复杂地形下TRMM降水数据的降尺度研究：以四川省为例

doi:10.3969/j.issn.1000-6362.2019.10.001

摘要/Abstract

摘要： TRMM数据是目前应用最广泛的卫星降水产品，其准确性已得到广泛验证和认可。但其相对较低的空间分辨率制约和阻碍了在各领域的进一步应用。本研究以降水空间分异显著的四川省为例，在综合考虑空间位置、地形等多个影响因素及其空间非平稳性特征的基础上，采用混合地理加权回归（MGWR）与克里格插值（Kriging）相结合的方法，建立一个兼顾多因素空间非平稳性特征的降尺度模型（MGWRK），对研究区域的TRMM年降水数据进行降尺度研究，并通过41个气象站点的实测数据对不同降尺度方法的结果进行对比验证。结果表明：（1）经过降尺度处理后，TRMM降水数据的空间分辨率从0.25°（约26km）提升至1km，数据的精细程度有了明显提升；（2）MGWRK模型综合运用了空间位置、地形等多个高分辨率的辅助信息，并进一步探究了不同影响因素对TRMM降水影响关系的空间非平稳性类型与特征。从多年平均及两个典型年份的验证结果看，MGWRK法比传统的重采样方法Bilinear法及基于OLS的全局回归克里格法具有更高的精度，降尺度结果的精度更接近TRMM原始数据；（3）构建的降尺度模型兼顾了提升空间分辨率和保持数据精确度两方面的要求，适用于四川省TRMM降水数据的降尺度研究，可为TRMM数据在小尺度的应用研究提供有效的数据支持。

关键词: TRMM降水数据, 降尺度, 混合地理加权回归, 四川省

Abstract: Precipitation data have became an indispensable part for agriculture, hydrological, meteorological, ecological and other environmental applications. Satellites obtain the earth's precipitation data from space through on-board sensors, which is playing a more and more important role in the data collection currently. Research increasingly suggests that satellite-derived precipitation products with their advantages in the continuity of spatial scale and high degree of prediction accuracy have vast space for development. It is well-known that use of the Tropical Rainfall Measuring Mission (TRMM) has been widely employed for obtaining global precipitation data recently due to its incomparable superiority to traditional method. However, the application is subject to certain restrictions by the relatively low spatial resolution (about 20?30km) of the data. Considering various influence factors such as spatial location and terrain and their spatial non-stationary characteristics, a case study on the application of mixed geographic weighted regression combined with Kriging interpolation (MGWRK) for spatial downscaling of the TRMM annual precipitation data was undertaken at Sichuan Province, Southwest China with a significant space differentiation of precipitation. And in the meantime, assessment of the downscaling results derived by different methods were carried out based on the measured data of 41 meteorological stations. Some results in this study showed that: (1) by use of the MGWRK model for downscaling, the spatial resolution of TRMM precipitation data was increased sharply from 0.25° (about 26km) to 1km, which can describe the spatial variation of precipitation more detailly and effectively in study area. (2) The MGWRK model not only attempted to use a combination of various auxiliary information with high-resolution such as spatial location and terrain, but also explored the characteristics of spatial stationary of the relationship between TRMM precipitation and its factors. From the assessment results of various downscaling approach to the TRMM data of mean annual values (1998?2017) and the two typical years’ values (the wet year at 1998 and the dry year at 2006), it was found that the MGWRK method can prove a higher accuracy compared with the OLS-based global regression Kriging (GRK) and the Bilinear resample (Bil) method and obtain a result that is more approximate to the original status. (3) The downscaling model presented in this paper considered the improvement of spatial resolution without compromising the maintaining accuracy and therefore it is obviously an approach available for the spatial downscaling of TRMM precipitation data in study area and contribute to define a foundation for the application of the TRMM data in small scale.

Key words: TRMM precipitation data, Spatial downscaling, Mix geographically weighted regression, Sichuan Province

李豪，雷苑. 复杂地形下TRMM降水数据的降尺度研究：以四川省为例[J]. 中国农业气象, 2019, 40(10): 607-619.

LI Hao, LEI Yuan. Spatial Downscaling of TRMM Precipitation Data in Areas of Complex Terrain: A Case Study in Sichuan Province[J]. Chinese Journal of Agrometeorology, 2019, 40(10): 607-619.

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