Progress and Challenge in Statistically Downscaling Climate Model Outputs

作者: 陈 杰 , 郭生练 , 陈 华 :武汉大学水资源与水电工程科学国家重点实验室,湖北 武汉; 许崇育 :武汉大学水资源与水电工程科学国家重点实验室,湖北 武汉;挪威奥斯陆大学地学系,奥斯陆,挪威;

关键词: 统计降尺度气候模式偏差校正随机天气发生器进展与挑战Statistical Downscaling Climate Model Bias Correction Stochastic Weather Generator Progress and Challenge


Abstract: Statistical downscaling is a process to build up statistical relationships between large-scale (usually 1˚-3˚ on latitude and longitude) climate model outputs and point/watershed-scale meteorological variables. It is an important technique to conduct climate change impact assessment for a specific site or a watershed. This paper systematically reviewed the recent advances in three fields related to statistical downscaling methods: perfect prognosis, model output statistics, and stochastic weather generator. Merits and draw-backs associated with each downscaling method were summarized. In addition, the challenges in pro-gressing statistical downscaling methods were stated, as well as the potential solutions. The contribution of this review is aimed at pointing out the direction of developing statistical downscaling methods and providing clues for climate change impact studies.

文章引用: 陈 杰 , 许崇育 , 郭生练 , 陈 华 (2016) 统计降尺度方法的研究进展与挑战。 水资源研究, 5, 299-313. doi: 10.12677/JWRR.2016.54037


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