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Power ramps are sudden changes in turbine power and must be accurately predicted to minimize costly imbalances in the electrical grid. Doing so requires reliable wind speed forecasts, which can be obtained from ensembles of physical numerical weather prediction (NWP) models through statistical postprocessing.

Introduction of distributional regression trees and random forests for circular responses, yielding probabilistic forecasts based on the von Mises distribution. The proposed methodology is evaluated in a case study on probabilistic wind direction forecasting at two Austrian airports, considering other common approaches as a benchmark.

Comparison of various time-adaptive training schemes with the classical sliding training window approach for the application of post-processing near-surface air temperature forecasts across Central Europe. The results show that time-adaptive training schemes using data over multiple years stabilize the temporal evolution of the coefficient estimates, yielding an increased predictive performance for all station types tested compared to the classical sliding-window approach based on the most recent days only.