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 ens...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.展开更多
Warm-sector torrential rainfall(WSTR)events that occur in the annually first rainy season in south China are characterized by high rainfall intensity and low radar echo centroids.To understand the synoptic characteris...Warm-sector torrential rainfall(WSTR)events that occur in the annually first rainy season in south China are characterized by high rainfall intensity and low radar echo centroids.To understand the synoptic characteristics related to these features,16 WSTR events that occurred in 2013-2017 were examined with another 16 squall line(SL)events occurred during the same period as references.Composite analysis derived from ERA-Interim reanalysis data indicated the importance of the deep layer of warm and moist air for WSTR events.The most significant difference between WSTR and SL events lies in their low-level convergence and lifting;for WSTR events,the low-level convergence and lifting is much shallower with comparable or stronger intensity.The trumpet-shaped topography to the north of the WSTR centers is favorable for the development of such shallow convergences in WSTR events.Results in this study will provide references for future studies to improve the predictability of WSTR.展开更多
Nowadays,ensemble forecasting is popular in numerical weather prediction(NWP).However,an ensemble may not produce a perfect Gaussian probability distribution due to limited members and the fact that some members signi...Nowadays,ensemble forecasting is popular in numerical weather prediction(NWP).However,an ensemble may not produce a perfect Gaussian probability distribution due to limited members and the fact that some members significantly deviate from the true atmospheric state.Therefore,event samples with small probabilities may downgrade the accuracy of an ensemble forecast.In this study,the evolution of tropical storms(weak typhoon)was investigated and an observed tropical storm track was used to limit the probability distribution of samples.The ensemble forecast method used pure observation data instead of assimilated data.In addition,the prediction results for three tropical storm systems,Merbok,Mawar,and Guchol,showed that track and intensity errors could be reduced through sample optimization.In the research,the vertical structures of these tropical storms were compared,and the existence of different thermal structures was discovered.One possible reason for structural differences is sample optimization,and it may affect storm intensity and track.展开更多
基金National Key Project for Basic Research(973 project)(2015CB452802)National Natural Science Fund(41475102,41675099,41475061)+2 种基金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)
文摘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.
基金National Key R&D Program of China(2018YFC1507402)National Natural Science Foundation of China(41875168,U1811464)Science and Technology Planning Project of Guangzhou(201605131033247)。
文摘Warm-sector torrential rainfall(WSTR)events that occur in the annually first rainy season in south China are characterized by high rainfall intensity and low radar echo centroids.To understand the synoptic characteristics related to these features,16 WSTR events that occurred in 2013-2017 were examined with another 16 squall line(SL)events occurred during the same period as references.Composite analysis derived from ERA-Interim reanalysis data indicated the importance of the deep layer of warm and moist air for WSTR events.The most significant difference between WSTR and SL events lies in their low-level convergence and lifting;for WSTR events,the low-level convergence and lifting is much shallower with comparable or stronger intensity.The trumpet-shaped topography to the north of the WSTR centers is favorable for the development of such shallow convergences in WSTR events.Results in this study will provide references for future studies to improve the predictability of WSTR.
基金Science and Technology Planning Project of Guangdong Province(2017B020244002,2018B020208004,2017B030314140)Natural Science Foundation of Guangdong Province(2019A1515011118)+1 种基金National Natural Science Fund(41705089)Science and Technology Project of Guangdong Meteorological Service(GRMC2017Q01)
文摘Nowadays,ensemble forecasting is popular in numerical weather prediction(NWP).However,an ensemble may not produce a perfect Gaussian probability distribution due to limited members and the fact that some members significantly deviate from the true atmospheric state.Therefore,event samples with small probabilities may downgrade the accuracy of an ensemble forecast.In this study,the evolution of tropical storms(weak typhoon)was investigated and an observed tropical storm track was used to limit the probability distribution of samples.The ensemble forecast method used pure observation data instead of assimilated data.In addition,the prediction results for three tropical storm systems,Merbok,Mawar,and Guchol,showed that track and intensity errors could be reduced through sample optimization.In the research,the vertical structures of these tropical storms were compared,and the existence of different thermal structures was discovered.One possible reason for structural differences is sample optimization,and it may affect storm intensity and track.
