improver.cli.apply_rainforests_calibration module
CLI to apply rainforests calibration.
- process(forecast, *features, model_config, output_thresholds=None, output_threshold_config=None, threshold_units=None, threads=1, bin_data=False)[source]
Calibrate a forecast cube using the Rainforests method.
Ensemble forecasts must be in realization representation. Deterministic forecasts can be processed to produce a pseudo-ensemble; a realization dimension will be added to deterministic forecast cubes if one is not already present.
This calibration is done in a situation dependent fashion using a series of decision-tree models to construct representative error distributions which are then used to map each input ensemble member onto an error distribution. The error distributions are averaged in probability space, and interpolated to the output thresholds.
It is assumed that the models have been trained using the >= comparator; i.e. they predict the probability that the error is greater than or equal to the various error thresholds. The output probability cube also uses the >= comparator.
- Parameters:
forecast_cube (iris.cube.Cube) – Cube containing the forecast to be calibrated; must be as realizations.
feature_cubes (iris.cube.Cubelist) – Cubelist containing the feature variables (physical parameters) used as inputs to the tree-models for the generation of the associated error distributions. Feature cubes are expected to have the same dimensions as forecast_cube, with the exception of the realization dimension. Where the feature_cube contains a realization dimension this is expected to be consistent, otherwise the cube will be broadcast along the realization dimension.
model_config (dict) – Dictionary containing RainForests model configuration data.
output_thresholds (list) – List of thresholds at which to evaluate output probabilities.
output_threshold_config (dict) – Threshold configuration dictionary where the keys are strings representing thresholds. The threshold config should follow the same format as that of the threshold cli, however here only the threshold keys are used and the threshold values are disregarded.
threshold_units (str) – Units in which threshold_values are specified. If not provided the units are assumed to be the same as those of the input cube. Specifying the units here will allow a suitable conversion to match the input units of forecast_cube.
threads (int) – Number of threads to use during prediction with tree-model objects.
bin_data (
bool) – Bin data according to splits used in models. This speeds up prediction if there are many data points which fall into the same bins for all threshold models. Limits the calculation of common feature values by only calculating them once.
- Returns:
The forecast cube following calibration.
- Return type: