Log-normal distribution based EMOS models for probabilistic wind speed forecasting

TL;DR

This paper introduces log-normal based EMOS models for wind speed forecast calibration, demonstrating improved probabilistic calibration and point forecast accuracy over traditional methods across multiple ensemble datasets.

Contribution

It proposes a novel EMOS model using the log-normal distribution and a regime-switching extension combining truncated normal and log-normal distributions for wind speed forecasting.

Findings

Log-normal EMOS models improve calibration over raw ensembles.

The regime-switching model outperforms traditional EMOS methods.

The models achieve comparable performance to GEV-based methods without negative values.

Abstract

Ensembles of forecasts are obtained from multiple runs of numerical weather forecasting models with different initial conditions and typically employed to account for forecast uncertainties. However, biases and dispersion errors often occur in forecast ensembles, they are usually under-dispersive and uncalibrated and require statistical post-processing. We present an Ensemble Model Output Statistics (EMOS) method for calibration of wind speed forecasts based on the log-normal (LN) distribution, and we also show a regime-switching extension of the model which combines the previously studied truncated normal (TN) distribution with the LN. Both presented models are applied to wind speed forecasts of the eight-member University of Washington mesoscale ensemble, of the fifty-member ECMWF ensemble and of the eleven-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service, and their predictive performances are compared to those of the TN and general extreme value (GEV) distribution based EMOS methods and to the TN-GEV mixture model. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison to the raw ensemble and to climatological forecasts. Further, the TN-LN mixture model outperforms the traditional TN method and its predictive performance is able to keep up with the models utilizing the GEV distribution without assigning mass to negative values.