【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。更多还原

【Abstract】 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 typical heterogeneous distribution of stations in complex terrain is accomplished with an iterative station density algorithm.Cross-validation analysis is used to test the sensitivity of our method to variation of parameters and to estimate the prediction errors associated with the final selected parameters.The general cross-validation protocol is to withhold one observation at a time from a sample,generating a prediction error for the withheld case by comparison with the observed value,and repeating over all observations in the sample to generate an average prediction error.Mean absolute error(MAE) for predicted daily average air temperature is about 0.7 ℃.The results show that our spatial interpolation method produces less error than other methods used in Chinese terrene area.Our methods are designed to be independent of prediction at arbitrarily placed points,but the same methods could be applied to the generation of predicted values over an evenly spaced grid of prediction points.There is another level of abstraction involved in translating these predictions to areal totals or averages as determined by the area of grid boxes centered on the prediction points.That is to say,our method presented here is well useful for reinforcing the lacked records of weather stations and for scaling-up of the records.更多还原