基于云依赖背景场误差协方差的雷达资料同化及对降雨预报影响研究  

Assimilation of radar data based on cloud-dependent background error covariance and its impact on rainfall forecasting

作　　者：陈娴雅 陈耀登[1] 孟德明 CHEN Xianya;CHEN Yaodeng;MENG Deming(Key Laboratory of Meteorological Disaster,Ministry of Education/International Joint Laboratory on Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science&Technology,Nanjing 210044,China;Key Laboratory of Mesoscale Severe Weather/Ministry of Education,School of Atmospheric Sciences,Nanjing University,Nanjing 210023,China)

机构地区：[1]南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心,南京210044 [2]南京大学大气科学学院,中尺度灾害性天气教育部重点实验室,南京210023

出　　处：《气象学报》2022年第2期243-256,共14页Acta Meteorologica Sinica

基　　金：国家自然科学基金面上项目(42075148);国家重点研发计划重点专项项目(2017YFC1502102)。

摘　　要：传统变分同化方法中使用各向同性和均质的背景场误差协方差,忽略了背景场误差协方差的天气系统依赖性,而在变分框架下引入集合流依赖的背景场误差协方差还需要额外的集合预报。为在变分同化中引入更合理的背景场误差协方差,通过引入云指数构建“云依赖”背景场误差协方差,提出了一种云依赖背景场误差协方差的同化方案,并应用于雷达等多源观测资料同化。基于云依赖背景场误差协方差的资料同化方案,开展了一系列单点观测试验、梅雨期批量循环同化预报试验以及降雨个例详细诊断分析。从单点观测试验看,云依赖背景场误差协方差可以实时动态地调整各格点背景场误差,使分析增量具有明显的各向异性和对云雨特征的依赖;批量循环同化与预报试验表明采用云依赖背景场误差协方差的雷达资料同化可以稳定提高降水预报能力,对大量级降水评分的改善尤为明显;对强对流暴雨过程的诊断进一步表明,云依赖背景场误差协方差的应用改进了动力、热力、水汽和水凝物场的预报。基于云依赖背景场误差协方差的同化方案,能在变分同化框架下引入更符合实时天气特征的背景场误差协方差信息,为更好地同化高分辨率雷达资料提供了基础,有效提高了降雨预报的效果。The traditional variational assimilation method uses isotropic and homogeneous background error covariance, which ignores the weather system dependence of the background error covariance, and the introduction of ensemble flow-dependent background error covariance in the variational framework requires additional ensemble forecasts. In order to introduce more reasonable background error covariance in the variational assimilation, a "cloud-dependent" background error covariance is constructed by introducing cloud indices, and a cloud-dependent background error covariance assimilation scheme is proposed and applied to the assimilation of radar and other multi-source observations. Based on the cloud-dependent background error covariance data assimilation scheme, a series of single observation tests and batch cyclic assimilation forecasts during the rainy season as well as detailed diagnostic analysis of rainfall cases are carried out. From the single observation tests, it is found that the cloud-dependent background error covariance can dynamically adjust the background error at each grid point in real time, resulting in significant anisotropy and dependence of the analysis increments on cloud and rain characteristics;the batch cyclic assimilation and forecasting experiments show that the radar assimilation with cloud-dependent background error covariance can steadily improve the precipitation forecasting capability, and the improvement is especially obvious for the large magnitude precipitation scores;The diagnosis of strong convective storms further shows that the application of cloud-dependent background error covariance improves the prediction of dynamical, thermal, water vapor and hydrometeor fields. The assimilation scheme based on cloud-dependent background error covariance can introduce background error covariance information that is more consistent with real-time weather characteristics in the framework of variational assimilation, which provides a basis for better assimilation of high-resolution radar data a

关 键 词：雷达资料同化 变分同化 背景场误差协方差 云依赖 

分 类 号：P456.7[天文地球—大气科学及气象学]