›› 2013, Vol. 15 ›› Issue (4): 546-553.doi: 10.3724/SP.J.1047.2013.00546

• ARTICLES • Previous Articles     Next Articles

Spatial Scale Analysis of Temperature Based on WRF and Statistical Methods

YU Lingxue1,2, ZHANG Shuwen1, LIU Tingxiang1, BU Kun1, YANG Jiuchun1   

  1. 1. Northeast Institute of Geography and Agroecology, CA, Changchun 130012, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2013-02-28 Revised:2013-04-01 Online:2013-08-08 Published:2013-08-08


Due to intensified global warming, more and more global and regional issues become the focus of research. In the context of global scale change, the mechanism of how regional climate makes feedback to global environment change and how regional climate change influences large-scale environment in turn is a difficult point for climate research. However, the transformation between scales provides ideas to solve these difficulties. With the development of climate models, nesting regional climate model into global climate models to simulate is an effective downscaling way, however the resolution limitations of the regional climate model cause simulation accuracy of mountainous areas and other complex terrain much lower than expected. WRF model, a mesoscale climate model with resolution from several meters to thousands kilometers and with two-way nested features, provides opportunities for transforming between scales dynamically. In this paper, we firstly use the WRF model to simulate the temperature at different scales and compare with 15 climate-stations' in-site value. Through the comparison, we can conclude that the simulated values are more similar with the measured ones with the resolution becomes better. Then we used statistical downscaling method to downscale the temperature from 27km to 3km and compared with the WRF downscaling results and ANUSPLIN interpolation. The results showed that the downscaling from statistical method had the same tendency with the WRF downscaling and ANUSPLIN interpolation results, but compared with the latter, the former downscaling had obvious Mosaic effect. The statistical based downscaling method only considered the influence of elevation change on temperature not other factors, while the temperature is influenced by various factors such as slope, slope aspect and land cover types. In the further research we will consider more factors and analyze the relationship between temperature and related factors more comprehensively, in this way we can make the statistical downscaling more realistic.

Key words: temperature, statistical methods, WRF, spatial scale analysis, downscaling