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pro vyhledávání: '"Monahan, Adam H."'
This paper explores the application of emerging machine learning methods from image super-resolution (SR) to the task of statistical downscaling. We specifically focus on convolutional neural network-based Generative Adversarial Networks (GANs). Our
Externí odkaz:
http://arxiv.org/abs/2302.08720
Current models for spatial extremes are concerned with the joint upper (or lower) tail of the distribution at two or more locations. Such models cannot account for teleconnection patterns of two-meter surface air temperature ($T_{2m}$) in North Ameri
Externí odkaz:
http://arxiv.org/abs/2208.12092
In traditional extreme value analysis, the bulk of the data is ignored, and only the tails of the distribution are used for inference. Extreme observations are specified as values that exceed a threshold or as maximum values over distinct blocks of t
Externí odkaz:
http://arxiv.org/abs/2110.10046
Simultaneous concurrence of extreme values across multiple climate variables can result in large societal and environmental impacts. Therefore, there is growing interest in understanding these concurrent extremes. In many applications, not only the f
Externí odkaz:
http://arxiv.org/abs/2006.08720
Stochastic parameterizations account for uncertainty in the representation of unresolved sub-grid processes by sampling from the distribution of possible sub-grid forcings. Some existing stochastic parameterizations utilize data-driven approaches to
Externí odkaz:
http://arxiv.org/abs/1909.04711
Akademický článek
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Publikováno v:
Nonlin. Processes Geophys. 26, 2019
While nonlinear stochastic partial differential equations arise naturally in spatiotemporal modeling, inference for such systems often faces two major challenges: sparse noisy data and ill-posedness of the inverse problem of parameter estimation. To
Externí odkaz:
http://arxiv.org/abs/1904.05310
Autor:
Endo, Kota1 (AUTHOR) kotae@uvic.ca, Monahan, Adam H.1 (AUTHOR), Bessac, Julie2 (AUTHOR), Christensen, Hannah M.3 (AUTHOR), Weitzel, Nils4,5 (AUTHOR)
Publikováno v:
Monthly Weather Review. Oct2023, Vol. 151 Issue 10, p2587-2607. 21p.
Akademický článek
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Publikováno v:
In Weather and Climate Extremes June 2022 36