weatherbench2.config.Eval

class weatherbench2.config.Eval(metrics, regions=None, evaluate_persistence=False, evaluate_climatology=False, evaluate_probabilistic_climatology=False, probabilistic_climatology_start_year=None, probabilistic_climatology_end_year=None, probabilistic_climatology_hour_interval=None, against_analysis=False, derived_variables=<factory>, temporal_mean=True, output_format='netcdf')

Evaluation configuration class.

Parameters:
  • metrics (Dict[str, Metric]) –

  • regions (Optional[Dict[str, Union[Region, ExtraTropicalRegion, SliceRegion, LandRegion]]]) –

  • evaluate_persistence (Optional[bool]) –

  • evaluate_climatology (Optional[bool]) –

  • evaluate_probabilistic_climatology (Optional[bool]) –

  • probabilistic_climatology_start_year (Optional[int]) –

  • probabilistic_climatology_end_year (Optional[int]) –

  • probabilistic_climatology_hour_interval (Optional[int]) –

  • against_analysis (Optional[bool]) –

  • derived_variables (Dict[str, DerivedVariable]) –

  • temporal_mean (Optional[bool]) –

  • output_format (str) –

metrics

Dictionary of Metric instances.

Type:

Dict[str, weatherbench2.metrics.Metric]

regions

Optional dictionary of Region instances.

Type:

Optional[Dict[str, Union[weatherbench2.regions.Region, weatherbench2.regions.ExtraTropicalRegion, weatherbench2.regions.SliceRegion, weatherbench2.regions.LandRegion]]]

evaluate_persistence

Evaluate persistence forecast, i.e. forecast at t=0.

Type:

Optional[bool]

evaluate_climatology

Evaluate climatology forecast.

Type:

Optional[bool]

evaluate_probabilistic_climatology

Evaluate probabilistic climatology, derived from using each year of the ground-truth dataset as a member.

Type:

Optional[bool]

probabilistic_climatology_start_year

First year of ground-truth to use for probabilistic climatology.

Type:

Optional[int]

probabilistic_climatology_end_year

Last year of ground-truth to use for probabilistic climatology.

Type:

Optional[int]

probabilistic_climatology_hour_interval

Hour interval to compute probabilistic climatology.

Type:

Optional[int]

against_analysis

Use forecast at t=0 as ground-truth. Warning: only for by-valid convention. For by-init, specify analysis dataset as obs.

Type:

Optional[bool]

derived_variables

dict of DerivedVariable instances to compute on the fly.

Type:

Dict[str, weatherbench2.derived_variables.DerivedVariable]

temporal_mean

Compute temporal mean (over time/init_time) for metrics.

Type:

Optional[bool]

output_format

Wether to save to ‘netcdf’ or ‘zarr’.

Type:

str

__init__(metrics, regions=None, evaluate_persistence=False, evaluate_climatology=False, evaluate_probabilistic_climatology=False, probabilistic_climatology_start_year=None, probabilistic_climatology_end_year=None, probabilistic_climatology_hour_interval=None, against_analysis=False, derived_variables=<factory>, temporal_mean=True, output_format='netcdf')
Parameters:
  • metrics (Dict[str, Metric]) –

  • regions (Optional[Dict[str, Union[Region, ExtraTropicalRegion, SliceRegion, LandRegion]]]) –

  • evaluate_persistence (Optional[bool]) –

  • evaluate_climatology (Optional[bool]) –

  • evaluate_probabilistic_climatology (Optional[bool]) –

  • probabilistic_climatology_start_year (Optional[int]) –

  • probabilistic_climatology_end_year (Optional[int]) –

  • probabilistic_climatology_hour_interval (Optional[int]) –

  • against_analysis (Optional[bool]) –

  • derived_variables (Dict[str, DerivedVariable]) –

  • temporal_mean (Optional[bool]) –

  • output_format (str) –

Return type:

None

Methods

__init__(metrics[, regions, ...])

Attributes

against_analysis

evaluate_climatology

evaluate_persistence

evaluate_probabilistic_climatology

output_format

probabilistic_climatology_end_year

probabilistic_climatology_hour_interval

probabilistic_climatology_start_year

regions

temporal_mean

metrics

derived_variables