Research on accuracy assessment of urban rainfall spatial interpolation from gauges data

Changfeng Jing, Mingyi Du, Peipei Dai, Haiyang Wei, Hui Liu
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引用次数: 3

Abstract

Rainfall data is useful in many fields such as urban management, agriculture, and so on. Spatial interpolation is widely used to interpolation continue rainfall data from discrete rainfall gauges. The uncertainty in spatial interpolation is change in different region. Paper focus on urban small area of Beijing city, Xicheng District and analyses uncertainty of spatial interpolation from four aspects: rainfall gauge number, density, position, spatial interpolation methods. RMSE and cross-validation is adopted to evaluate the accuracy of interpolation and the lowest RMSE is taken as optimal. The results suggest that more gauges can get a good performance with low error compared to little stations; and dense gauges network gets high accuracy than sparse station. Ordinary kriging is simple than other method and has a good estimation (except co-kriging) in small area spatial interpolation. Co-kriging has a high accuracy in interpolation but complex in computation and must be considering in the other variables.
基于实测数据的城市降水空间插值精度评价研究
降雨数据在城市管理、农业等许多领域都很有用。空间插值被广泛用于插值离散雨量计的连续降雨数据。空间插值的不确定性在不同区域是不同的。本文以北京市西城区城区为研究对象,从雨量计数、雨量密度、雨量位置、空间插值方法四个方面分析了空间插值的不确定性。采用均方根误差和交叉验证对插值精度进行评价,以均方根误差最小为最优。结果表明,相对于少台站,多台站可以获得较好的性能和较低的误差;密集测量站比稀疏测量站精度高。普通克里格法比其他方法简单,在小面积空间插值中具有较好的估计效果(除协同克里格法外)。协同克里格插值法插值精度高,但计算复杂,必须考虑其他变量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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