结合中尺度模式物理约束的雷达回波临近外推预报方法研究  

A study on radar echo extrapolation nowcasting method combined with physical constraints of mesoscale model

作　　者：孙泓川[1,2] 吴海英 曾明剑[2,3] 程丛兰[4] SUN Hongchuan;WU Haiying;ZENG Mingjian;CHENG Conglan(Jiangsu Meteorological Observatory,Nanjing 210008,China;Key Laboratory of Transportation Meteorology,CMA,Nanjing 210009,China;Jiangsu Institute of Meteorological Sciences,Nanjing 210009,China;Institute of Urban Meteorology,CMA,Beijing 100089,China)

机构地区：[1]江苏省气象台,南京210008 [2]中国气象局交通气象重点开发实验室,南京210009 [3]江苏省气象科学研究所,南京210009 [4]北京城市气象研究院,北京100089

出　　处：《气象学报》2022年第2期257-268,共12页Acta Meteorologica Sinica

基　　金：国家重点研发计划项目(2017YFC1502104);江苏省自然科学基金项目(BK20201506);江苏省“333工程”科研资助项目(BRA2018100)。

摘　　要：研究设计了一种结合中尺度模式物理约束的雷达回波临近智能外推预报方法,该方法在外推预报时效(0—2 h)内即利用中尺度高分辨率模式信息对外推进行约束。首先将模式风场和雷达回波轨迹风场融合成融合风场,然后利用融合风场光流外推形成动力约束外推;并在此基础上利用模式诊断产品和雷达历史资料通过投票回归器集成多种深度学习算法构建回波强度频率分布的预测模型,最终基于预测模型结果利用降水频率匹配订正技术对外推预测的原始回波强度进行订正形成物理约束外推方法。通过2个典型个例,以及2年主汛期的长期检验对原始光流法、动力约束外推方法和物理约束外推方法进行综合评估,结果表明:动力约束外推通过改善光流法回波在边缘的堆积扭曲从而改进了预报性能,物理约束外推通过基于模式信息预测的回波频率分布调整回波强度实现回波的增强和减弱来改善预报性能,随着时效延长改善越来越明显,整体而言物理约束外推是其中最优的方案。This study develops an intelligent extrapolation nowcasting method combined with physical constraints of mesoscale numerical model. The method uses the high-resolution mesoscale model information to constrain the extrapolation within 2 h. Based on optical flow(OF) method extrapolation, the dynamic constraint extrapolation optical flow(DCOF) method is applied to reconstruct the motion vector;the mesoscale numerical model diagnostic products and radar historical data are used to train the echo intensity frequency prediction model, which integrates multiple deep learning algorithms through the voting regressor. Finally, a physical constraint optical flow(PCOF) method is used to correct the original echo intensity of the OF method by using the frequency-matching method. The performance of the OF method, DCOF method and PCOF method in 2 typical cases and cases over a long-term period are evaluated. The results show that the DCOF method improves the prediction by improving the unrealistic accumulation and distortion produced by the OF method at the edge of the echoes. The PCOF method improves the prediction by adjusting the echo intensity frequency distribution prediction based on the model information. With increasing forecast lead time, the improvements caused by the two methods are becoming more obvious. Overall, the PCOF method is the best among the three methods.

关 键 词：光流法 中尺度模式 深度学习 短时预报 

分 类 号：P456[天文地球—大气科学及气象学]