地球信息科学学报 ›› 2011, Vol. 13 ›› Issue (4): 539-548.doi: 10.3724/SP.J.1047.2011.00539

• 地理模型与模拟应用 • 上一篇    下一篇

气温插值中不同空间插值方法的适用性分析——以江苏省为例

彭彬1, 周艳莲1,2, 高苹3, 居为民2   

  1. 1. 南京大学地理与海洋科学学院,南京 210093;
    2. 南京大学国际地球系统科学研究所,南京 210093;
    3. 江苏省气象台,南京 210008
  • 收稿日期:2010-11-30 修回日期:2011-05-23 出版日期:2011-08-25 发布日期:2011-08-23
  • 通讯作者: 周艳莲(1980-),女,南京大学地理与海洋科学学院讲师,博士,主要从事陆地生态系统碳水循环研究。 E-mail:zhouyl@nju.edu.cn E-mail:zhouyl@nju.edu.cn
  • 基金资助:

    中国科学院资源与环境信息系统国家重点实验室开放基金(2009-2011);江苏高校优势学科建设工程资助项目;中央高校基本科研业务费专项资金。

Suitability Assessment of Different Interpolation Methods in the Gridding Process of Station Collected Air Temperature: A Case Study in Jiangsu Province, China

PENG Bin1, ZHOU Yanlian1,2, GAO Ping3, JU Weimin2   

  1. 1. School of Geographic and Oceanographic Sciences in Nanjing University, Nanjing 210093, China;
    2. The International Institute of Earth System Science in Nanjing University, Nanjing 210093, China;
    3. Jiangsu Meteorological Bureau, Nanjing 210008, China
  • Received:2010-11-30 Revised:2011-05-23 Online:2011-08-25 Published:2011-08-23

摘要: 气温是最重要的气象因子之一,空间插值为台站气象数据降尺度提供了有效方法。本文利用江苏省67个气象台站2003年的逐日气温资料计算逐月平均气温和年平均气温,结合空间分辨率为30m×30m的DEM数据,分别利用反距离权重法、张力样条插值法、普通克里格插值法和协同克里格插值法,对月和年平均气温进行插值,并利用交叉验证法对插值精度进行了验证。研究结果表明,考虑空间自相关性的普通克里格法的插值精度明显优于反距离权重法和张力样条插值法;而协同克里格法由于考虑了经纬度、距海岸距离和高程等影响气温空间分布的局地因素,其插值精度一般略优于普通克里格法,尤其是在站点稀疏的情况下,协同克里格的优势更加明显;由于受海陆分布和东亚季风的影响,江苏省气温的空间分布与距海岸距离有关,在利用协同克里格进行空间插值时,距海岸距离也是一个值得关注的因素。

关键词: 气温, 协同克里格, 普通克里格, 插值

Abstract: Air temperature is an important parameter observed in metrological stations, and there are many ways to improve the precision of air temperature interpolation result. In this paper by using the air temperature data at 67 meteorological stations in Jiangsu Province in the year of 2003 and digital elevation model (DEM) data with spatial resolution of 30m×30m, four common interpolation methods, including Inverse Distance Weighting (IDW), Spline with tension (Spline), Ordinary Kriging (OK) and Co-Kriging (CK), were used to interpolate the monthly and yearly mean air temperature and the precision of those four methods was compared by using cross validation method. The results showed that OK has a much higher precision than IDW and Spline, indicating the method accounting for spatial self-correlation is more accurate than others. Four auxiliary variables, including latitude, longitude, distance from the coast and elevation, were selected for CK, and correlation analysis showed that the monthly mean air temperatures are best correlated with latitude, and the three other variables followed. As the four variables are correlated with each other, principal component analysis (PCA) was conducted in this paper. The first principal component mainly representing longitude and distance from the coast and the second one mainly representing latitude were utilized as the optimized auxiliary variables for Co-Kriging interpolation in most months except March whose input is only the second one, April and July whose inputs are the second and the fourth principal components. The results indicated that the precision of CK which makes good use of related auxiliary factors is slightly higher than that of OK; while it is obviously better than OK where there are fewer stations and is a potential ideal method for air temperature interpolation. The results of this paper also showed that distance from the coast is a critical factor to the spatial pattern of air temperature in Jiangsu, China, which should be an auxiliary variable for CK.

Key words: Co-Kriging, Ordinary Kriging, gridding process, air temperature