Efficient Probabilistic Tuning of Ensemble Forecasting Method

Ensemble forecasting techniques are gaining popularity in the weather and renewable energy communities, thanks to their ability to produce accurate predictions and at the same time to provide a measure of the uncertainty in the forecast. Analog ensemble techniques are a class of computationally efficient ensemble forecasting methods that predict future weather events based on historical similar cases (i.e., analogs). The definition of "similar"is dependent on the type of predictors used for searching in the historical dataset, and on how relevant they are to identify a similar weather event happened in the past. For a given geographical location, the relevancy of a predictor in identifying good analogs requires a long tuning process usually performed via brute-force. Although highly parallelizable, the brute-force tuning approach becomes unfeasible when large datasets and/or many predictors are used, even on modern supercomputers equipped with powerful accelerators. In this work, we provide several probabilistic alternatives to the tuning process, based on the dataset size, computational cost of a single evaluation, and number of predictors.