NEDAS.models.wrf.wrf_model module

class NEDAS.models.wrf.wrf_model.WRFModel(**kwargs)[source]

Bases: Model[RegularGrid]

map_proj: str
ref_lat: float
ref_lon: float
truelat1: float
truelat2: float
max_dom: int
e_we: list[int]
e_sn: list[int]
e_vert: list[int]
dx: list[float]
dy: list[float]
model_code_dir: str
z_units: str
restart_dt: float
filename(**kwargs)[source]
read_grid(**kwargs)[source]

Read the grid information from the model output.

Parameters:

**kwargs – Keyword arguments for reading the grid.

read_var(**kwargs)[source]

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)[source]

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)[source]

Get the vertical coordinates of the model.

Parameters:

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

Returns:

The vertical coordinates.

Return type:

np.ndarray

preprocess(task_id=0, **kwargs)[source]

Preprocess the model data.

Parameters:

**kwargs – Keyword arguments for preprocessing.

postprocess(task_id=0, **kwargs)[source]

Postprocess the model data.

Parameters:

**kwargs – Keyword arguments for postprocessing.

run(task_id=0, **kwargs)[source]

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.