【摘要】 地形复杂的四川地区,虽然雷暴大风发生频次较低,但造成的影响重大,现有的客观预报产品较少且时间分辨率低,为进一步提升四川复杂地形下雷暴大风预报准确率,综合考虑地形因子、模式物理量因子和时间因子,根据海拔高度将四川分为高海拔区和低海拔区,利用2018—2021年数据基于随机森林、自适应提升法、极端随机树三种机器学习方法分区构建雷暴大风预报模型,对2022年进行预报,获得逐3 h雷暴大风潜势预报,再利用气候背景将3 h预报时间降尺度到1 h,形成0～12 h逐小时雷暴大风预报,并检验预报效果。结果表明,逐3 h雷暴大风预报以自适应提升法效果最优,长时间检验和个例检验都表明,基于自适应提升法获得的0～12 h逐小时雷暴大风预报产品优于中央气象台产品,TS评分由0.0104提升至0.0595,空报率由0.988下降至0.808,业务应用价值较高。更多还原

【Abstract】 In the complex terrain of Sichuan Region,although the frequency of thunderstorm gale is relatively low, its impact is significant.There are few objective forecast products of the thunderstorm gale,and even if there are,the time resolution is lower.In order to further improve the accuracy of thunderstorm gale forecasting under complex terrain in Sichuan,in this article we comprehensively consider terrain factors,model physical quantity factors and time factors.According to the altitude,Sichuan is divided into high-altitude and low altitude areas.Based on the data from 2018 to 2021 and three machine learning methods of random forest,adaptive boosting and extreme random tree,we construct a thunderstorm gale prediction model and make a forecast for the 2022 thunderstorm gales obtaining a 3 h thunderstorm gale potential forecast.Then,based on the climate background, we downscale the 3 h forecast time to 1 h,and make a 0-12 h hourly thunderstorm gale forecast.At the same time,the forecasting effect is tested.The results show that,the adaptive boosting method of 3 h thunderstorm gale forecast has the best effect.The longtime and individual case tests show that the 0-12 h hourly thunderstorm gale forecast product obtained by the adaptive boosting method is superior to the forecasts of National Meteorological Centre with the TS score increased from 0.0104 to 0.0595,and the false alarm rate decreased from 0.988 to 0.808.This indicates that the adaptive boosting method has a higher application value in forecasting operation application value.更多还原