Research on four-dimensional data assimilation applied in a Meiyu front rainstorm case
This paper mainly applied four-dimensional observation nudging assimilation in WRF model to a rainstorm case in the Meiyu season of 2008 and studied the impact of different assimilation strategies on the simulation results. The assimilation data include 12 h rawinsonde observations and 3 h surface observations. The results show that the assimilation of rawinsonde observations can correct the displacement and development of meso-α system, therefore, effectively improve the location of the heavy rain band. Oppositely, the assimilation of moisture observations underestimates the intensity of the heavy rain band, because reducing water vapor mixing ratio in the near moist saturation regions results in decreasing latent heat and weakening the intensity of convection and precipitation in these regions. The assimilation of the surface observations can improve planetary boundary layer in the model. Usually this kind of effect is evident in the model surface layer and tends to decreases with increasing height upward. The simulation quality of the PBL is very important to model precipitation, so that the inclusion of surface observations makes model precipitation more real. Regional grid-nested simulation tests indicate that the data assimilation on coarse grid can effectively restrain the error growth of meso-α system and the one on fine grid can further reduce the error growth of meso-β system.
RainstormFour-dimensional data assimilationWRF modelAssimilation strategies
国家科技期刊平台
NSTL
万方数据
维普
