Research On Impact Of Microphysical Parameterization Schemes On Model Error Of WRF-EnSRF Assimilation System

Model error is largely due to the uncertainties of microphysical parameterizations in the process of mesoscale weather data assimilation and simulation. Based on the assumption that a multi-scheme ensemble forecast that combines all different microphysical parameterization schemes may significantly improve the EnKF performance than a single scheme, WRF-Ensemble Square Root Filter is examined to assimilate simulated radar data of a typical super storm occured on May 20,1977 in the central of Oklahoma city, and the Doppler radar data of a strong convective weather occured in the Midwest of Jiangsu for the period of June 5,2009 is also assimilated. During the research of some sensitive experiments, the main conclusions are as follows:(1) Model error caused by microphysical parameterization scheme reduces the performance of the WRF-EnSRF system to some degrees. In the data assimilation of simulated Doppler radar data in the presence of model errors, the value of ensemble average analysis root-mean-square error (RMSE) is larger than that of the corresponding control experiments and the EnKF performance is seriously degraded. However, the value of RMSE decreases with the assimilation times increasing. EnSRF assimilation system can improve the effect of model errors. Microphysical parameterization schemes have evident impact on the analysis of variables both of model field and observation field, especially on cloud microphysical variables.(2) Meanwhile, all of the multi-schemes can perform the assimilation better than the single ones to some extent. In the assimilation of simulated radar data, the multi-scheme not combine control experiment scheme only improve the performance of some variables. The multi-schemes including control experiment scheme improve the performance further than these single schemes, respectively on horizontal wind field, vertical velocity and microphysical field. Both the root mean square of column-averaged of difference of total ennergy (RM-DTE) and the evolution of root-aquare-mean error (RMSE) become smaller. It is further found that the combined microphysical schemes which only contain ice microphysical parameterization attain better analysis effect, because some schemes that can not output graupel mixing ratio are excluded such as Kessler warm-rain scheme. In addition, since increasing percentage of microphysical parameterization scheme control experiment uses, the analysis effect is more suitable with the assimilation times increasing.(3)In the assimilation of real Doppler radar data, different microphysical schemes cause to different assimilation analysis. The selection of schme has graeter impact on the assimilation analysis of reflectivity than that of radial velocity, which is dissimilar to former ideal test. Mutil microphysical schemes which only contain ice microphysical parameterization does not perform well. A more targeted multi-scheme is also tested. In this multi-scheme, both Lin schme and WSM 6 schme are selected which performs better than others. It is found that it performs as well as Lin schme, but not the best one. So mutil-schmes are able to reduce model error partially. It needs to be considered into more factors to estimate the model error. However, in practice, it is hard to determine a priori which microphysical scheme is the most suitable to predict certain kinds of weather systems in different flow regimes. The mutil microphysical scheme may be the better choice.