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Theoretical and Applied Climatology

, Volume 133, Issue 3–4, pp 1009–1019 | Cite as

Comparison of two air temperature gridding methods over complex terrain in China

  • Guoping Shi
  • Zhian Sun
  • Xinfa Qiu
  • Yan Zeng
  • Peng Chen
  • Changjie Liu
Original Paper
  • 89 Downloads

Abstract

This paper evaluates the monthly mean air temperature over complex terrain in China determined using two methods: Australian National University spline and GridMet_Climate_Temperature (grid meteorological and climatic temperature (GMCT)) models. It is found that the macroscopic patterns of the temperature distributions by the two methods are very similar. The comparison of modelled temperatures with observations from 40 verification stations shows that both methods have the same value of total mean absolute bias errors (MABE) of 0.43 °C. Since the observational data used are from meteorological observational stations which are located on horizontal flat and open areas, these evaluation results only represent those on the flat open areas. In order to evaluate the effects of topographical factors on the temperature, a concept of relief amplitude is defined. The temperatures are grouped according to range of the relief amplitudes, and variation of temperature with relief amplitudes is worked out. It is found that the temperature difference between the two models varies significantly with relief amplitudes. The averaged maximum difference can be as large as 12 °C in January when the relief amplitude is greater than 700 m. The reasons for the temperature difference are investigated, and they are due to the effects of topographical slopes and aspects. One model (GMCT) includes these effects while the other does not. The evaluation results demonstrate that it is necessary to include the effects of topographical factors in the model simulation in order to produce realistic temperature distributions in the complex terrain areas. The distributions of temperature on the northern and southern slopes are determined using the GMCT model, and the results show that the temperatures on the southern slopes are clearly greater than those of the northern slopes, especially in winter. These results can be used as a guide for the reasonable and sustainable utilization of heat resources in the mountainous area.

Notes

Acknowledgements

Dr. Michael Naughton from the Science and Innovation group, Australian Bureau of Meteorology, is thanked for polishing the English of this manuscript. The anonymous reviewers have made many valuable comments and suggestions, which lead to improving the quality of this paper. The National Natural Science Foundation of China (41330529) and the Forth “333 High-Level Talents Training Project” of Jiangsu Province (BRA2014373) are also thanked.

References

  1. Chronopoulos KI, Tsiros IX, Alvertos N, Dimopoulos IF (2010) Estimation of microclimatic data in remote mountainous areas using an artificial neural network model-based approach. Glob NEST J 12(4):384–389Google Scholar
  2. Didari S, Norouzi H, Zand-Parsa S, Khanbilvardi R (2016) Estimation of daily minimum land surface air temperature using MODIS data in southern Iran. Theor Appl Climatol: 1–13Google Scholar
  3. Dodson R, Marks D (1997) Daily air temperature interpolated at high spatial resolution over a large mountainous region. Clim Res 8:1–20CrossRefGoogle Scholar
  4. Fu BP (1988) Simulation of the distribution of climatic elements in mountainous areas. Acta Meteor Sin 46:319–325 (In Chinese)Google Scholar
  5. He YJ, Qiu XF, Cao Y, Zeng Y (2014) Estimation of monthly average sunshine duration over China based on cloud fraction from MODIS satellite data. Curr Sci 107:2013–2018Google Scholar
  6. Hutchinson MF (2011) ANUSPLIN version 4.3. Centre for Resource and Environmental Studies, Australian National University. Available online at http://fennerschool.anu.edu.au/research/ publications/software-datasets/anusplin
  7. Jarvis CH, Stuart N (2001) A comparison among strategies for interpolating maximum and minimum daily air temperatures. Part II: The interaction between number of guiding variables and the type of interpolation method. J Appl Meteorol 40:1075–1084CrossRefGoogle Scholar
  8. Jeffrey SJ, Carter JO, Moodie KB, Beswick AR (2001) Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ Model Softw 16:309–330CrossRefGoogle Scholar
  9. Lennon JJ, Turner JR (1995) Predicting the spatial distribution of climate: temperature in Great Britain. J Anim Ecol 64:370–392CrossRefGoogle Scholar
  10. Li X, Cheng GD, Lu L (2003) Comparison study of spatial interpolation methods of air temperature over Qinghai-Xizang Plateau. Plateau Meteorol 22:565–573 (In Chinese)Google Scholar
  11. Li J, You SC, Huang JF (2006) Spatial interpolation method and spatial distribution characteristics of monthly mean temperature in China during 1961–2000. Ecol Environ 15:109–114Google Scholar
  12. Li YC, He ZM, Liu CX (2014) Review on spatial interpolation methods of temperature data from meteorological stations. Prog Geogr 33:1019–1028 (In Chinese)Google Scholar
  13. Lin ZH, Mo XG, Li HX, Li HB (2002) Comparison of three spatial interpolation methods for climate variables in china. Acta Geograph Sin 57:47–56 (In Chinese)Google Scholar
  14. Lipton AE, Ward JM (1997) Satellite-view biases in retrieved surface temperatures in mountain areas. Remote Sens Environ 60:92–100CrossRefGoogle Scholar
  15. Liu AL, Tang GA (2006) DEM based auto-classification of Chinese land form. J Geo-Inf Sci 8:8–14Google Scholar
  16. Liu Y, Chen PQ, Zhang W, Hu F (2006) A spatial interpolation method for surface air temperature and its error analysis. Chin J Atmos Sci 30:146–152 (In Chinese)Google Scholar
  17. Nalder IA, Wein RW (1998) Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agric For Meteorol 92:211–225CrossRefGoogle Scholar
  18. Neteler M (2010) Estimating daily land surface temperatures in mountainous environments by reconstructed MODIS LST data. Remote Sens 2:333–351CrossRefGoogle Scholar
  19. Pan YZ, Gong DY, Deng L, Li J, Gao J (2004) Smart distance searching-based and DEM-informed interpolation of surface air temperature in China. Acta Geograph Sin 59:366–374 (In Chinese)Google Scholar
  20. Price DT, McKenney DW, Nalder IA, Hutchinson MF, Kesteven JL (2000) A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agric For Meteorol 101:81–94CrossRefGoogle Scholar
  21. Robeson SM, Janis MJ (1998) Comparison of temporal and unresolved spatial variability in multiyear time-averages of air temperature. Clim Res 10:15–26CrossRefGoogle Scholar
  22. Thornton PE, Running SW, White MA (1997) Generating surfaces of daily meteorological variables over large regions of complex terrain. J Hydrol 190:214–251CrossRefGoogle Scholar
  23. Wang L, Wang PF, Liu AL, Li Y, Wu H, Luo YH (2015a) Spatial interpolation of mean temperature of Jiangsu province based on DEM. J Nanjing Univ Inf Sci Technol (Nat Sci Ed) 1:79–85 (In Chinese)Google Scholar
  24. Wang L, Wang PF, Yang SS, Wu H, Luo YH (2015b) The impact of EDM data on the accuracy on temperature interpolation. J Northwest Univ (Nat Sci Ed) 45:485–488 (In Chinese)Google Scholar
  25. You SC, Li J (2005) Study on error and its pervasion of temperature estimation. J Nat Resour 20:140–144Google Scholar
  26. Zeng Y, Qiu XF, Liu SM (2005a) Distributed modeling of extraterrestrial solar radiation over rugged terrains. Chin J Geophys 48:1028–1033 (In Chinese)Google Scholar
  27. Zeng Y, Qiu XF, Liu CM, Jiang AJ (2005b) Distributed modeling of direct solar radiation on rugged terrain of the Yellow River Basin. Acta Geograph Sin 15:680–688 (In Chinese)Google Scholar
  28. Zeng Y, Qiu XF, He YJ, Liu CM (2008) Distributed modeling of diffuse solar radiation over rugged terrain of the Yellow River Basin. Chin J Geophys 51:991–998 (In Chinese)CrossRefGoogle Scholar
  29. Zeng Y, Qiu XF, He YJ, Shi GP, Liu CM (2009) Distributed modeling of monthly air temperatures over the rugged terrain of the Yellow River Basin. Sci China Ser D Earth Sci 52:694–707CrossRefGoogle Scholar
  30. Zhang LW, Huang JF, Guo RF, Li XX, Sun WB, Wang XZ (2013) Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data. J Zhejiang Univ Sci B 14:144–161CrossRefGoogle Scholar
  31. Zhen JG, Zhao J (2005) A GIS method for regional accumulative temperatures interpolation. J Glaciol Geocryol 27:591–597 (In Chinese)Google Scholar

Copyright information

© Springer-Verlag GmbH Austria 2017

Authors and Affiliations

  • Guoping Shi
    • 1
  • Zhian Sun
    • 2
  • Xinfa Qiu
    • 3
  • Yan Zeng
    • 4
  • Peng Chen
    • 5
  • Changjie Liu
    • 6
  1. 1.School of Geography and Remote SensingNanjing University of Information Science & TechnologyNanjingChina
  2. 2.Science & Innovation Group, Australian Bureau of MeteorologyMelbourneAustralia
  3. 3.School of Applied MeteorologyNanjing University of Information Science & TechnologyNanjingChina
  4. 4.Jiangsu Climate CenterNanjingChina
  5. 5.Jiangsu Meteorological Information CenterNanjingChina
  6. 6.Zhejiang Climate CenterHangzhouChina

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