Spatial exploration and interpolation of the surface precipitation data
降雨量地面观测数据空间探索与插值方法探讨

Various spatial interpolation methods are widely applied to climate map production.The quality of climate spatial interpolation depends on the spatial variation of climate factors,the spatial distribution of climate stations,and the interpolation method.For examining the relationships between station distributions,interpolation methods and interpolation quality,599 climate stations in Texas,US with 30-year precipitation records are collected and 27 station samples are designed by regular or random sampling.The spatial patterns of Annual,January and August precipitation data are investigated using exploring spatial analysis such as spatial statistics,spatial autocorrelation testing,and semivariogram modeling.Five methods,i.e.,Kriging,IDW,local polynomial,regularized spline and thin plate spline,are used in the spatial interpolation of Annual,January and August precipitation data for all the station samples.The interpolation results,in terms of cross-validation errors,known-point check errors,and linear regression of the known values versus predicted values,are compared and discussed.Four findings are generalized from this case study.First,precipitation data usually have patterns such as obvious spatial trend,high-level spatial autocorrelation and stable semivariogram model.Nevertheless,the spatial patterns may vary by sample stations and seasonal changes.Considering these spatial characteristics,the exploring spatial data analysis is necessary and essential for climate spatial interpolation.Second,increasing the sample size of climate stations,the interpolation accuracy will be improved.But at a reasonable number of stations,increasing the sample size,the interpolation accuracy will not be improved obviously.Third,when the observation samples are scarce,different methods usually give very different interpolation results.When the samples are relatively rich,general methods tend to create similar results.Fourth,considering the intrinsic limitations of the general spatial interpolation methods,the authors suggest to explore the local relationships between climate factors and geographic variations,and to develop a knowledge-based interpolation method by introducing geographic variables and local regression models.The weighted linear regression of precipitation versus elevation for northwest Texas and the geographic weighted regression for entire Texas have shown the potentials of such new approaches.It is also argued that exploring spatial data analysis and knowledge-based spatial interpolation are critical for high-quality climate data interpolation.