improver.cli.calculate_forecast_bias module
CLI to calculate the bias values from the specified set of reference forecasts.
- process(*cubes, truth_attribute)[source]
Calculate forecast bias from the specified set of historical forecasts and truth values.
The historical forecasts are expected to be representative single-valued forecasts (eg. control or ensemble mean forecast).
The bias values are evaluated point-by-point and the associated bias cube will retain the same spatial dimensions as the input cubes. By using a point-by-point approach, the bias-correction enables a form of statistical downscaling where coherent biases exist between a coarse forecast dataset and finer truth dataset.
Where multiple forecasts values are provided, the value returned is the mean value over the set of forecast/truth pairs.
- Parameters:
cubes (list of iris.cube.Cube) – A list of cubes containing the historical forecasts and corresponding truths used for calibration. The cubes must include the same diagnostic name in their names. The cubes will be distinguished using the user specified truth attribute.
truth_attribute (str) – An attribute and its value in the format of “attribute=value”, which must be present on truth cubes.
- Returns:
Cube containing forecast bias values evaluated over the specified set of historical forecasts.
- Return type: