通过陆面模式模拟得到的陆面参数精度易受强迫数据质量的影响, 为了提高基于NCEP/NCAR的强迫数据精度, 提出了一种新的强迫数据构造方法。该方法以静止气象卫星FY-2的反演产品——逐时降水估计和地面入射太阳辐射数据为基础, 结合部分NCEP/NCAR再分析数据构造用于陆面模式模拟的高时空分辨率强迫数据(FYDATA), 进一步利用陆面模式CLM3.0模拟得到较高精度的时空间连续的土壤湿度数据。与站点观测数据的比较表明, FYDATA的模拟结果在空间分布和时间变率上与观测数据均较为一致; 与再分析数据的模拟结果比较表明, 无论是月平均的站点尺度还是区域尺度, FYDATA的模拟结果都优于时空分辨率较粗的NCEP/NCAR再分析数据的模拟结果, 充分说明该数据构造方法的有效性。
The accuracy of land surface parameters, obtained from the land surface models, is always affected by the quality of atmospheric forcing data. To improve the performance of forcing data based on the NCEP/NCAR, this paper proposes a new method to construct an atmospheric forcing data (specifically called FYDATA) for land surface model CLM3.0. Partially together with the interpolated NCEP/NCAR reanalysis data, this method mainly utilizes the geo-stationary satellite FY-2 retrieved products including hourly estimated precipitation data and the surface incident solar radiation data, to form the forcing data with high spatial and temporal resolution. When forced by FYDATA, the CLM3.0 can produce continuous soil moisture datasets with good precision in time and spatial domain. These datasets are accordant in time variation and space distribution when compared with the simulation results using observation data, and better than those of NCEP/NCAR reanalysis data with large-scaled spatial and temporal resolution. All the evaluation results show that the forcing data construction method is effective.
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