摘要
以1961~2000年全国726个气象站点旬平均温度为基础数据,在分析了多年月平均温度和年平均温度的空间分布与经度、纬度、高度的内在关系后,提出了一种基于DEM和智能搜索距离的温度空间插值方法(SSI),并与反距离平方(IDS) 等传统方法进行了对比。交叉验证结果表明:1) 传统的IDS方法最优结果的MAE范围是1.44 oC^1.63 oC,平均1.510C;而SSI温度插值方法的平均绝对误差为0.53 oC^0.92 oC,平均值0.69 oC,精度超过IDS等方法一倍以上。2) 随着距离的增大,站点间温度的相关性逐渐降低,会降低估算精度;小于一定的搜索半径,被估算点周围的相邻站点的数目逐渐减少,同样会降低插值的精度,因而对中国陆地部分温度插值而言,最优的空间插值搜索半径介于150~250 km之间。最后,结合DEM数据,生成了0.1o ×0.1o中国陆地区域多年月平均和年平均温度栅格图像数据集,该结果表明:利用SSI方法不仅可以生成高精度、高空间分辨率的网格温度结果,而且其插值结果能客观细致的反映温度随经度、纬度和高度梯度变化的地带性特征。
Statistical interpolation of the temperature for the missing points is one of the most popular approaches for generating high spatial resolution data sets. However, many interpolation methods used by previous studies are purely mathematic ways, without geographical significance being considered. In the present study the authors interpolate the monthly and annual mean temperature climatologies using 726-station observations in China, utilizing improved methods by taking into account geographical factors such as latitude, longitude, altitude. In addition, a smart distance-searching technique is adopted, which helps select the optimum stations on which the guess values at missing points are generated. Results show that the methods used here have evident advantages over the previous approaches. The mean absolute err of ordinary inverse-distance-squared (IDS) technique is in the range of 1.44-1.63oC, on average 1.51oC. The smart distance searching technique yield a MAE of 0.53-0.92oC, on average 0.69oC. Errors have been reduced as much as 50%.
出处
《地理学报》
EI
CSCD
北大核心
2004年第3期366-374,共9页
Acta Geographica Sinica
基金
国家自然科学基金资助项目(40371001)
国家科技部高新技术重点规划项目(2003AA131080)~~
关键词
温度
空间插值
DEM
智能方法
中国
temperature
spatial interpolation
smart distance-searching method
DEM
China
