Probabilistic Wind Gust Forecasting during the 2022 Beijing Winter Olympics

The probabilistic forecast of wind gusts poses a significant challenge during the post-processing of numerical model outputs.Comparative analysis of probabilistic forecasting methods plays a crucial role in enhancing forecast accuracy.Within the context of meteorological services for alpine skiing at the 2022 Beijing Winter Olympics,The ECMWF ensemble products were used to evaluate six post-processing methods.These methods include ensemble model output statistics(EMOS),backpropagation neural networks(BP),particle swarm optimization algorithms with backpropagation neural networks(PSO),truncated normal distributions,truncated logarithmic distributions,and generalized extreme value(GEV) distributions.The performance of these methods in predicting gust probabilities at five observation points along a ski track was compared.All six methods exhibited a substantial reduction in forecast errors compared to the original ECMWF products;however,the ability to correct the model forecast results varied significantly across different wind speed ranges.Specifically,the EMOS,truncated normal distribution,truncated logarithmic distribution,and GEV distribution demonstrated advantages in low wind-speed ranges,whereas the BP and PSO methods exhibit lower forecast errors for high wind-speed events.Furthermore,this study affirms the rationality of utilizing the statistical characteristics derived from ensemble forecasts as probabilistic forecast factors.The application of probability integral transform(PIT) and quantile–quantile(QQ) plots demonstrates that gust variations at the majority of observation sites conform to the GEV distribution,thereby indicating the potential for further enhanced forecast accuracy.The results also underscore the significant utility of the PSO hybrid model,which amalgamates particle swarm optimization with a BP neural network,in the probabilistic forecasting of strong winds within the field of meteorology.