基于WRF和统计方法的气温空间尺度分析
收稿日期: 2013-02-28
修回日期: 2013-04-01
网络出版日期: 2013-08-08
基金资助
中国科学院战略性先导科技专项子课题“过去百年增暖对北方农牧交错带格局的影响”(XDA05090310)。
Spatial Scale Analysis of Temperature Based on WRF and Statistical Methods
Received date: 2013-02-28
Revised date: 2013-04-01
Online published: 2013-08-08
WRF模式作为一个中尺度气候模式,其分辨率从几米到几千公里,其自身的双向嵌套特征也为进行动力尺度下推提供了有力条件。本文利用WRF模式和传统的统计方法对研究区的气温进行尺度下推。首先,通过动力下推得到不同分辨率下的气温空间分布,并选取15个气象站点进行点对点验证,为了更明显观察不同尺度间的差异,对不同尺度的输出与ANUSPLIN插值结果进行比对,结果显示动力尺度下推中,分辨率越高模拟效果越好。其次,我们采用传统的统计下推方法,从27km下推到3km分辨率,并与WRF和ANUSPLIN插值在该尺度的结果进行对比分析,结果显示统计下推结果的趋势与动力下推的插值结果是一致的,但具有明显的马赛克效果,通过分析认为,这与统计方法的尺度下推只考虑高程信息的变化对气温的影响,而未考虑其他因素有关,如若在下推时加入更多的变量,如对温度有较大影响的坡度、坡向、土地覆被类型等因素,综合分析不同尺度之间的关系,会使下推结果有所改善。
于灵雪, 张树文, 刘廷祥, 卜坤, 杨久春 . 基于WRF和统计方法的气温空间尺度分析[J]. 地球信息科学学报, 2013 , 15(4) : 546 -553 . DOI: 10.3724/SP.J.1047.2013.00546
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
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