Stochastic downscaling of precipitation with neural network conditional mixture models
نویسندگان
چکیده
منابع مشابه
Multisite downscaling of daily precipitation with a stochastic weather generator
Stochastic models of daily precipitation are useful both for characterizing different precipitation climates and for stochastic simulation of these climates in conjunction with agricultural, hydrological, or other response models. A simple stochastic precipitation model is used to downscale— i.e. disaggregate from area-average to individual station—precipitation statistics for 6 groups of 5 U.S...
متن کاملStatistical downscaling of precipitation
Papers published in Hydrology and Earth System Sciences Discussions are under open-access review for the journal Hydrology and Earth System Sciences Abstract Global Circulation Models (GCMs) are a major tool used for future projections of climate change using different emission scenarios. However, for assessing the hydrological impacts of climate change at the watershed and the regional scale, ...
متن کاملStatistical downscaling of precipitation through nonhomogeneous stochastic weather typing
We present a novel statistical downscaling method that provides accurate and relatively transparent simulations of local-scale precipitation characteristics. The method combines large-scale upper-air circulation with surface precipitation fields, and is based on a nonhomogeneous stochastic weather typing approach. Here we applay the method to downscale precipitation at 37 rain gauges in the sta...
متن کاملDownscaling of daily precipitation
Downscaling of daily precipitation with a stochastic weather generator for the subtropical region in South China Y. D. Chen, X. Chen, C.-Y. Xu, and Q. Shao Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China Department of Geosci...
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ژورنال
عنوان ژورنال: Water Resources Research
سال: 2011
ISSN: 0043-1397
DOI: 10.1029/2010wr010128