期刊
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS
卷 12, 期 6, 页码 409-416出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/16742834.2019.1671767
关键词
Heavy rainfall; multigrid scheme NLS-4DVar method; radar radial velocity data assimilation
资金
- National Key Research and Development Program of China [2016YFA0600203]
- National Natural Science Foundation of China [41575100]
- Key Research Program of Frontier Sciences, Chinese Academy of Sciences [QYZDY-SSW-DQC012]
The nonlinear least-squares four-dimensional variational assimilation (NLS-4DVar) method introduced here combines the merits of the ensemble Kalman filter and 4DVar assimilation methods. The multigrid NLS-4DVar method can be implemented without adjoint models and also corrects small- to large-scale errors with greater accuracy. In this paper, the multigrid NLS-4DVar method is used in radar radial velocity data assimilations. Observing system simulation experiments were conducted to determine the capability and efficiency of multigrid NLS-4DVar for assimilating radar radial velocity with WRF-ARW (the Advanced Research Weather Research and Forecasting model). The results show significant improvement in 24-h cumulative precipitation prediction due to improved initial conditions after assimilating the radar radial velocity. Additionally, the multigrid NLS-4DVar method reduces computational cost.
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