摘要
为了提高5 km分辨率的网格降水预报准确率,针对数值模式网格降水预报产品中尚存在许多降水空报的现象,基于网格降水实况,应用动态建模和机器训练择优技术,借助新检验参数归一化后的单调性、新降水TS公式在计算上的便利性,建立了两种动态最优降水消空技术方案,开展网格降水消空研究。研究表明,两步法抑制了消空阈值偏大现象,归一化法使阈值优选更加直接。用这两种方法,晴雨准确率全部上升,其中,ECMWF(European Centre for Medium-range Weather Forecasts)提高最大(2.39%~4.76%);降水TS评分,ECMWF提高最大,白天提高多(2.98%~3.64%),夜间提高少(1.61%~1.78%),但CMA-SH9(中国气象局上海数值预报模式系统)和CMA-BJ(中国气象局北京快速更新循环数值预报系统)则出现下降。归一化法在白天使晴雨准确率提高最多。分析表明,经过消空处理后,雨空百分率下降数值明显大于雨漏百分率增加数值,从而使空报率出现大幅下降,晴雨准确率也升高明显。
In order to improve the accuracy of grid precipitation forecast,aiming at the phenomenon that there are still much empty precipitation forecasts in the numerical model grid precipitation forecast,based on the live grid precipitation,applying the dynamic modelling and machine training,with the monotonicity of normalized new test parameters,and the computational convenience of the new precipitation TS scoring formula,the dynamic optimal elimination of grid-by-grid precipitation is researched.The research show that the empty precipitation threshold is suppressed in the two-step method,and the process of threshold training selection is more direct in the normalized method.With these two methods,the rain or shine accuracy is all increased.Among them,ECMWF(European Centre for Medium-Range Weather Forecasts) increases by 2.39%-4.76%;the TS score of ECMWF improves the most,increasing 2.98%-3.64% during the day and increasing 1.61%-1.78% at night.However,the TS scores in CMA-SH9 and CMA-BJ decline.The normalized method increases the clear/rain forecast accuracy the most during the day.The analysis shows that the drop in the false alarm rate is significantly greater than the missing forecast rate.As a result,the FAR drops significantly,and the clear/rain forecast accuracy also increases significantly.
作者
张成军
赵声蓉
任小芳
张亚刚
苏洋
ZHANG Chengjun;ZHAO Shengrong;REN Xiaofang;ZHANG Yagang;SU Yang(Key Laboratory for Meteorological Disaster Monitoring and Early Warning and Risk Management of Characteristic Agriculture in Arid Regions,CMA,Ningxia Key Laboratory of Meteorological Disaster Prevention and Reduction,Yinchuan 750002;Ningxia Meteorological Observatory,Yinchuan 750002;National Meteorological Center,Beijing 100081)
出处
《气象科技》
2022年第5期677-685,共9页
Meteorological Science and Technology
基金
2021年中国气象局创新发展专项(CXFZ2021Z034-97)
宁夏回族自治区重点研发计划项目(2021BEG03021)共同资助。
关键词
网格降水
消空
动态建模
训练择优
grid precipitation
eliminating false forecast
dynamic modelling
training selection optimal
