雷达资料不同同化试验对短时降水预报影响的分析  

Analysis of the influence of different assimilation experiments of radar data on short-term precipitation forecast

作　　者：胡婧婷 陈良吕 夏宇 HU Jingting;CHEN Lianglü;XIA Yu(Chongqing Institute of Meteorological Sciences,Chongqing 401147;Institute of Urban Meteorology,CMA,Beijing 100089;Weather Online Institute of Meteorological Applications,Wuxi 214000)

机构地区：[1]重庆市气象科学研究所,重庆401147 [2]北京城市气象研究院,北京100089 [3]天气在线气象应用研究所,无锡214000

出　　处：《暴雨灾害》2022年第6期679-690,共12页Torrential Rain and Disasters

基　　金：重庆市技术创新与应用发展专项(cstc2019jscx-tjsbX0007);重庆市气象局业务技术攻关项目(YWJSGG-202114)。

摘　　要：基于重庆市气象局与美国俄克拉荷马大学风暴分析与预测中心合作构建的雷达资料直接同化系统,采用集合卡尔曼滤波(Ensemble Kalman Filter,EnKF)和不同背景误差协方差权重组合(静态背景误差协方差权重分别取0、0.2、0.5、0.8和1)的集合三维变分混合(Hybrid Ensemble Three-dimensional Variational, Hybrid En3DV)同化方法,开展了2020年汛期连续一个月的雷达资料同化及预报试验,由此探讨雷达资料的不同直接同化方法对短时降水预报的影响,结果表明:不同同化试验的降水预报技巧在较短预报时效(3 h内)差异最显著,随预报时效的增加差异逐渐减小;总体而言,除个别预报时效外,EnKF试验的短时降水预报结果优于Hybrid En3DV试验;在Hybrid En3DV试验中,仅使用集合估计背景误差协方差的试验最优,说明静态背景误差协方差的正面影响有限;个例试验结果表明,随集合估计背景误差协方差权重的增加,模式的平衡调整时间随之减少,说明集合估计背景误差协方差相对于静态的背景误差协方差而言,能使得到的同化分析场变量间更加协调。Based on the radar data direct assimilation system established by the Chongqing Meteorological Bureau in cooperation with the Center for Analysis and Prediction of Storms of the University of Oklahoma, a set of radar data assimilation and forecasting experiments for a continuous month in the 2020 are carried out by using ensemble Kalman filter(EnKF) assimilation and hybrid ensemble three-dimensional variational(Hybrid Ensemble Three-dimensional Variational, Hybrid En3DV) assimilation with different hybrid weights(with 0%, 20%,50%, 80% and 100% of static background error covariance). The performance of different direct assimilation methods of radar data on the short-term precipitation is explored. The results of one month continuous experiment indicate that the precipitation forecasting skills of different assimilation experiments show significant difference in the forecast with short leading-time(within 3 h), and then the difference gradually decreases with the increase of the forecast leading-time. In general, the short-term precipitation forecasting skills of EnKF are much better than the others. In the Hybrid En3DV assimilation experiments, the experiment with only the ensemble-based background error covariance performs the best, suggesting that the static background error covariance has no positive effect on radar data assimilation. The results of case study show that with the increase of ensemble-based background error covariance weights, the spin-up time of model decreases, suggesting that the ensemble-based background error covariance can make the variables in assimilation analysis filed more balanced compared with the static background error covariance.

关 键 词：降水预报 雷达资料同化 集合卡尔曼滤波 

分 类 号：P456.7[天文地球—大气科学及气象学]