Direct Assimilation of Radar Data with Ensemble Kalman Filter and Hybrid Ensemble‐Variational Method in the National Weather Service Operational Data Assimilation System GSI for the Stand‐Alone Regional FV3 Model at a Convection‐Allowing Resolution

doi:10.1029/2020GL090179

Capabilities to directly assimilate radar radial velocity (V_r) and reflectivity (Z) data are implemented within the operational GSI data assimilation (DA) framework and coupled with the new stand‐alone regional (SAR) FV3 model. The effectiveness and performance of 3DVar, EnKF, hybrid En3DVar methods are evaluated with a storm cluster over the U.S. Central Plains at 3 km grid spacing. During the DA cycles, 3DVar analyses show better fit to Z observations but fastest error growth while EnKF and pure En3DVar lead to smaller forecast errors. For V_r, EnKF outperforms other methods in both analysis and forecast. Good correspondence with tornado reports is obtained by most experiments for probabilistic forecast of updraft helicity (UH), except for 3DVar which shows insufficient confidence in certain regions. Overall, EnKF and hybrid En3DVar show best forecast skills in terms of composite reflectivity and UH. Tests with more cases are needed to draw more general conclusions, however.

DOI	10.1029/2020GL090179
	Direct Assimilation of Radar Data with Ensemble Kalman Filter and Hybrid Ensemble‐Variational Method in the National Weather Service Operational Data Assimilation System GSI for the Stand‐Alone Regional FV3 Model at a Convection‐Allowing Resolution
	Chong‐; Chi Tong; Youngsun Jung; Ming Xue; Chengsi Liu
	2020-09-23
发表期刊	Geophysical Research Letters
出版年	2020
英文摘要	Capabilities to directly assimilate radar radial velocity (V_r) and reflectivity (Z) data are implemented within the operational GSI data assimilation (DA) framework and coupled with the new stand‐alone regional (SAR) FV3 model. The effectiveness and performance of 3DVar, EnKF, hybrid En3DVar methods are evaluated with a storm cluster over the U.S. Central Plains at 3 km grid spacing. During the DA cycles, 3DVar analyses show better fit to Z observations but fastest error growth while EnKF and pure En3DVar lead to smaller forecast errors. For V_r, EnKF outperforms other methods in both analysis and forecast. Good correspondence with tornado reports is obtained by most experiments for probabilistic forecast of updraft helicity (UH), except for 3DVar which shows insufficient confidence in certain regions. Overall, EnKF and hybrid En3DVar show best forecast skills in terms of composite reflectivity and UH. Tests with more cases are needed to draw more general conclusions, however.
领域	气候变化
URL	查看原文
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/296336
专题	气候变化
推荐引用方式 GB/T 7714	Chong‐,Chi Tong,Youngsun Jung,等. Direct Assimilation of Radar Data with Ensemble Kalman Filter and Hybrid Ensemble‐Variational Method in the National Weather Service Operational Data Assimilation System GSI for the Stand‐Alone Regional FV3 Model at a Convection‐Allowing Resolution[J]. Geophysical Research Letters,2020.
APA	Chong‐,Chi Tong,Youngsun Jung,Ming Xue,&Chengsi Liu.(2020).Direct Assimilation of Radar Data with Ensemble Kalman Filter and Hybrid Ensemble‐Variational Method in the National Weather Service Operational Data Assimilation System GSI for the Stand‐Alone Regional FV3 Model at a Convection‐Allowing Resolution.Geophysical Research Letters.
MLA	Chong‐,et al."Direct Assimilation of Radar Data with Ensemble Kalman Filter and Hybrid Ensemble‐Variational Method in the National Weather Service Operational Data Assimilation System GSI for the Stand‐Alone Regional FV3 Model at a Convection‐Allowing Resolution".Geophysical Research Letters (2020).

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