Comparison of nonhomogeneous regression models for probabilistic wind speed forecasting

TL;DR

This paper compares three nonhomogeneous regression models, including GEV-based approaches, for probabilistic wind speed forecasting, demonstrating their calibration and sharpness with the regime switching method excelling in the upper tail.

Contribution

It introduces two GEV-based regression approaches and a regime switching model for wind speed forecasting, expanding beyond the traditional truncated normal model.

Findings

All models produced calibrated, sharp forecasts.

The regime switching approach performed best in the upper tail.

Models were tested on European wind speed data.

Abstract

In weather forecasting, nonhomogeneous regression is used to statistically postprocess forecast ensembles in order to obtain calibrated predictive distributions. For wind speed forecasts, the regression model is given by a truncated normal distribution where location and spread are derived from the ensemble. This paper proposes two alternative approaches which utilize the generalized extreme value (GEV) distribution. A direct alternative to the truncated normal regression is to apply a predictive distribution from the GEV family, while a regime switching approach based on the median of the forecast ensemble incorporates both distributions. In a case study on daily maximum wind speed over Germany with the forecast ensemble from the European Centre for Medium-Range Weather Forecasts, all three approaches provide calibrated and sharp predictive distributions with the regime switching approach showing the highest skill in the upper tail.