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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Jiang, Z., Ding, Y., Zheng, C. et al. An improved, downscaled, fine model for simulation of daily weather states. Adv. Atmos. Sci. 28, 1357–1366 (2011). https://doi.org/10.1007/s00376-011-0086-8
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DOI: https://doi.org/10.1007/s00376-011-0086-8