降水空间插值方法应用研究:以甘肃省为例

空间插值算法旨在利用离散的观测点测量数据估算同一区域中未采样点的估计值,进而生成连续的空间表面模型。为了获得高精度的缺失数据估计值和高分辨率空间表面模型,提出了一种顾及多因素影响的自适应反距离加权插值算法(adaptive cluster gradient inverse-distance weighting, ACGIDW)。该算法以气象数据为例,顾及复杂地形因素、经纬度和高程对空间插值的影响,并根据采样点的空间分布模式对反距离加权算法中的距离衰减参数α进行自适应调整,提高了空间插值算法的精度和自适应性。采用两组实际气温和降水数据,运用交叉验证模型,对ACGIDW方法、其他反距离加权方法、普通克立金方法进行实验对比分析,验证了ACGIDW方法的优越性和可行性。

A Review of Comparative Studies of Spatial Interpolation Methods in Environmental Sciences:Performance and Impact Factors

Spatial interpolation is an approach to estimate data at an un-sampled points based on known observations at sampled stations and generate a continuous surface model. In order to obtain the estimated value for missing data and precise spatial surface models, it is necessary to develop a high-performance spatial interpolation method. Based on the typical adaptive inverse-distance weighting (AIDW) spatial interpolation method, a new method, called adaptive cluster gradient inverse-distance weighting (ACGIDW) is presented in this paper. Considering the effect of latitude, longitude, elevation and complex topography factors, this method offers a more accurate result, 1) it adjusts the distance-decay parameter in the IDW method to improve the adaptability of the ACGIDW according to the spatial distribution pattern of the stations; 2) it was tested by using two groups of different actual meteorological data. The experimental results demonstrate its superiority and feasibility.