Spatiotemporal interpolation of precipitation across Xinjiang, China using space-time CoKriging

Abstract

In various environmental studies, geoscience variables not only have the characteristics of time and space, but also are influenced by other variables. Multivariate spatiotemporal variables can improve the accuracy of spatiotemporal estimation. Taking the monthly mean ground observation data of the period 1960–2013 precipitation in the Xinjiang Uygur Autonomous Region, China, the spatiotemporal distribution from January to December in 2013 was respectively estimated by space-time Kriging and space-time CoKriging. Modeling spatiotemporal direct variograms and a cross variogram was a key step in space-time CoKriging. Taking the monthly mean air relative humidity of the same site at the same time as the covariates, the spatiotemporal direct variograms and the spatiotemporal cross variogram of the monthly mean precipitation for the period 1960–2013 were modeled. The experimental results show that the space-time CoKriging reduces the mean square error by 31.46% compared with the space-time ordinary Kriging. The correlation coefficient between the estimated values and the observed values of the space-time CoKriging is 5.07% higher than the one of the space-time ordinary Kriging. Therefore, a space-time CoKriging interpolation with air humidity as a covariate improves the interpolation accuracy.摘要在对地观测中,所研究的地学变量不仅具有时间、空间特征,还受其它变量的影响,采用多元 时空相关数据,可以提高时空估值的精度。以新疆区域为试验区, 利用1960–2013 年气象站的降水 量观测数据的月平均值, 采用时空克里金和时空协同克里金插值方法, 估计试验区2013 年1–12 月降 水量的时空分布情况。在使用时空协同克里金插值过程中,建立时空直接变异函数和协变异函数是时 空CoKriging 插值的关键一步。以该地区1960–2013 年月平均降水量为主变量,引入同时间同位置 的月平均空气相对湿度作为协变量,对降水量和空气相对湿度进行时空直接变异函数和时空交叉协变 异函数建模。实验结果表明,引入空气相对湿度作为协变量的时空协同克里金的插值方法比时空普通 克里金的插值方法的均方根误差降低了31.46%;引入空气相对湿度作为协变量的时空协同克里金的 插值方法的估计值与观测值的相关系数比时空普通克里金的插值方法的相关系数提高了5.07%。因此, 引入空气湿度作为协变量的时空协同克里金插值方法提高了插值精度。