基于TRMM降水数据的空间降尺度模拟

本研究以黑河流域为例,通过在不同空间尺度上建立降水与影响因素的关系并选择最优尺度以此进行降尺度,建立了基于地理加权回归（GWR）与高精度曲面建模方法（HASM）相结合的跨尺度统计降尺度方法,对TRMM降水数据进行了降尺度模拟。最后,结合站点观测数据采用交叉验证法对降尺度结果进行了验证。结果表明,与传统的降尺度方法相比,考虑最优尺度的降尺度方法在一定程度上提高了降尺度结果的精度,同时表明对跨尺度过程中产生的误差进行修正可进一步提高结果精度。本研究所提出的方法可用于粗分辨率降水数据的降尺度模拟,可为站点稀疏地区或地形复杂地区高分辨率高精度降水数据的获取提供方法上的支持。

Downscaling of TRMM Satellite Precipitation Data

Precipitation data with high accuracy and high spatial resolution are very important for improving our understanding of basin-scale hydrology, agriculture and earth science, and are essential in characterizing the behavior of a catchment. Attaining accurate and high spatial resolution precipitation data is deemed necessary for environmental, meteorological, and hydrological applications. This study proposed a statistical downscaling method based on the geographical weighted regression method (GWR) and high accuracy surface modeling method (HASM) by selecting the optimal downscaling scale and considering the errors produced in the scale-change process. GWR can address the spatially heterogeneous relationships between precipitation and its influence factors, such as digital elevation model (DEM), normalized difference vegetation index (NDVI) and slope, at different spatial resolutions, whereas HASM is used to merge the cross-scale error fields that are produced from the downscaling process and meteorological observations.The method was used to downscale the TRMM precipitation dataset over the Heihe River basin (HRB) from 0.25^o to 1 km. Cross-validation method was used to validate the developed method combined with the station observations. Results showed that the proposed downscaling method performed better than the traditional downscaling method, which directly downscaled the TRMM products without considering the optimal downscaling scale. Besides, it was found that residual correction is necessary after the GWR-based downscaling method. The method proposed in this research can be used to downscale precipitation dataset with coarse resolution and could be applied to the areas with data-scare network and complex topography.