Two Newtonian-relaxation-based FDDA schemes, analysis-nudging and observation-nudging were recently implemented into the latest WRF model (Stauffer et al. 2005, Liu et al. 2005). These FDDA capabilities allow the WRF model to continuously assimilate observations through observation-nudging, and to control large-scale model error through analysis-nudging. By starting from a spun-up condition produced by FDDA, WRF model is capable of producing meaningful forecasts from first time step, which differs from cold-start runs, which have to discard first a few hours of forecasts contaminated by spin-up noises. Thus, FDDA can be a viable approach to improve 0 12 h forecasts of summer convection (where 2 12h forecasts of convection are referred as to dark-area where convective nowcasting loses its skill and mesoscale NWP suffers from spin-up processes.
This paper studies the effect of analysis-nudging, observation-nudging, WRF-VAR (3DVAR) and a hybrid scheme that combines nudging and 3DVAR for summer convection forecasts during IHOP field experiment over the Central Plain. Three nested domains with grid sizes of 30, 10 and 3.3 km are employed. Observations from conventional networks, mesonets and IHOP special platforms are assimilated. Analysis-nudging is conducted on coarse domains (30 and 10 km grids) with 3-hour windows and observation-nudging is performed on fine meshes (10 and 3.3 km grids) continuously. WRF-3DVAR is set up to run in a 3-hourly cycling mode on the coarse meshes (30 and 10 km grids). Finally, an experiment is carried out using the hybrid approach. Simulation result shows an encouraging impact of the FDDA schemes.