NEDAS.models.vort2d.vort2d_model module

class NEDAS.models.vort2d.vort2d_model.Vort2DModel(**kwargs)

Bases: Model[RegularGrid]

nx: int

ny: int

dx: float

dt: float

restart_dt: float

Vmax: float

Rmw: float

Vbg: float

Vslope: float

loc_sprd: int

gen: float

diss: float

memory: dict = {}

filename(**kwargs)

read_grid(**kwargs)

Read the grid information from the model output.

Parameters:

**kwargs – Keyword arguments for reading the grid.

read_mask(**kwargs)

read_var_from_file(**kwargs)

write_var_to_file(var, **kwargs)

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

generate_initial_condition(loc_sprd=None)

preprocess(**kwargs)

Preprocess the model data.

Parameters:

**kwargs – Keyword arguments for preprocessing.

postprocess(*args, **kwargs)

Postprocess the model data.

Parameters:

**kwargs – Keyword arguments for postprocessing.

run(*args, **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.

generate_truth(*args, **kwargs) → None

Generate truth (nature run) model states. Use for running synthetic observation experiments.

generate_init_ensemble(*args, **kwargs) → None

Generate initial perturbed model states for ensemble forecasts.

Parameters:

• nens (int) – ensemble size

• **kwargs