veriflow.scores.probabilistic#

Probabilistic verification scores.

For verification of probabilistic and ensemble forecasts, and probabilistic historical simulations of continuous variables.

For reference, see: https://scores.readthedocs.io/en/stable/included.html#probability

Classes

`CrpsCDF`(config)	Implementation for CRPS for probabilistic forecasts, expressed as cdf.
`CrpsCDFConfig`(*[, reduce_dims, ...])	Configuration for CRPS for CDF.
`CrpsForEnsemble`(config)	Implementation for CRPS for an ensemble.
`CrpsForEnsembleConfig`(*[, reduce_dims, ...])	Configuration for CRPS for ensemble.
`RankHistogram`(config)	Compute the rank histogram (Talagrand diagram) over the specified dimensions.
`RankHistogramConfig`(*[, reduce_dims, ...])	A rank histogram config element.

class veriflow.scores.probabilistic.CrpsCDF(config)[source]#

Implementation for CRPS for probabilistic forecasts, expressed as cdf.

Parameters:: config (CrpsCDFConfig)

kind: str = 'crps_cdf'#

config_class#: alias of CrpsCDFConfig

supported_data_types: ClassVar[set[DataType]] = {DataType.simulated_forecast_probabilistic}#

config: CrpsCDFConfig#

compute(obs, sim)[source]#

Compute the CRPS for an ensemble of forecasts and observations.

Parameters:

obs (DataArray)
sim (DataArray)

Return type:

DataArray | Dataset

classmethod from_config(raw_config)#

Initialize class from config dict.

Parameters:: raw_config (dict[str, Any])
Return type:: Self

validate_and_compute(obs, sim)#

Validate and compute.

Parameters:

obs (DataArray)
sim (DataArray)

Return type:

DataArray | Dataset

class veriflow.scores.probabilistic.CrpsCDFConfig(*, reduce_dims=<factory>, score_adapter, general, verification_pair_ids=[], integration_method='exact', **extra_data)[source]#

Configuration for CRPS for CDF.

For reference, see: https://scores.readthedocs.io/en/stable/api.html#scores.probability.crps_cdf

Parameters:

reduce_dims (list[Literal[StandardDim.station, StandardDim.forecast_reference_time, StandardDim.lead_time]])
score_adapter (Literal[ScoreKind.crps_cdf])
general (Annotated[GeneralInfoConfig, SkipJsonSchema()])
verification_pair_ids (list[str])
integration_method (Literal['exact', 'trapz'])
extra_data (Any)

score_adapter: Literal[ScoreKind.crps_cdf]#

integration_method: Annotated[Literal['exact', 'trapz'], FieldInfo(annotation=NoneType, required=True, description="The method of integration. 'exact' computes the exact integral, 'trapz' uses a trapezoidal rule and is an approximation of the CRPS.")]#

property lead_times: LeadTimes | None#

property preserve_dims: list[StandardDim]#: The dimensions to preserve.

verification_pair_ids_valid()#

Check provided filter for verification pairs contains valid ids.

Return type:: Self

property verification_pairs: list[VerificationPair]#

The configured variable pairs.

If the verification_pairs element is configured for the score, filter only these ids from the verification_pairs defined in general config.

general#

verification_pair_ids#

reduce_dims#

class veriflow.scores.probabilistic.CrpsForEnsemble(config)[source]#

Implementation for CRPS for an ensemble.

Parameters:: config (CrpsForEnsembleConfig)

kind: str = 'crps_for_ensemble'#

config_class#: alias of CrpsForEnsembleConfig

supported_data_types: ClassVar[set[DataType]] = {DataType.simulated_forecast_ensemble}#

config: CrpsForEnsembleConfig#

compute(obs, sim)[source]#

Compute the CRPS for an ensemble of forecasts and observations.

Parameters:

obs (DataArray)
sim (DataArray)

Return type:

Dataset | DataArray

classmethod from_config(raw_config)#

Initialize class from config dict.

Parameters:: raw_config (dict[str, Any])
Return type:: Self

validate_and_compute(obs, sim)#

Validate and compute.

Parameters:

obs (DataArray)
sim (DataArray)

Return type:

DataArray | Dataset

class veriflow.scores.probabilistic.CrpsForEnsembleConfig(*, reduce_dims=<factory>, score_adapter, general, verification_pair_ids=[], method='ecdf', **extra_data)[source]#

Configuration for CRPS for ensemble.

For reference, see: See: https://scores.readthedocs.io/en/stable/api.html#scores.probability.crps_for_ensemble

Parameters:

reduce_dims (list[Literal[StandardDim.station, StandardDim.forecast_reference_time, StandardDim.lead_time]])
score_adapter (Literal[ScoreKind.crps_for_ensemble])
general (Annotated[GeneralInfoConfig, SkipJsonSchema()])
verification_pair_ids (list[str])
method (Literal['ecdf', 'fair'])
extra_data (Any)

score_adapter: Literal[ScoreKind.crps_for_ensemble]#

method: Annotated[Literal['ecdf', 'fair'], FieldInfo(annotation=NoneType, required=False, default='ecdf', description='Method to compute the cumulative distribution function from an ensemble.')]#

property lead_times: LeadTimes | None#

property preserve_dims: list[StandardDim]#: The dimensions to preserve.

verification_pair_ids_valid()#

Check provided filter for verification pairs contains valid ids.

Return type:: Self

property verification_pairs: list[VerificationPair]#

The configured variable pairs.

If the verification_pairs element is configured for the score, filter only these ids from the verification_pairs defined in general config.

general#

verification_pair_ids#

reduce_dims#

class veriflow.scores.probabilistic.RankHistogram(config)[source]#

Compute the rank histogram (Talagrand diagram) over the specified dimensions.

For external documentation, see below: https://xskillscore.readthedocs.io/en/stable/api/xskillscore.rank_histogram.html?highlight=rank%20histogram#xskillscore.rank_histogram

Parameters:: config (RankHistogramConfig)

kind: str = 'rank_histogram'#

config_class#: alias of RankHistogramConfig

supported_data_types: ClassVar[set[DataType]] = {DataType.simulated_forecast_ensemble}#

config: RankHistogramConfig#

compute(obs, sim)[source]#

Compute the histogram of ranks over the specified dimensions.

Parameters:

obs (DataArray)
sim (DataArray)

Return type:

DataArray | Dataset

classmethod from_config(raw_config)#

Initialize class from config dict.

Parameters:: raw_config (dict[str, Any])
Return type:: Self

validate_and_compute(obs, sim)#

Validate and compute.

Parameters:

obs (DataArray)
sim (DataArray)

Return type:

DataArray | Dataset

class veriflow.scores.probabilistic.RankHistogramConfig(*, reduce_dims=<factory>, score_adapter, general, verification_pair_ids=[], **extra_data)[source]#

A rank histogram config element.

Parameters:

reduce_dims (list[Literal[StandardDim.station, StandardDim.forecast_reference_time, StandardDim.lead_time]])
score_adapter (Literal[ScoreKind.rank_histogram])
general (Annotated[GeneralInfoConfig, SkipJsonSchema()])
verification_pair_ids (list[str])
extra_data (Any)

score_adapter: Literal[ScoreKind.rank_histogram]#

property lead_times: LeadTimes | None#

property preserve_dims: list[StandardDim]#: The dimensions to preserve.

verification_pair_ids_valid()#

Check provided filter for verification pairs contains valid ids.

Return type:: Self

property verification_pairs: list[VerificationPair]#

The configured variable pairs.

If the verification_pairs element is configured for the score, filter only these ids from the verification_pairs defined in general config.

general#

verification_pair_ids#

reduce_dims#