfdataset(timeSeriesFiles, variableList) as dsIn:
ds[VAR_NAME] = \
dsIn.timeMonthly_avg_layerThickness.where(cellMask3D, 0.)
ds = mpas.add_time(ds, dsIn)
ds.compute()
ds = mpas.add_depth(ds, dsMesh)
ds.compute()
ds = mpas.remap(ds, mappingFileName)
# set masked values (where there are no MPAS grid cells) to zero
ds[VAR_NAME] = ds[VAR_NAME].where(
ds[VAR_NAME] != netCDF4.default_fillvals['f4'], 0.)
# the result above is just a mask of area fraction. We need to multiply
# by the area on the output grid
dsMap = xarray.open_dataset(mappingFileName)
area_b = dsMap.area_b.values
dst_grid_dims = dsMap.dst_grid_dims.values
area_b = area_b.reshape((dst_grid_dims[1], dst_grid_dims[0]))
area_b = xarray.DataArray(data=area_b,
dims=('lat', 'lon'),
coords=(ds.coords['lat'], ds.coords['lon']))
# area_b is in square radians, so need to multiply by the earth_radius**2
# multiply variables in this order so they don't get transposed
ds[VAR_NAME] = ds[VAR_NAME] * earth_radius**2 * area_b
setup_cmor(var_name=VAR_NAME,
table_path=tables,
table_name=TABLE,
user_input_path=user_input_path)
# create axes
axes = [{
'table_entry': 'time',
'units': ds.time.units
}, {
'table_entry': 'depth_coord',
'units': 'm',
'coord_vals': ds.depth.values,
'cell_bounds': ds.depth_bnds.values
}, {
'table_entry': 'latitude',
'units': 'degrees_north',
'coord_vals': ds.lat.values,
'cell_bounds': ds.lat_bnds.values
}, {
'table_entry': 'longitude',
'units': 'degrees_east',
'coord_vals': ds.lon.values,
'cell_bounds': ds.lon_bnds.values
}]
try:
mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS)
except Exception:
return ""
return VAR_NAME
def handle(infiles, tables, user_input_path, **kwargs):
"""
Transform MPASSI timeMonthly_avg_iceAreaCell and
timeMonthly_avg_iceVolumeCell into CMIP.sithick
Parameters
----------
infiles : dict
a dictionary with namelist, mesh and time series file names
tables : str
path to CMOR tables
user_input_path : str
path to user input json file
Returns
-------
varname : str
the name of the processed variable after processing is complete
"""
if kwargs.get('simple'):
msg = f"{VAR_NAME} is not supported for simple conversion"
print_message(msg)
return
msg = 'Starting {name}'.format(name=__name__)
logging.info(msg)
meshFileName = infiles['MPAS_mesh']
mappingFileName = infiles['MPAS_map']
timeSeriesFiles = infiles['MPASSI']
dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False)
cellMask2D, _ = mpas.get_cell_masks(dsMesh)
variableList = [
'timeMonthly_avg_iceAreaCell', 'timeMonthly_avg_iceVolumeCell',
'xtime_startMonthly', 'xtime_endMonthly'
]
ds = xarray.Dataset()
with mpas.open_mfdataset(timeSeriesFiles, variableList) as dsIn:
ds[VAR_NAME] = dsIn.timeMonthly_avg_iceVolumeCell
ds['siconc'] = dsIn.timeMonthly_avg_iceAreaCell
ds = mpas.add_time(ds, dsIn)
ds.compute()
ds = mpas.add_si_mask(ds, cellMask2D, ds.siconc)
ds['cellMask'] = ds.siconc * ds.cellMask
ds.compute()
ds = mpas.remap(ds, mappingFileName)
mpas.setup_cmor(VAR_NAME, tables, user_input_path, component='seaice')
# create axes
axes = [{
'table_entry': 'time',
'units': ds.time.units
}, {
'table_entry': 'latitude',
'units': 'degrees_north',
'coord_vals': ds.lat.values,
'cell_bounds': ds.lat_bnds.values
}, {
'table_entry': 'longitude',
'units': 'degrees_east',
'coord_vals': ds.lon.values,
'cell_bounds': ds.lon_bnds.values
}]
try:
mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS)
except Exception:
return ""
return VAR_NAME
def handle(infiles, tables, user_input_path, **kwargs):
"""
Transform MPASO timeMonthly_avg_pressureAdjustedSSH, timeMonthly_avg_ssh,
timeMonthly_avg_density, timeMonthly_avg_layerThickness, and EAM PSL into
CMIP.pbo
Parameters
----------
infiles : dict
a dictionary with namelist, mesh and time series file names
tables : str
path to CMOR tables
user_input_path : str
path to user input json file
Returns
-------
varname : str
the name of the processed variable after processing is complete
"""
msg = 'Starting {name}'.format(name=__name__)
logging.info(msg)
namelistFileName = infiles['MPASO_namelist']
meshFileName = infiles['MPAS_mesh']
mappingFileName = infiles['MPAS_map']
timeSeriesFiles = infiles['MPASO']
pslFileNames = infiles['PSL']
namelist = mpas.convert_namelist_to_dict(namelistFileName)
config_density0 = float(namelist['config_density0'])
gravity = 9.80616
dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False)
cellMask2D, cellMask3D = mpas.get_cell_masks(dsMesh)
variableList = [
'timeMonthly_avg_pressureAdjustedSSH', 'timeMonthly_avg_ssh',
'timeMonthly_avg_layerThickness', 'timeMonthly_avg_density',
'xtime_startMonthly', 'xtime_endMonthly'
]
ds = xarray.Dataset()
with mpas.open_mfdataset(timeSeriesFiles, variableList) as dsIn:
seaIcePressure = config_density0 * gravity * \
(dsIn.timeMonthly_avg_pressureAdjustedSSH -
dsIn.timeMonthly_avg_ssh)
ds[VAR_NAME] = seaIcePressure.where(cellMask2D) + gravity * \
(dsIn.timeMonthly_avg_density *
dsIn.timeMonthly_avg_layerThickness).where(cellMask3D).sum(
dim='nVertLevels')
ds = mpas.add_time(ds, dsIn)
ds.compute()
ds = mpas.remap(ds, mappingFileName)
with xarray.open_mfdataset(pslFileNames, concat_dim='time') as dsIn:
ds[VAR_NAME] = ds[VAR_NAME] + dsIn.PSL.values
mpas.setup_cmor(VAR_NAME, tables, user_input_path, component='ocean')
# create axes
axes = [{
'table_entry': 'time',
'units': ds.time.units
}, {
'table_entry': 'latitude',
'units': 'degrees_north',
'coord_vals': ds.lat.values,
'cell_bounds': ds.lat_bnds.values
}, {
'table_entry': 'longitude',
'units': 'degrees_east',
'coord_vals': ds.lon.values,
'cell_bounds': ds.lon_bnds.values
}]
try:
mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS)
except Exception:
return ""
return VAR_NAME
def handle(infiles, tables, user_input_path, **kwargs):
"""
Transform MPASO timeMonthly_avg_layerThickness into CMIP.zhalfo
Parameters
----------
infiles : dict
a dictionary with namelist, mesh and time series file names
tables : str
path to CMOR tables
user_input_path : str
path to user input json file
Returns
-------
varname : str
the name of the processed variable after processing is complete
"""
msg = 'Starting {name}'.format(name=__name__)
logging.info(msg)
meshFileName = infiles['MPAS_mesh']
mappingFileName = infiles['MPAS_map']
timeSeriesFiles = infiles['MPASO']
dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False)
_, cellMask3D = mpas.get_cell_masks(dsMesh)
variableList = [
'timeMonthly_avg_layerThickness', 'xtime_startMonthly',
'xtime_endMonthly'
]
nVertLevels = dsMesh.sizes['nVertLevels']
ds = xarray.Dataset()
with mpas.open_mfdataset(timeSeriesFiles, variableList) as dsIn:
layerThickness = dsIn.timeMonthly_avg_layerThickness
layerThickness = layerThickness.where(cellMask3D)
thicknessSum = layerThickness.sum(dim='nVertLevels')
mask = cellMask3D.isel(nVertLevels=0)
zSurface = (-dsMesh.bottomDepth + thicknessSum).where(mask)
zSurface.compute()
# print('done zSurface')
slices = [zSurface]
maskSlices = [mask]
zLayerBot = zSurface
for zIndex in range(nVertLevels):
mask = cellMask3D.isel(nVertLevels=zIndex)
zLayerBot = (zLayerBot -
layerThickness.isel(nVertLevels=zIndex)).where(mask)
zLayerBot.compute()
# print('done zLayerBot {}/{}'.format(zIndex+1, nVertLevels))
slices.append(zLayerBot)
maskSlices.append(mask)
ds[VAR_NAME] = xarray.concat(slices, dim='olevhalf')
mask = xarray.concat(maskSlices, dim='olevhalf')
ds = mpas.add_mask(ds, mask)
ds = ds.transpose('Time', 'olevhalf', 'nCells')
ds = mpas.add_time(ds, dsIn)
ds.compute()
ds = mpas.remap(ds, mappingFileName)
depth_coord_half = numpy.zeros(nVertLevels + 1)
depth_coord_half[1:] = dsMesh.refBottomDepth.values
mpas.setup_cmor(VAR_NAME, tables, user_input_path, component='ocean')
# create axes
axes = [{
'table_entry': 'time',
'units': ds.time.units
}, {
'table_entry': 'depth_coord_half',
'units': 'm',
'coord_vals': depth_coord_half
}, {
'table_entry': 'latitude',
'units': 'degrees_north',
'coord_vals': ds.lat.values,
'cell_bounds': ds.lat_bnds.values
}, {
'table_entry': 'longitude',
'units': 'degrees_east',
'coord_vals': ds.lon.values,
'cell_bounds': ds.lon_bnds.values
}]
try:
mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS)
except Exception:
return ""
return VAR_NAME
def handle(infiles, tables, user_input_path, **kwargs):
"""
Transform MPASO cellArea into CMIP.areacello
Parameters
----------
infiles : dict
a dictionary with namelist, mesh and time series file names
tables : str
path to CMOR tables
user_input_path : str
path to user input json file
Returns
-------
varname : str
the name of the processed variable after processing is complete
"""
if kwargs.get('simple'):
msg = f"{VAR_NAME} is not supported for simple conversion"
print_message(msg)
return
msg = 'Starting {name}'.format(name=__name__)
logging.info(msg)
meshFileName = infiles['MPAS_mesh']
mappingFileName = infiles['MPAS_map']
dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False)
earth_radius = dsMesh.attrs['sphere_radius']
cellMask2D, _ = mpas.get_cell_masks(dsMesh)
ds = xarray.Dataset()
ds[VAR_NAME] = ('nCells', cellMask2D.astype(float))
ds = mpas.remap(ds, mappingFileName)
# the result above is just a mask of area fraction. We need to multiply
# by the area on the output grid
dsMap = xarray.open_dataset(mappingFileName)
area_b = dsMap.area_b.values
dst_grid_dims = dsMap.dst_grid_dims.values
area_b = area_b.reshape((dst_grid_dims[1], dst_grid_dims[0]))
area_b = xarray.DataArray(data=area_b, dims=ds[VAR_NAME].dims)
# area_b is in square radians, so need to multiply by the earth_radius**2
ds[VAR_NAME] = earth_radius**2 * area_b * ds[VAR_NAME]
setup_cmor(var_name=VAR_NAME,
table_path=tables,
table_name=TABLE,
user_input_path=user_input_path)
# create axes
axes = [{
'table_entry': 'latitude',
'units': 'degrees_north',
'coord_vals': ds.lat.values,
'cell_bounds': ds.lat_bnds.values
}, {
'table_entry': 'longitude',
'units': 'degrees_east',
'coord_vals': ds.lon.values,
'cell_bounds': ds.lon_bnds.values
}]
try:
mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS)
except Exception:
return ""
return VAR_NAME