Truncated generalized extreme value distribution based EMOS model for calibration of wind speed ensemble forecasts

TL;DR

This paper introduces a new statistical post-processing model for wind speed ensemble forecasts using a truncated generalized extreme value distribution, improving forecast accuracy by preventing negative wind speed predictions.

Contribution

A novel EMOS model employing a truncated GEV distribution for wind speed calibration, addressing negative forecast issues and enhancing predictive performance.

Findings

TGEV EMOS outperforms traditional models in forecast skill.

The model effectively prevents negative wind speed predictions.

Results are consistent across multiple datasets and prediction systems.

Abstract

In recent years, ensemble weather forecasting have become a routine at all major weather prediction centres. These forecasts are obtained from multiple runs of numerical weather prediction models with different initial conditions or model parametrizations. However, ensemble forecasts can often be underdispersive and also biased, so some kind of post-processing is needed to account for these deficiencies. One of the most popular state of the art statistical post-processing techniques is the ensemble model output statistics (EMOS), which provides a full predictive distribution of the studied weather quantity. We propose a novel EMOS model for calibrating wind speed ensemble forecasts, where the predictive distribution is a generalized extreme value (GEV) distribution left truncated at zero (TGEV). The truncation corrects the disadvantage of the GEV distribution based EMOS models of occasionally predicting negative wind speed values, without affecting its favorable properties. The new model is tested on four data sets of wind speed ensemble forecasts provided by three different ensemble prediction systems, covering various geographical domains and time periods. The forecast skill of the TGEV EMOS model is compared with the predictive performance of the truncated normal, log-normal and GEV methods and the raw and climatological forecasts as well. The results verify the advantageous properties of the novel TGEV EMOS approach.