陆面数据同化方法在绿洲农田土壤温湿度模拟中的应用

为准确预测绿洲农田土壤水分的变化以利于合理分配有限的水资源，该文利用耦合于中尺度大气数值模式MM5和WRF中的Noah Lsm与集合卡尔曼滤波方法建立了一维的陆面数据同化系统，将其应用到绿洲农田土壤的水热模拟研究。模拟结果表明：在陆面数据同化系统中同化土壤湿度后，不仅提高了绿洲农田土壤湿度的模拟精度，还可在一定程度上提高土壤温度和潜热的模拟性能；在同化土壤湿度的基础上进一步同化土壤温度，可显著提高绿洲农田土壤温度的模拟精度。若将研究结果进一步应用到中尺度大气数值模式MM5和WRF，可以较好地监测大面积的土壤温湿度变化以便做出合理的水资源调配。

Application of landsurface data assimilation into the simulation of soil moisture and temperature of croplands in oasis

Precisely predicting soil moisture can benefit the reasonable distribution of the limited water resources. A one dimensional landsurface data assimilation system was applied to water and heat transfer study over croplands in Jinta oasis, which was a combination of ensemble Kalman filter method and Noah Lsm (Community Noah Land Surface Model, N: National Center for Environmental Prediction, O: Oregon State University, A: Air Force, H: Hydrology Research Lab) coupled in the mesoscale models MM5 (Mesoscale Model version 5) and WRF (Weather Forecast and Research model). The results showed the improvements not only on the simulated soil moisture, but also the simulation of soil temperature and latent heat flux in the experiment of assimilation of the soil moisture of shallow layers. The simulated soil temperature can be significantly improved after the assimilation of the soil temperature. If the study is applied into the mesoscale model in the future, the result will benefit distributing water resources efficiently by means of predicting soil moisture of a region accurately.