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An Improved, Downscaled, Fine Model for Simulation of Daily Weather States

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文摘 In this study, changes in daily weather states were treated as a complex Markov chain process, based on a continuous-time watershed model (soil water assessment tool, SWAT) developed by the Agricultural Research Service at the U.S. Department of Agriculture (USDA-ARS). A finer classification using total cloud amount for dry states was adopted, and dry days were classified into three states: clear, cloudy, and overcast (rain free). Multistate transition models for dry- and wet-day series were constructed to comprehensively downscale the simulation of regional daily climatic states. The results show that the finer, improved, downscaled model overcame the oversimplified treatment of a two-weather state model and is free of the shortcomings of a multistate model that neglects finer classification of dry days (i.e., finer classification was applied only to wet days). As a result, overall simulation of weather states based on the SWAT greatly improved, and the improvement in simulating daily temperature and radiation was especially significant.
来源 Advances in Atmospheric Sciences ,2011,28(6):1357-1366 【核心库】
DOI 10.1007/s00376-011-0086-8
关键词 stochastic simulation ; daily weather state series ; Markov chain ; state vector
地址

Nanjing University of Information Science & Technology, Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing, 210044

语种 英文
文献类型 研究性论文
ISSN 0256-1530
学科 大气科学(气象学)
基金 国家自然科学基金 ;  Natural Science Key Research of Jiangsu Province High Education ;  江苏高校优势学科建设工程 ;  National Key Technologies Research and Development Program ;  CMA Meteorological Special Science Foundation
文献收藏号 CSCD:4325079

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引证文献 1 篇

1 江志红 南京信息工程大学气候与气候变化研究进展回顾 大气科学学报,2020,43(5):785-801
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