geocat.comp.moc_globe_atl
geocat.comp.moc_globe_atl#
- geocat.comp.moc_globe_atl(lat_aux_grid, a_wvel, a_bolus, a_submeso, t_lat, rmlak, msg=None, meta=False)#
Facilitates calculating the meridional overturning circulation for the globe and Atlantic.
Args:
- lat_aux_grid
xarray.DataArray
,numpy.ndarray
: Latitude grid for transport diagnostics.
- a_wvel
xarray.DataArray
,numpy.ndarray
: Area weighted Eulerian-mean vertical velocity [TAREA*WVEL].
- a_bolus
xarray.DataArray
,numpy.ndarray
: Area weighted Eddy-induced (bolus) vertical velocity [TAREA*WISOP].
- a_submeso
xarray.DataArray
,numpy.ndarray
: Area weighted submeso vertical velocity [TAREA*WSUBM].
- tlat
xarray.DataArray
,numpy.ndarray
: Array of t-grid latitudes.
- rmlak
xarray.DataArray
,numpy.ndarray
: Basin index number: [0]=Globe, [1]=Atlantic
- msg
numpy.number
: A numpy scalar value that represent a missing value. This argument allows a user to use a missing value scheme other than NaN or masked arrays, similar to what NCL allows.
- meta
bool
: If set to True and the input array is an Xarray, the metadata from the input array will be copied to the output array; default is False. Warning: this option is not currently supported.
- Returns
fo (
xarray.DataArray
,numpy.ndarray
:) – A multi-dimensional array of size [moc_comp] x [n_transport_reg] x [kdepth] x [nyaux] where:moc_comp refers to the three components returned
n_transport_reg refers to the Globe and Atlantic
kdepth is the the number of vertical levels of the work arrays
nyaux is the size of the lat_aux_grid
Examples
# Input data can be read from a data set as follows: import xarray as xr
ds = xr.open_dataset(“input.nc”)
lat_aux_grid = ds.lat_aux_grid a_wvel = ds.a_wvel a_bolus = ds.a_bolus a_submeso = ds.a_submeso tlat = ds.tlat rmlak = ds.rmlak
# (1) Calling with xArray inputs and default arguments (Missing value = np.nan, NO meta information) out_arr = moc_globe_atl(lat_aux_grid, a_wvel, a_bolus, a_submeso, tlat, rmlak)
# (2) Calling with Numpy inputs and default arguments (Missing value = np.nan, NO meta information) out_arr = moc_globe_atl(lat_aux_grid.values, a_wvel.values, a_bolus.values, a_submeso.values,
tlat.values, rmlak.values)
# (3) Calling with xArray inputs and user-defined arguments (Missing value = np.nan, NO meta information) out_arr = moc_globe_atl(lat_aux_grid, a_wvel, a_bolus, a_submeso, tlat, rmlak, msg=-99.0, meta=True)
# (4) Calling with Numpy inputs and user-defined arguments (Missing value = np.nan, NO meta information) out_arr = moc_globe_atl(lat_aux_grid.values, a_wvel.values, a_bolus.values, a_submeso.values,
tlat.values, rmlak.values, msg=-99.0, meta=True)
- lat_aux_grid