NEDAS.core.model module

class NEDAS.core.model.Model(context: Context | None = None, io_mode: Literal['online', 'offline'] | None = None, config_file: str | None = None, parse_args: bool = False, **kwargs)

Bases: Generic[GridT], ABC

Class for configuring and running a model

variables: dict[Annotated[str, 'variable name'], VarDesc]

grid: GridT

z: dict[Annotated[int, 'z level index'], ndarray]

mask: ndarray

ens_init_dir: str | None

truth_dir: str | None

ens_run_strategy: Literal['scheduler', 'batch']

nproc_per_run: int = 1

nproc_per_util: int = 1

walltime: int | None = None

run_process = None

run_status: str = 'pending'

restart_dir: str

forecast_period: int

memory: dict

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

model_name: str

property c: Context

parse_kwargs(kwargs: dict[str, Any]) → dict[str, Any]

get_mstr(member)

get_tstr(time)

abstractmethod read_grid(**kwargs) → None

Read the grid information from the model output.

Parameters:

**kwargs – Keyword arguments for reading the grid.

read_var(**kwargs) → ndarray

Read a variable from the model output.

Parameters:

**kwargs – Keyword arguments for reading the variable.

Returns:

The read variable.

Return type:

np.ndarray

read_var_from_file(**kwargs) → ndarray

read_var_from_memory(**kwargs)

write_var(var, **kwargs) → None

Write a variable to the model output.

Parameters:

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

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

write_var_to_file(var, **kwargs)

write_var_to_memory(var, **kwargs)

save_memory(tag: str, time: datetime | None = None, path: str | None = None) → None

load_memory(tag: str, time: datetime | None = None, path: str | None = None) → None

abstractmethod z_coords(**kwargs) → ndarray

Get the vertical coordinates of the model.

Parameters:

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

Returns:

The vertical coordinates.

Return type:

np.ndarray

abstractmethod preprocess(*args, **kwargs) → None

Preprocess the model data.

Parameters:

**kwargs – Keyword arguments for preprocessing.

abstractmethod postprocess(*args, **kwargs) → None

Postprocess the model data.

Parameters:

**kwargs – Keyword arguments for postprocessing.

abstractmethod run(*args, **kwargs) → None

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