An Improved, Downscaled, Fine Model for Simulation of Daily Weather States |
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Authors: | JIANG Zhihong DING Yuguo ZHENG Chunyu and CHEN Weilin |
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Institution: | JIANG Zhihong 1,DING Yuguo 1,ZHENG Chunyu 1,2,and CHEN Weilin 1 1 Key Laboratory of Meteorological Disaster of Ministry of Education,Nanjing University of Information Science & Technology,Nanjing 210044 2 Environment Assessment,Northeast Electric Power Design Institute,Shenyang 110000 |
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Abstract: | 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. |
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Keywords: | stochastic simulation daily weather state series Markov chain state vector |
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