Enhancing High Wind Speed Event Prediction through Adaptive Analog Ensemble Sizing

The Croatian Meteorological and Hydrological Service (DHMZ) has a long experience in post-processing wind speed and wind gust variables. One of the most used methods at DHMZ is the analog method. Although notable improvements in wind speed forecasts have been observed, one of the disadvantages of the analog method is its accuracy in forecasting high wind speed episodes, especially when using an analog ensemble for deterministic post-processing (HRAN). Since onshore parts of Croatia regularly experience violent winds several times a year, leading to disruptions in electricity production and traffic, accurate high wind speed forecasts hold significant importance. 

 

This study investigates the impact of reducing the analog ensemble size on HRAN accuracy. Analysing wind speed forecasts from onshore locations in Croatia, we compare raw model output and HRAN with predictor weight optimisation. In the analysis, we use ALADIN-HR NWP, tuned for wind across Croatian territory, providing us with forecasts 72 hours ahead. Utilising measures for continuous forecast verification, the overall forecast quality is inspected, while the categorical approach is used for examining forecast performances for more extreme events. Generally, the highest enhancements are achieved with an analog ensemble size of 15 members. Since high winds are rare, compared to low and moderate winds, employing larger ensembles often results in overall error reduction. However, our study shows how fewer than 15 ensemble members can provide more favourable results for the highest wind categories, causing less pronounced underestimations. Thus, varying ensemble size might be an optimal way to address these issues. In the ongoing work, besides wind speed forecasts, the analog method is also used to improve visibility forecasts, focusing on low-visibility events often caused by fog.