顾及GNSS水汽分布特性和站间距离的优化IDW插值方法  

An improved IDW interpolation method based on GNSS water vapor distribution and interstation distance

作　　者：闫香蓉 王晓春 梁薇 穆雅璇 宋雨龙 丁楠 张文渊 YAN Xiangrong;WANG Xiaochun;LIANG Wei;MU Yaxuan;SONG Yulong;DING Nan;ZHANG Wenyuan(School of Geography,Geomatics and Planning,Jiangsu Normal University,Xuzhou 221116;School of Environment and Spatial Informatics,China University of Mining and Technology,Xuzhou 221116)

机构地区：[1]江苏师范大学地理测绘与城乡规划学院,徐州221116 [2]中国矿业大学环境与测绘学院,徐州221116

出　　处：《南京信息工程大学学报（自然科学版）》2022年第3期287-293,共7页Journal of Nanjing University of Information Science & Technology（Natural Science Edition）

基　　金：国家自然科学基金(41904013);江苏师范大学自然科学研究基金(19XSRS010);江苏省大学生创新创业训练计划(202010320126Y)。

摘　　要：针对大气可降水量(Precipitable Water Vapor,PWV)精细化过程中插值算法的选取,本文系统性地分析了线性插值三角网法、克里金插值法、空间反距离(Inverse Distance Weighting,IDW)插值法3种方法,并提出了顾及GNSS水汽特性和站间距离的优化IDW插值方法.该方法通过分析GNSS站点距离与大气水汽分布特性对插值结果的影响,进而对插值参数进行优化,使插值结果靠近高精度的观测值.利用2017年5—7月徐州连续运行参考站的GNSS实测数据与探空站数据对该方法进行分析,实验结果表明:顾及GNSS水汽特性和站间距离的优化IDW插值方法的标准差、平均绝对误差、平均相对误差、均方根误差都要低于其他3种经典插值方法,其中均方根误差分别降低了14.88%、15.70%、4.12%.此外,本文分析了暴雨天气下不同插值算法重构高分辨率大气水汽分布图的能力,发现采用优化IDW插值方法能够显著减小采样站点分布不均及降水量激增造成的插值误差.这表明优化方法有助于重构局部地区稀疏GNSS站网的高分辨率大气水汽分布图,改进监测能力.To select the interpolation algorithm for the refinement of Precipitable Water Vapor(PWV),this paper systematically analyzes three interpolation methods including the linear interpolation triangulation,the Kriging interpolation and the Inverse Distance Weighting(IDW)interpolation,and then proposes an improved IDW interpolation approach.First,both the influence of GNSS station distance and the distribution characteristics of atmospheric water vapor on the interpolation result is analyzed,which is then used to optimize the interpolation parameters thus make the interpolation result close to the high-precision observation value.Second,this approach is tested using GNSS data of Xuzhou continuously operated reference stations as well as the radiosonde data during the period of May to July 2017.The results demonstrate that the improved IDW interpolation approach outperforms the above three classical interpolation methods in standard deviation,mean absolute error,mean relative error,and Root Mean Square Error(RMSE).Specifically,the RMSE is lowered by 14.88%,15.70%and 4.12%,compared with the linear interpolation triangulation,the Kriging and the IDW interpolation,respectively.Moreover,the proposed interpolation approach has excellent ability in reconstructing the high-resolution atmospheric water vapor distribution map during storms,which can significantly reduce the interpolation error caused by the uneven distribution of sampling sites and the precipitation surge.The comparisons indicate that the improved IDW interpolation approach is conducive to reconstruct the high-resolution atmospheric water vapor distribution map for areas with sparse GNSS station network,thus to improve the capacity of extreme weather monitoring.

关 键 词：GNSS水汽反演 大气可降水量 空间插值 反距离加权法 

分 类 号：P228[天文地球—大地测量学与测量工程]