THE EFFECT OF SAMPLE OPTIMIZATION ON THE ENSEMBLE KALMAN FILTER IN FORECASTING TYPHOON RAMMASUN(2014)  

作　　者：LI Ji-hang WAN Qi-lin GAO Yu-dong XIAO Hui 李霁杭;万齐林;高郁东;肖辉

机构地区：[1]Key Laboratory of Regional Numerical Weather Prediction, Guangzhou Institute of Tropical and Marine Meteorology

出　　处：《Journal of Tropical Meteorology》2018年第4期433-447,共15页热带气象学报（英文版）

基　　金：National Key Project for Basic Research(973 project)(2015CB452802);National Natural Science Fund(41475102,41675099,41475061);Science and Technology Planning Project of Guangdong Province(2017B020218003,2017B030314140);Natural Science Foundation of Guangdong Province(2016A030313140,2017A030313225);Science and technology project of Guangdong Meteorological Bureau(GRMC2017Q01)

摘　　要：In a limited number of ensembles, some samples do not adequately reflect the true atmospheric state and can in turn affect forecast performance. This study explored the feasibility of sample optimization using the ensemble Kalman filter(EnKF) for a simulation of the 2014 Super Typhoon Rammasun, which made landfall in southern China in July 2014. Under the premise of sufficient ensemble spread, keeping samples with a good fit to observations and eliminating those with poor fit can affect the performance of En KF. In the sample optimization, states were selected based on the sample spatial correlation between the ensemble state and observations. The method discarded ensemble states that were less representative and, to maintain the overall ensemble size, generated new ensemble states by reproducing them from ensemble states with a good fit by adding random noise. Sample selection was performed based on radar echo data. Results showed that applying En KF with optimized samples improved the estimated track, intensity,precipitation distribution, and inner-core structure of Typhoon Rammasun. Therefore, the authors proposed that distinguishing between samples with good and poor fits is vital for ensemble prediction, suggesting that sample optimization is necessary to the effective use of En KF.In a limited number of ensembles, some samples do not adequately reflect the true atmospheric state and can in turn affect forecast performance. This study explored the feasibility of sample optimization using the ensemble Kalman filter(EnKF) for a simulation of the 2014 Super Typhoon Rammasun, which made landfall in southern China in July 2014. Under the premise of sufficient ensemble spread, keeping samples with a good fit to observations and eliminating those with poor fit can affect the performance of En KF. In the sample optimization, states were selected based on the sample spatial correlation between the ensemble state and observations. The method discarded ensemble states that were less representative and, to maintain the overall ensemble size, generated new ensemble states by reproducing them from ensemble states with a good fit by adding random noise. Sample selection was performed based on radar echo data. Results showed that applying En KF with optimized samples improved the estimated track, intensity,precipitation distribution, and inner-core structure of Typhoon Rammasun. Therefore, the authors proposed that distinguishing between samples with good and poor fits is vital for ensemble prediction, suggesting that sample optimization is necessary to the effective use of En KF.

关 键 词：data ASSIMILATION ENSEMBLE prediction SAMPLE OPTIMIZATION TYPHOON Rammasun ENSEMBLE KALMAN filter 

分 类 号：P457.8[天文地球—大气科学及气象学]