CategoricalEnsCombination {CSTools}

Make categorical forecast based on a multi-model forecast with potential for calibrate

Description

This function converts a multi-model ensemble forecast into a categorical forecast by giving the probability for each category. Different methods are available to combine the different ensemble forecasting models into probabilistic categorical forecasts.

See details in ?CST_CategoricalEnsCombination

Usage

CategoricalEnsCombination(fc, obs, cat.method, eval.method, amt.cat, ...)

Arguments

fc

A multi-dimensional array with named dimensions containing the seasonal forecast experiment data in the element named $data. The amount of forecasting models is equal to the size of the dataset dimension of the data array. The amount of members per model may be different. The size of the member dimension of the data array is equal to the maximum of the ensemble members among the models. Models with smaller ensemble sizes have residual indices of member dimension in the data array filled with NA values.

obs

A multidimensional array with named dimensions containing the observed data in the element named $data.

cat.method

Method used to produce the categorical forecast, can be either pool, comb, mmw or obs. The method pool assumes equal weight for all ensemble members while the method comb assumes equal weight for each model. The weighting method is descirbed in Rajagopalan et al. (2002), Robertson et al. (2004) and Van Schaeybroeck and Vannitsem (2019). Finally, the obs method classifies the observations into the different categories and therefore contains only 0 and 1 values.

eval.method

Is the sampling method used, can be either "in-sample" or "leave-one-out". Default value is the "leave-one-out" cross validation.

amt.cat

Is the amount of categories. Equally-sized quantiles will be calculated based on the amount of categories.

...

Other parameters to be passed on to the calibration procedure.

Value

An array containing the categorical forecasts in the element called $data. The first two dimensions of the returned object are named dataset and member and are both of size one. An additional dimension named category is introduced and is of size amt.cat.

Author(s)

Bert Van Schaeybroeck, [e-mail]

References

Rajagopalan, B., Lall, U., & Zebiak, S. E. (2002). Categorical climate forecasts through regularization and optimal combination of multiple GCM ensembles. Monthly Weather Review, 130(7), 1792-1811.

Robertson, A. W., Lall, U., Zebiak, S. E., & Goddard, L. (2004). Improved combination of multiple atmospheric GCM ensembles for seasonal prediction. Monthly Weather Review, 132(12), 2732-2744.

Van Schaeybroeck, B., & Vannitsem, S. (2019). Postprocessing of Long-Range Forecasts. In Statistical Postprocessing of Ensemble Forecasts (pp. 267-290).