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首页> 外文期刊>ISPRS International Journal of Geo-Information >A Double-Smoothing Algorithm for Integrating Satellite Precipitation Products in Areas with Sparsely Distributed In Situ Networks
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A Double-Smoothing Algorithm for Integrating Satellite Precipitation Products in Areas with Sparsely Distributed In Situ Networks

机译：具有稀疏分布原位网络的地区集成卫星降水产品的双平滑算法

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摘要

The spatial distribution of automatic weather stations in regions of western China (e.g., Tibet and southern Xingjiang) is relatively sparse. Due to the considerable spatial variability of precipitation, estimations of rainfall that are interpolated in these areas exhibit considerable uncertainty based on the current observational networks. In this paper, a new statistical method for estimating precipitation is introduced that integrates satellite products and in situ observation data. This method calculates the differences between raster data and point data based on the theory of data assimilation. In regions in which the spatial distribution of automatic weather stations is sparse, a nonparametric kernel-smoothing method is adopted to process the discontinuous data through correction and spatial interpolation. A comparative analysis of the fusion method based on the double-smoothing algorithm proposed here indicated that the method performed better than those used in previous studies based on the average deviation, root mean square error, and correlation coefficient values. Our results indicate that the proposed method is more rational and effective in terms of both the efficiency coefficient and the spatial distribution of the deviations.
机译：中国西部地区（例如西藏和新疆南部）自动气象站的空间分布相对稀疏。由于降水的空间差异很大，根据目前的观测网络，对这些地区内插的降雨的估算显示出相当大的不确定性。本文介绍了一种新的统计降水量的统计方法，该方法整合了卫星产品和原位观测数据。该方法基于数​​据同化理论计算栅格数据和点数据之间的差异。在自动气象站空间分布稀疏的地区，采用非参数核平滑方法通过校正和空间插值处理不连续数据。对此处提出的基于双平滑算法的融合方法的比较分析表明，基于平均偏差，均方根误差和相关系数值，该方法的性能优于以前的研究。我们的结果表明，无论是效率系数还是偏差的空间分布，该方法都更加合理有效。

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