| THE EFFECT OF SAMPLE OPTIMIZATION ON THE ENSEMBLE KALMAN FILTER IN
FORECASTING TYPHOON RAMMASUN (2014)? |
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| 引用本文： | 李霁杭,万齐林,高郁东,肖 辉.THE EFFECT OF SAMPLE OPTIMIZATION ON THE ENSEMBLE KALMAN FILTER IN
FORECASTING TYPHOON RAMMASUN (2014)?[J].热带气象学报(英文版),2018,24(4):433-447. |
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| 作者姓名： | 李霁杭 万齐林 高郁东 肖 辉 |
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| 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 EnKF. 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 EnKF 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 EnKF.
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| 关 键 词： | data assimilation ensemble prediction sample optimization Typhoon Rammasun ensemble Kalman filter |
| 修稿时间： | 2018/9/5 0:00:00 |
THE EFFECT OF SAMPLE OPTIMIZATION ON THE ENSEMBLE KALMAN FILTER IN FORECASTING TYPHOON RAMMASUN(2014) |
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| LI Ji-hang,WAN Qi-lin,GAO Yu-dong and XIAO Hui.THE EFFECT OF SAMPLE OPTIMIZATION ON THE ENSEMBLE KALMAN FILTER IN FORECASTING TYPHOON RAMMASUN(2014)[J].Journal of Tropical Meteorology,2018,24(4):433-447. |
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| Authors: | LI Ji-hang WAN Qi-lin GAO Yu-dong and XIAO Hui |
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| Institution: | Key Laboratory of Regional Numerical Weather Prediction, Guangzhou
Institute of Tropical and Marine Meteorology, Guangzhou 510641 China |
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| Abstract: | 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 EnKF. 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 EnKF 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 EnKF. |
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| Keywords: | data assimilation ensemble prediction sample optimization Typhoon Rammasun ensemble Kalman filter |
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