NEDAS.models.nextsim.v2.nextsim_model module

class NEDAS.models.nextsim.v2.nextsim_model.NextsimModel(**kwargs)

Bases: Model

Class for configuring and running the neXtSIM v2 model (lagrangian version)

nextsim_dir: str

model_env: str

nextsim_mesh_dir: str

msh_filename: str

nextsim_data_dir: str

ocean_forcing_path: str

restart_input_path: str

restart_dt: float

atmos_forcing_path: str

forcing_dt: float

filename(**kwargs)

Get the filename with specified variable name, time, member, etc.

read_grid_from_mshfile(mshfile)

Read mshfile and update the self.grid object

read_grid(**kwargs)

Update self.grid object based on input kwargs

write_grid(**kwargs)

write updated mesh back to mesh file

Note: now we assume that number of mesh elements and their indices doesn’t change! only updating the mesh node position x,y

get_boundary_nodes()

get_neighbor_nodes(nodes)

taper_boundary(fld, depth=5)

displace(u, v, **kwargs)

Nextsim has a Lagrangian mesh, so it’s possible to displace the mesh coordinates directly Inputs: - u, v: displacement vectors defined on self.grid.x,y

read_mask(**kwargs)

prepare_mask(dst_grid)

read_var(**kwargs)

read variable from a model restart file

write_var(var, **kwargs)

write variable back to a model restart file

z_coords(**kwargs)

Get the vertical coordinates of the model.

Parameters:

**kwargs – Keyword arguments for getting the vertical coordinates.

Returns:

The vertical coordinates.

Return type:

np.ndarray

get_seaice_drift(**kwargs)

get_diag_var(**kwargs)

read_param(**kwargs)

Read a scalar parameter from the model.

Default implementation reads from self.memory[‘param’][mstr][name], falling back to the on-disk param store in offline mode (see write_param), then to getattr(self, name) if nowhere else.

Parameters:

• name (str) – parameter name

• member (int|None) – ensemble member index

Returns:

the parameter value

Return type:

float

write_param(value, **kwargs)

Write a scalar parameter to the model.

Default implementation stores in self.memory[‘param’][mstr][name] and, in offline mode, also persists to an on-disk per-member store so the value survives across the per-step/per-member subprocesses (see _param_file).

Parameters:

• value (float) – the parameter value to write

• name (str) – parameter name

• member (int|None) – ensemble member index

preprocess(task_id=0, **kwargs)

Preprocess the dir, collect input files for model run

postprocess(task_id=0, **kwargs)

Postprocess the model data.

Parameters:

**kwargs – Keyword arguments for postprocessing.

run(task_id=0, **kwargs)

Run the model forward in time.

Parameters:

• *args – Arguments

• **kwargs – Keyword arguments

Keyword Arguments:

• time (datetime) – current time when forecast starts

• restart_dir (str) – directory where restart files are located

• forecast_period (int) – forecast period in hours

If self.ens_run_strategy == ‘batch’, the method will run all ensemble members in one go, expect additional kwargs[‘nens’] to be the ensemble size. If self.ens_run_strategy == ‘scheduler’, the method runs a single member indexed by kwargs[‘member’], and kwargs[‘worker_id’] is the pid assigned by the scheduler to run this method.