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calc_meridional_trsp.py
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calc_meridional_trsp.py
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"""
Module for computing meridional transport of quantities
"""
import numpy as np
import xarray as xr
# xarray compatibility
try:
from xarray.core.pycompat import OrderedDict
except ImportError:
from collections import OrderedDict
from .get_basin import get_basin_mask
from .ecco_utils import get_llc_grid
from ecco_v4_py import vector_calc
# Define constants
# These are chosen (for now) to match gcmfaces
METERS_CUBED_TO_SVERDRUPS = 10**-6
WATTS_TO_PETAWATTS = 10**-15
RHO_CONST = 1029
HEAT_CAPACITY = 4000
def calc_meridional_vol_trsp(ds,lat_vals,
basin_name=None,coords=None,grid=None):
"""Compute volumetric transport across latitude band in Sverdrups
Parameters
----------
ds : xarray Dataset
must contain UVELMASS,VVELMASS, drF, dyG, dxG
lat_vals : float or list
latitude value(s) specifying where to compute transport
basin_name : string, optional
denote ocean basin over which to compute streamfunction
If not specified, compute global quantity
see get_basin.get_available_basin_names for options
coords : xarray Dataset
separate dataset containing the coordinate information
YC, Z, drF, dyG, dxG, optionally maskW, maskS
grid : xgcm Grid object, optional
denotes LLC90 operations for xgcm, see ecco_utils.get_llc_grid
see also the [xgcm documentation](https://xgcm.readthedocs.io/en/latest/grid_topology.html)
Returns
-------
ds_out : xarray Dataset
Dataset with the following variables
'vol_trsp' :
meridional volume transport in Sv
with dimensions 'time' (if in given dataset) and 'lat'
'vol_trsp_z' :
meridional volume transport in Sv at each depth level
in the given input fields
dimensions: 'time' (if provided), 'lat', and 'k'
"""
coords = _parse_coords(ds,coords,['Z','YC','drF','dyG','dxG'])
x_vol = ds['UVELMASS'] * coords['drF'] * coords['dyG']
y_vol = ds['VVELMASS'] * coords['drF'] * coords['dxG']
# Computes salt transport in m^3/s at each depth level
ds_out = meridional_trsp_at_depth(x_vol,y_vol,
coords=coords,
lat_vals=lat_vals,
basin_name=basin_name,
grid=grid)
# Rename to useful data array name
ds_out = ds_out.rename({'trsp_z': 'vol_trsp_z'})
# Sum over depth for total transport
ds_out['vol_trsp'] = ds_out['vol_trsp_z'].sum('k')
# Convert both fields to Sv
for fld in ['vol_trsp','vol_trsp_z']:
ds_out[fld] = METERS_CUBED_TO_SVERDRUPS * ds_out[fld]
ds_out[fld].attrs['units'] = 'Sv'
return ds_out
def calc_meridional_heat_trsp(ds,lat_vals,
basin_name=None,coords=None,grid=None):
"""Compute heat transport across latitude band in Petwatts
see calc_meridional_vol_trsp for argument documentation.
The only differences are:
Parameters
----------
ds : xarray Dataset
must contain fields 'ADVx_TH','ADVy_TH','DFxE_TH','DFyE_TH'
coords : xarray Dataset, optional
in case coordinates are in a separate dataset
only needs field 'YC' and optionally maskW, maskS
Returns
-------
ds_out : xarray Dataset
Dataset with the following variables
'heat_trsp' :
meridional heat transport in PW
with dimensions 'time' (if in given dataset) and 'lat'
'heat_trsp_z' :
meridional heat transport in PW at each depth level
in the given input fields
dimensions: 'time' (if provided), 'lat', and 'k'
"""
coords = _parse_coords(ds,coords,['Z','YC'])
x_heat = ds['ADVx_TH'] + ds['DFxE_TH']
y_heat = ds['ADVy_TH'] + ds['DFyE_TH']
# Computes heat transport in degC * m^3/s at each depth level
ds_out = meridional_trsp_at_depth(x_heat,y_heat,
coords=coords,
lat_vals=lat_vals,
basin_name=basin_name,
grid=grid)
# Rename to useful data array name
ds_out = ds_out.rename({'trsp_z': 'heat_trsp_z'})
# Sum over depth for total transport
ds_out['heat_trsp'] = ds_out['heat_trsp_z'].sum('k')
# Convert both fields to PW
for fld in ['heat_trsp','heat_trsp_z']:
ds_out[fld] = WATTS_TO_PETAWATTS * RHO_CONST * HEAT_CAPACITY * ds_out[fld]
ds_out[fld].attrs['units'] = 'PW'
return ds_out
def calc_meridional_salt_trsp(ds,lat_vals,
basin_name=None,coords=None,grid=None):
"""Compute salt transport across latitude band in psu * Sv
see calc_meridional_vol_trsp for argument documentation.
The only differences are:
Parameters
----------
ds : xarray Dataset
must contain fields 'ADVx_SLT','ADVy_SLT','DFxE_SLT','DFyE_SLT'
coords : xarray Dataset, optional
in case coordinates are in a separate dataset
only needs field 'YC' and optionally maskW, maskS
Returns
-------
ds_out : xarray Dataset
Dataset with the following variables
'salt_trsp' :
meridional salt transport in psu*Sv
with dimensions 'time' (if in given dataset) and 'lat'
'salt_trsp_z' :
meridional salt transport in psu*Sv at each depth level
in the given input fields
dimensions: 'time' (if provided), 'lat', and 'k'
"""
coords = _parse_coords(ds,coords,['Z','YC'])
x_salt = ds['ADVx_SLT'] + ds['DFxE_SLT']
y_salt = ds['ADVy_SLT'] + ds['DFyE_SLT']
# Computes salt transport in psu * m^3/s at each depth level
ds_out = meridional_trsp_at_depth(x_salt,y_salt,
coords=coords,
lat_vals=lat_vals,
basin_name=basin_name,
grid=grid)
# Rename to useful data array name
ds_out = ds_out.rename({'trsp_z': 'salt_trsp_z'})
# Sum over depth for total transport
ds_out['salt_trsp'] = ds_out['salt_trsp_z'].sum('k')
# Convert both fields to psu.Sv
for fld in ['salt_trsp','salt_trsp_z']:
ds_out[fld] = METERS_CUBED_TO_SVERDRUPS * ds_out[fld]
ds_out[fld].attrs['units'] = 'psu.Sv'
return ds_out
# ---------------------------------------------------------------------
def meridional_trsp_at_depth(xfld, yfld, lat_vals, coords,
basin_name=None, grid=None, less_output=True):
"""
Compute transport of vector quantity at each depth level
across latitude(s) defined in lat_vals
Parameters
----------
xfld, yfld : xarray DataArray
3D spatial (+ time, optional) field at west and south grid cell edges
lat_vals : float or list
latitude value(s) specifying where to compute transport
coords : xarray Dataset
only needs YC, and optionally maskW, maskS (defining wet points)
basin_name : string, optional
denote ocean basin over which to compute streamfunction
If not specified, compute global quantity
see get_basin.get_available_basin_names for options
grid : xgcm Grid object, optional
denotes LLC90 operations for xgcm, see ecco_utils.get_llc_grid
see also the [xgcm documentation](https://xgcm.readthedocs.io/en/latest/grid_topology.html)
Returns
-------
ds_out : xarray Dataset
with the main variable
'trsp_z'
transport of vector quantity across denoted latitude band at
each depth level with dimensions 'time' (if in given dataset),
'k' (depth), and 'lat'
"""
if grid is None:
grid = get_llc_grid(coords)
# Initialize empty DataArray with coordinates and dims
ds_out = _initialize_trsp_data_array(coords, lat_vals)
# Get basin mask
maskW = coords['maskW'] if 'maskW' in coords else xr.ones_like(xfld)
maskS = coords['maskS'] if 'maskS' in coords else xr.ones_like(yfld)
if basin_name is not None:
maskW = get_basin_mask(basin_name,maskW)
maskS = get_basin_mask(basin_name,maskS)
# These sums are the same for all lats, therefore precompute to save
# time
tmp_x = xfld.where(maskW)
tmp_y = yfld.where(maskS)
for lat in ds_out.lat.values:
if not less_output:
print ('calculating transport for latitutde ', lat)
# Compute mask for particular latitude band
lat_maskW, lat_maskS = vector_calc.get_latitude_masks(lat, coords['YC'], grid)
# Sum horizontally
lat_trsp_x = (tmp_x * lat_maskW).sum(dim=['i_g','j','tile'])
lat_trsp_y = (tmp_y * lat_maskS).sum(dim=['i','j_g','tile'])
ds_out['trsp_z'].loc[{'lat':lat}] = lat_trsp_x + lat_trsp_y
return ds_out
def _initialize_trsp_data_array(coords, lat_vals):
"""Create an xarray DataArray with time, depth, and latitude dims
Parameters
----------
coords : xarray Dataset
contains LLC coordinates 'k' and (optionally) 'time'
lat_vals : int or array of ints
latitude value(s) rounded to the nearest degree
specifying where to compute transport
Returns
-------
ds_out : xarray Dataset
Dataset with the variables
'trsp_z'
zero-valued DataArray with time (optional),
depth, and latitude dimensions
'Z'
the original depth coordinate
"""
lat_vals = np.array(lat_vals) if isinstance(lat_vals,list) else lat_vals
lat_vals = np.array([lat_vals]) if np.isscalar(lat_vals) else lat_vals
lat_vals = xr.DataArray(lat_vals,coords={'lat':lat_vals},dims=('lat',))
xda = xr.zeros_like(coords['k']*lat_vals)
xda = xda if 'time' not in coords.dims else xda.broadcast_like(coords['time']).copy()
# Convert to dataset to add Z coordinate
xds = xda.to_dataset(name='trsp_z')
xds['Z'] = coords['Z']
xds = xds.set_coords('Z')
return xds
def _parse_coords(ds,coords,coordlist):
if coords is not None:
return coords
else:
for f in set(['maskW','maskS']).intersection(ds.reset_coords().keys()):
coordlist.append(f)
if 'time' in ds.dims:
coordlist.append('time')
dsout = ds[coordlist]
if 'domain' in ds.attrs:
dsout.attrs['domain'] = ds.attrs['domain']
return dsout