Mixture EMOS model for calibrating ensemble forecasts of wind speed

TL;DR

This paper introduces a new mixture EMOS model for wind speed forecast calibration, combining truncated normal and log-normal distributions, which improves probabilistic calibration and forecast accuracy over existing methods.

Contribution

The paper proposes a novel mixture EMOS model using weighted TN and LN distributions, enhancing calibration and flexibility in wind speed forecast post-processing.

Findings

Mixture EMOS outperforms single distribution EMOS models.

The model provides better calibration than raw ensembles and climatology.

It avoids covariate selection issues present in other models.

Abstract

Ensemble model output statistics (EMOS) is a statistical tool for post-processing forecast ensembles of weather variables obtained from multiple runs of numerical weather prediction models in order to produce calibrated predictive probability density functions (PDFs). The EMOS predictive PDF is given by a parametric distribution with parameters depending on the ensemble forecasts. We propose an EMOS model for calibrating wind speed forecasts based on weighted mixtures of truncated normal (TN) and log-normal (LN) distributions where model parameters and component weights are estimated by optimizing the values of proper scoring rules over a rolling training period. The new model is tested on wind speed forecasts of the 50 member European Centre for Medium-Range Weather Forecasts ensemble, the 11 member Aire Limit\'ee Adaptation dynamique D\'eveloppement International-Hungary Ensemble Prediction System ensemble of the Hungarian Meteorological Service and the eight-member University of Washington mesoscale ensemble, and its predictive performance is compared to that of various benchmark EMOS models based on single parametric families and combinations thereof. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison with the raw ensemble and climatological forecasts. The mixture EMOS model significantly outperforms the TN and LN EMOS methods, moreover, it provides better calibrated forecasts than the TN-LN combination model and offers an increased flexibility while avoiding covariate selection problems.