一种地面气温的空间插值方法及其误差分析  

A Spatial Interpolation Method for Surface Air Temperature and Its Error Analysis

作　　者：刘宇[1] 陈泮勤[1] 张稳[1] 胡非[1] 

机构地区：[1]中国科学院大气物理研究所大气边界层物理和大气化学国家重点实验室,北京100029

出　　处：《大气科学》2006年第1期146-152,共7页Chinese Journal of Atmospheric Sciences

基　　金：中国科学院知识创新工程重大项目KZCX1-SW-01-19;国家自然科学基金资助项目40233030

摘　　要：根据最近邻域法和反向距离法的基本原理,利用空间卷积算法,采用结合中国大陆气象站点位置的截断高斯滤波算子作为距离权重方程,给出一种适合中国陆地区域的地面气温插值方法,并以300多个地面站记录的气温为例,使用交叉验证法分析了给定插值方法的误差分布,结果表明该插值方法比其他插值方法所得误差较小,能够很好地用于气象站点气象观测记录缺失的插补及其空间尺度的扩大化。Research efforts in the hydrological and ecological sciences are increasingly being directed towards the application of knowledge gained at small spatial scales to questions framed over larger domains. Consequently, there is a growing need for a new collection of research tools and methods designed with attention to the particular needs and constraints of large-scale studies. Reliable surface meteorological data are a basic requirement for hydrological and ecological research at any spatial scale, and are a particularly crucial component of studies of mass and energy transfer over large land surfaces. Our study of hydrological and ecological processes at regional and continental scales has been hindered by lack of a general method which meets the meteorological data requirements of such large-scale studies. Here it is presented that a method for generating surfaces air temperature over Chinese terrene regions by us. In our methods, it is borrowed from the nearest-neighbor method which asserts that the area of relative influence for a given observation should be inversely related to the local observation density, that is, a relatively isolated observation should influence predictions for a larger area than an observation in a data-rich region. In order to overcome the most serious fault of the nearest-neighbor method which generates a discontinuous surface, our method borrowed the assertion that the influence could decrease with increasing distance from an observation from the inverse distance method. Required inputs of our method include digital elevation data and observations of air temperature from ground-based meteorological stations. The spatial convolution of a truncated Gaussian filter with a surface containing the horizontal projections of Chinese meteorological station locations is adopted as our basic interpolation framework. A Gaussian function is chosen because it is simple to evaluate, and has the desired features of being both an inverse-distance algorithm and a smoothing filter. Sensitivity to the t

关 键 词：空间卷积 空间插值 地面气温 交叉验证 误差分析 

分 类 号：P423[天文地球—大气科学及气象学]