基于DEM的中国陆地多年平均温度插值方法
收稿日期: 2003-08-07
修回日期: 2003-10-12
网络出版日期: 2004-05-25
基金资助
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)
Smart Distance Searching-based and DEM-informed Interpolation of Surface Air Temperature in China
Received date: 2003-08-07
Revised date: 2003-10-12
Online published: 2004-05-25
Supported by
National Natural Science Foundation of China, No.40371001; National High Technology Research and Development Program of China, No.2003AA131080
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1.51oC；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。
关键词： 温度；空间插值；DEM；智能方法；中国
潘耀忠,龚道溢,邓磊,李京,高静 . 基于DEM的中国陆地多年平均温度插值方法[J]. 地理学报, 2004 , 59(3) : 366 -374 . DOI: 10.11821/xb200403006
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%.
Key words： temperature; spatial interpolation; smart distance-searching method; DEM; China
[1] Willmott C J. On the evaluation of model performance in physical geography. In: Gaile G L, Willmott C J, D Reidel (eds.), Spatial Statistics and Models, 1984. 433-460.
[2] Willmott C J, Robeson S M, Feddema J J. Estimating continental and terrestrial precipitation averages from rain-gauge networks. Int. J. Climatol., 1994, 14: 403-414.
[3] Lin Zhonghui, Mo Xingguo, Li Hongxuan et al. Comparison of three spatial interpolation methods for climate variables in China. Acta Geographica Sinica, 2002, 57(1): 47-56.
[林忠辉, 莫兴国, 李宏轩 等. 中国陆地区域气象要素的空间插值. 地理学报, 2002, 57(1): 47-56.]
[4] Bennett R J. The problem of missing data on spatial surfaces. Ann. AssoC. Am. Geogr. oCean, 1984, 27(3): 521-541.
[5] Willmott C J, Kellji M. Smart interpolation of annually air temperature in the United States. J. Appl. Meteorol., 1999, 34: 2557-2586.
[6] Robeson S M. Influence of spatial sampling and interpolation on estimates of air temperature change. Climate Res., 1994, 4: 119-126.
[7] Ishida T, Kawashima K. Use of Cokriging to estimate surface air temperature from elevation. Thero. Appl. Climatol., 1993, 47: 147-157.
[8] Jones P D, Raper S C B, Bradley R S et al. Northern hemisphere surface air temperature variations. J. Clim. Appl. Meteorol., 1986a, 25: 161-179.
[9] Jones P D. Southern hemisphere surface air temperature variations. J. Clim. Appl. Meteorol., 1986b, 25: 1213-1230.
[10] Jones P D. Hemispheric surface air temperature variation: a reanalysis and update to 1993. J. Clim., 1994, 7: 1794-1802.
[11] Jones P D, Osborn T J, Briffa K R. Estimating sampling errors in large-scale temperature averages. J. Clim., 1997, 10: 2548-2568.
[12] Slonoskya V C, Jones P D, Daviesc T D. Atmospheric circulation and surface temperature in Europe from 18th century to 1995. Int. J. Climatol., 2001, 21: 63-75.
[13] Jones P D. An updated grid point surface air temperature anomaly data set: 1851-1990. J. Clim. Appl. Meteorol., 1991, NDP-020/R1, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, TN.
[14] Legates D R, Willmott C J. Mean seasonal and spatial variability in global surface air temperature. Thero. Appl. Climatol., 1990, 41: 11-21.
[15] Willmott C J, Robeson S M. Climatologically aided interpolation of terrestrial air temperature. Int. J. Climatol., 1995, 15: 221-229.
[16] Willmott C J, Philpot W D. Small-scale climate maps: a sensitivity analysis of some common assumptions assoCiated with grid-point interpolation and contouring. Amer. Cartogr., 1985a, 12: 5-16.
[17] Nalder I A, Wei R W. Spatial interpolation of climate normals: test of a new method in the Canadian boreal forest. Agric. For. Meteroral., 1998, 92: 211-225.
[18] Hutchinson M F. The application of thin-plate smoothing splines to continent-wide data assimilation. In: Jasper J D(ed.), Data Assimilation Systems. BMRC Res. Report No.27, Bureau of Meteorology, Melbourne, 1991. 104-113.
[19] Hutchinson M F. Interpolating mean rainfall using thin plate smoothing splines. Int. J. GIS, 1995, 9: 385-403.
[20] Hutchinson M F. Interpolation of rainfall data with thin plate smoothing splines I: two dimensional smoothing of data with short range correlation. J. Geographic Information Decision Analysis, 1998a, 2: 153-167.
[21] Hutchinson M F. Interpolation of rainfall data with thin plate smoothing splines II: analysis of topographic dependence. J. Geographic Information Decision Analysis, 1998b, 2: 168-185.
[22] Hutchinson M F. ANUSPLIN Version 4.0, 1999. http://cres.anu.edu.au/software/anusplin.html
[23] Price D T, Mickenny D W, Nalder I A. A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agric. For. Meteorol., 2000, 101: 81-94.
[24] Pan Y, Li X, Gong P et al. An integrative classification of vegetation in China based on NOAA/AVHRR and vegetation-climate indices of the Holdridge Life Zone. Int. J. Remote Sensing, 2003, 24: 1009-1027.
[25] Zhang Xinshi. The potential evapotranspiration index for vegetation and vegetation-climate classification (II). Acta Phytoecologica et Geobotanica Sinica, 1989, 13(4): 107-207.
[张新时. 植被的PE 指标与植被－气候分类 (二). 植物生态学与地植物学报, 1989, 13(4): 107-207.]
[26] Efron B, Gong G. A leisurely look at the Bootstrap, the Jackknife and Cross-validation. Am. Stat., 1983, 37: 181-200.
[27] Robeson S M. Spatial interpolation network bias and terrestrial air temperature variability. Publ. Climatol., 1993, 46: 1-51.
/
| 〈 |
|
〉 |