基于数字高程模型的复杂地形下的黑龙江平均气温空间插值

Spatial Interpolation of Mean Temperature of Heilongjiang Province Based on Digital Elevation Model

空间化的气象资源被广泛应用于生态系统模拟与农业区划中,但常规插值模型对地形复杂、气象站点稀少的地区气象要素空间分布模拟的精度不高。本文在定量分析海拔高度、经纬度等因子对气温空间分布影响的基础上,选取黑龙江1957-2004年1月、4月、7月和10月的平均气温数据,利用DEM模型和相关辅助信息进行复杂地形条件下的气象资源空间分布模拟。精度交叉验证的结果表明,基于DEM辅助相关信息的插值精度明显高于常规的逆距离权重法、样条函数法、普通克里格法,插值精度提高了0.5-0.9℃。气温的空间分布趋势也更加合理,更好的体现出气温分布的空间异质性。

Meteorological data sets were widely used in ecological system modeling and zonation of agricultural resources. However, without geographical knowledge being considered, most present interpolation models were purely mathematical ways, so the result were usually very poor, especially in the district where just has less or no meteorological observation stations. Based on the relationships between mean temperature and geographical factors, a geographical knowledge aided spatial interpolation model was introduced to improve the spatial interpolation result. Monthly mean temperature data from 67 meteorological stations in Heilongjiang Province from 1957 to 2004 were interpolated by using Inverse Distance Weighting （IDW） , Ordinal Kriging （OK） , Spline and Knowledge Based Spatial Interpolation （KBSI）3 Cross-validation was applied to evaluate the four interpolation methods, from which the Mean Absolute Error （MAE）, Mean Relative Error （MRE） and Root Mean Squared Error （RMSE） were calculated. The resulted showed that KBSI had the minimal MAE and RMSE. The spatial distributions of monthly mean temperature were also reasonable.