NEDAS.models.topaz.v4.topaz4model module

class NEDAS.models.topaz.v4.topaz4model.Topaz4Model(**kwargs)

Bases: Model[RegularGrid]

io_mode: Literal['online', 'offline'] = 'offline'

basedir: str

R: str

T: str

E: str

V: str

X: str

onem: float

z_units: str

restart_dt: int

forcing_frc: str

era5_path: str

priver: int

jerlv0: int

relax: int

nproc: int

filename(**kwargs)

read_grid(**kwargs)

Read the grid information from the model output.

Parameters:

**kwargs – Keyword arguments for reading the grid.

read_mask()

read_var(**kwargs)

Read a variable from the model output.

Parameters:

**kwargs – Keyword arguments for reading the variable.

Returns:

The read variable.

Return type:

np.ndarray

write_var(var, **kwargs)

Write a variable to the model output.

Parameters:

• var (np.ndarray) – The variable to write.

• **kwargs – Keyword arguments for writing the variable.

z_coords(**kwargs) → ndarray

Calculate vertical coordinates given the 3D model state. :returns: The corresponding z field. :rtype: np.ndarray

preprocess(task_id=0, **kwargs)

Preprocess the model data.

Parameters:

**kwargs – Keyword arguments for preprocessing.

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.