摘要：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.
