Experiments Of Soil Temperature And Moisture Assimilation System Based On Ensemble Kalman Filter

Land is the only interface with physical meaning in the whole atmosphere system, which accounts for nearly 30% of the Earth’s surface. Simultaneously, land surface is the major input of the atmospheric energy. With the continuous improvement and development of land surface model, to describe the soil heat/ water flux and the change of the atmospheric boundary layer more accurately has become an urgent need for global change research. In this paper, to promote the application of the observation data in land surface model, a soil temperature and moisture data assimilation scheme was developed. The experiment results showed that the assimilation system could improve the simulation performance of land surface model. The accurate estimation of land surface water/heat conditions and energy fluxes would be helpful to further study on land-atmosphere interactions. Specific research work and conclusions of this paper are as follows:(1) The land surface process in the Haihe River basin was modeled by using CLM 3.0 (Community Land Model). The simulated soil tempetature, soil moisture, latent heat flux and sensible heat flux of three sites were validated to evaluate the model performance. Then, the monthly and seasonal space-time characteristics of land surface process in Haihe river basin were analyzed from 2008 to 2010.(2) The sensitivity analysis of soil texture, the leaf area index and the initial field was made. Finally, the sand/clay proportion data set made by Beijing Normal University was used to replace the original model data. The results showed that the model was sensitive to the initial field, an accurate initial field could largely improve the simulation precision of the soil temperature, soil moisture and soil heat flux. Sensitivity analysis indicated that soil temperature was not sensitive to sand/clay proportion with the rate of change only from-1% to 1%. Soil moisture was sensitive to sand/clay proportion with the rate of change between -20% to 20%. The soil heat flux is not sensitive to sand/clay proportion.(3) With the CLM 3.0 as a model operator, a soil temperature and moisture data assimilation scheme was developed, which was based on Ensemble Kalman filter (EnKF). The assimilation of different state variables, assimilation frequency and sensitivity of the initial field to assimilation system were analyzed. The results showed that assimilating soil temperature was much more sensitive to assimilation frequency which required high temporal resolution. Assimilating soil moisture was insensitive to assimilation frequency. When the initial field was not accurate, assimilating soil moisture could improve the model simulation precision of soil temperature and moisture by setting assimilation frequency as 12 hours.(4) The assimilation timeliness, assimilating the first layer of soil moisture and assimilating AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) satellite data were experimented on the influence of assimilation system. It showed that when the bias of soil temperature and soil moisture simulation was quite bigger, assimilating the single moment soil moisture could still improve the model precision. When only assimilating the first layer of soil moisture, the RMSE of soil temperature was almost the same as assimilating 8-layers soil moisture, the model performance of soil moisture was improved obviously compared with the original CLM 3.0 modeling and simultaneously the abnormal values of flux simulation have been corrected. Assimilating the optimized AMSR-E soil moisture products turned out the model accuracy of soil tempetature did not decrease compared with assimilating the first layer of soil moisture, the soil moisture simulation was also improved and the model accuracy flux was enhanced to some extent. Before the assimilation of AMSR-E soil moisture products, the data should be done the localization correction.