| Data assimilation is an important method to improve the quality of initial field of numerical model.It is also a core issue in the current research of numerical weather prediction.The surface observation data in China is widely distributed with high time and spatial resolution.Due to the significant difference between the heights of the observation and model,the surface observation data under complex terrain are largely eliminated during the data assimilation,resulting in low actual utilization rate.Therefore,it is of great significance to study the assimilation of surface observation data under complex terrain and the methodoly of using surface observation data in a full and effective way,assimilating them into the initial field required by numerical weather prediction to improve the level of numerical model simulation and forecasting.In this study,the meso-scale weather model WRF(Weather Research and Forecasting Model)and GSI assimilation system(Gridpoint Statistical Interpolation System)were used to assimilate surface observation data,sounding data,radar data and satellite data.A WRF-GSI cycle assimilation scheme was designed and carried out batch test research.Based on the analysis results of the batch test,the surface data assimilation scheme was initially improved for the complex terrain in western China,and the surface observation data was corrected according to the height difference between the model terrain and the actual station terrain.Ruggiero's air pressure correction method and Benjamin's temperature correction method were adopted to correct the surface pressure and temperature.For verifying the feasibility of the correction method,two sets of numerical simulation experiments were carried out.The heavy rain process in July 2016 was taken as an example to compare and analyze the impact of the correction and assimilation of surface observation data on the precipitation process.The main results and conclusions are as follows:(1)The WRF model and GSI system can be coupled and carried out cyclic assimilation experiment.The batch experiment evaluation results show that compared to the ERA-Interim reanalysis data,the simulated surface and sounding elements are improved.The root mean square error of the surface pressure is reduced by 46.6%,and the root mean square error of the zonal and meridional wind speed are reduced by 6.6% and 14.7% respectively;The vertical average root mean square error of temperature is smaller than the ERA-Interim reanalysis data at 400-925 h Pa.(2)According to the height difference between the model and the actual terrain,the stations where the root mean square error is greater than 3 in the central and western regions is corrected.The corrected experiment results show that the surface pressure and temperature in the central and western regions have been effectively improved after the correcting.In the region where the temperature improved the most,the RMSE decreased by 0.8 K,and the RMSE of the whole 100-925 h Pa layer decreased significantly.(3)After the correction,the simulated rainfall area and magnitude of the sensitivity experiment are closer to the observed rain band,and compared to the control experiment,the 6-hour TS scores of the sensitivity experiment are increased,what's more,the dynamic aspects such as divergence and vertical velocity are also improved,which indicates that the revising of the surface observation data does have a positive effect on torrential rain simulation.In general,the correction of the surface observation data under complex terrain based on the height difference between the model and actual station terrain can improve the accuracy of precipitation forecasts,which provide a certain scientific and application basis for the surface observation data in assimilation and the establishment of reanalysis data sets in East Asia. |
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