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 ...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.展开更多
以内蒙古中部某风电场为实验风电场,采用随机森林(Random forest,RF)方法、相似误差订正(Analogue correction of errors,ACE)方法以及概率密度匹配方法(Probability density function matching method,PDF)分别对风电场风速预报进行订...以内蒙古中部某风电场为实验风电场,采用随机森林(Random forest,RF)方法、相似误差订正(Analogue correction of errors,ACE)方法以及概率密度匹配方法(Probability density function matching method,PDF)分别对风电场风速预报进行订正及适用性研究。结果表明:3种方法在各季均对中尺度天气预报模式(Weather research and forecasting model,WRF)风速预报具有不同程度的订正效果,RF方法可以有效改善WRF误差较大的问题,但兼具误差过分放大情况,ACE方法和PDF虽然对较大误差的改善能力不及RF方法,但是能够较好地控制误差过分放大问题。此外,3种方法针对小于5 m·s^(-1)的小风速段,订正效果不理想,随着风速的增加,订正能力逐渐增强。参照预报模型各自的优势,尝试开展多种预报模型的分风速等级集成应用,可以对不同风速等级下的WRF预报起到较好的改善作用。展开更多
基金Supported by the National Meteorological Centre’s Special Project for Meteorological Modernization Construction in 2022(QXXDH202230)。
文摘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.
文摘以内蒙古中部某风电场为实验风电场,采用随机森林(Random forest,RF)方法、相似误差订正(Analogue correction of errors,ACE)方法以及概率密度匹配方法(Probability density function matching method,PDF)分别对风电场风速预报进行订正及适用性研究。结果表明:3种方法在各季均对中尺度天气预报模式(Weather research and forecasting model,WRF)风速预报具有不同程度的订正效果,RF方法可以有效改善WRF误差较大的问题,但兼具误差过分放大情况,ACE方法和PDF虽然对较大误差的改善能力不及RF方法,但是能够较好地控制误差过分放大问题。此外,3种方法针对小于5 m·s^(-1)的小风速段,订正效果不理想,随着风速的增加,订正能力逐渐增强。参照预报模型各自的优势,尝试开展多种预报模型的分风速等级集成应用,可以对不同风速等级下的WRF预报起到较好的改善作用。
