Probabilistic wind speed forecasting in Hungary

TL;DR

This paper applies Bayesian Model Averaging to calibrate and improve the accuracy of wind speed ensemble forecasts from Hungary's operational weather prediction system, addressing under-dispersion and calibration issues.

Contribution

The study introduces two BMA models tailored for wind speed data and demonstrates their effectiveness in enhancing forecast calibration and precision.

Findings

BMA significantly improves forecast calibration.

Post-processing reduces under-dispersion.

Forecast accuracy is notably enhanced.

Abstract

Prediction of various weather quantities is mostly based on deterministic numerical weather forecasting models. Multiple runs of these models with different initial conditions result ensembles of forecasts which are applied for estimating the distribution of future weather quantities. However, the ensembles are usually under-dispersive and uncalibrated, so post-processing is required. In the present work Bayesian Model Averaging (BMA) is applied for calibrating ensembles of wind speed forecasts produced by the operational Limited Area Model Ensemble Prediction System of the Hungarian Meteorological Service (HMS). We describe two possible BMA models for wind speed data of the HMS and show that BMA post-processing significantly improves the calibration and precision of forecasts.