NEDAS.models.nextsim.dg.nextsimdg_model module

class NEDAS.models.nextsim.dg.nextsimdg_model.NextsimDGModel(**kwargs)

Bases: Model[RegularGrid]

restart_dt: float

dg_comp: int

proj: str

xstart: float

xend: float

ystart: float

yend: float

dx: float

mask_file: str

files: dict

perturb: dict

model_env: str

model_config_file: str

parallel_mode: Literal['serial', 'mpi', 'openmp']

run_separate_jobs: bool

use_job_array: bool

grid: RegularGrid

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)

Get the vertical coordinates of the model.

Parameters:

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

Returns:

The vertical coordinates.

Return type:

np.ndarray

get_seaice_conc(**kwargs)

get_seaice_thick(**kwargs)

preprocess(task_id: int = 0, **kwargs)

Preprocessing method for nextsim.dg.

Parameters:

• task_id (int) – task id for parallel execution

• nproc_per_util (int) – number of processors for each task

• kwargs (dict) – Runtime keyword arguments (member, time, time_start, path, forecast_period, restart_dir). Additional class attributes self.files and self.perturb are populated from the model_def nextsim.dg config_file entry by parse_config.

postprocess(task_id=0, **kwargs)

Postprocessing method for nextsim.dg Parameters: same as preprocess

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.

run_single(task_id=0, **kwargs)

Run nextsim.dg model forecast

run_batch(task_id=0, **kwargs)

Run nextsim.dg model ensemble forecast, use job array to spawn the member runs

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