NEDAS.models.lorenz96.lorenz96_model module

class NEDAS.models.lorenz96.lorenz96_model.Lorenz96Model(**kwargs)

Bases: Model[Grid1D]

Lorenz 1996 model with 40 variables

Parameters:

• nx (int) – dimension of the model, default is 40.

• F (float) – forcing parameter, default is 8

• dt (float) – model time step, default is 0.05

• numeric_opt (str) – numeric option, default is ‘rk4’

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

nx: int

F: float

dt: float

restart_dt: float

numeric_opt: str

memory: dict = {}

dxdt(x)

run_1step_euler_forward(x)

run_1step_rk4(x)

advance_time(x_in, T)

Nonlinear advance_time function

Parameters:

• x (np.ndarray) – the initial condition

• T (float) – duration of the simulation

Returns:

the updated model state after simulation

Return type:

np.ndarray

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()

preprocess(*args, **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)

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

generate_init_ensemble(*args, **kwargs)

Generate initial perturbed model states for ensemble forecasts.

Parameters:

• nens (int) – ensemble size

• **kwargs