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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
Author(s) -
Tong ChongChi,
Jung Youngsun,
Xue Ming,
Liu Chengsi
Publication year - 2020
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2020gl090179
Subject(s) - data assimilation , ensemble kalman filter , meteorology , radar , probabilistic logic , environmental science , kalman filter , computer science , forecast skill , physics , extended kalman filter , telecommunications , artificial intelligence
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, and 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.