The Error Of Terrain Representativeness Research Of Automatic Weather Station Data Assimilation

The elevation difference between model surface and observation site is a key problem affecting the efficiency of surface data assimilation.In this paper,a reasonable quality control(QC)and assimilation scheme was proposed,which is able to assimilate surface automatic weather station(AWS)data into Meso-scale model efficiently,and the negative effects of the elevation difference can be reduced.Above all,the error sources in data assimilation were analyzed and conducted,and according to the categories and the demand of data assimilation,the matching quality control methods were developed.Then,surface AWS data are assimilated into WRF model through an ensemble Kalman filter,and the assimilation capacity of dealing with the elevation difference was assessed.A new scheme was designed through two steps.Firstly assimilating 2-m potential temperature,2-m dewpoint temperature,instead of 2-m temperature,2-m specific humidity.Then an adjusted scheme including error of terrain representativeness was given to reduce the negative effects of the elevation difference.A series of numerical experiments were carried out,and the results show that:(1)the method is able to reduce rough errors and representative errors effectively,keep normal distribution characteristic of random errors,and lower the root mean square error between observation and background significantly.The percentage of data excluded by QC is more reasonable.(2)Contrast experiments of the two assimilation schemes show that The elevation difference between model surface and observation site is a key problem.By increasing the elevation difference threshold,the efficiency of surface data assimilation can be increased,but no obvious improvement on the 24 h forecast.(3)Using Guo et al.(2002)'s scheme,the assimilation of surface AWS data through Ensemble Square Root filter(En SRF)can improve the simulation results.The assimilation of any element of surface observation data,including temperature,humidity,wind and surface pressure,separately can affect the rainfall forecast,but different elements have different impacts.The most influenced one is dewpoint temperature,and the use of 2-m potential temperature and2-m dewpoint temperature instead of 2-m temperature and 2-m specific humidity get a more better simulation results.(4)For more efficient assimilation of surface AWS data,a further improvement based on the En SRF is proposed to solve the impact of assimilation results caused by elevation difference between observation site and model surface.Potential temperature and dewpoint temperature Terrain Error of Representativeness(TER)are added into temperature and dewpoint temperature error of surface observation data assimilation in WRF-En SRF system respectively,and a numerical simulation has been carried and the results show that the Root-Mean-Square Error(RMSE),threat score(TS)and the first 13 hours elements prediction have been improved generally.As terrain error of representativeness(TER)added,RMSE of wind is improved in general,which of potential temperature and dewpoint temperature is unstable in the earlier stage,but improved in later stage,and TS of the first and the later 24 h accumulated rainfall are overall improved,compared with no TER added.So new scheme is able to reduce the impact of assimilation results caused by elevation difference.