Results 21 to 30 of about 7,980 (145)
Journal of Advances in Modeling Earth Systems, 2023 We address the question of how to use a machine learned (ML) parameterization in a general circulation model (GCM), and assess its performance both computationally and physically.
Cheng Zhang +5 more
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Impact of Stochastic Ocean Density Corrections on Air‐Sea Flux Variability
Geophysical Research Letters, 2023 Air‐sea flux variability has contributions from both ocean and atmosphere at different spatio‐temporal scales. Atmospheric synoptic scales and the air‐sea turbulent heat flux that they drive are well represented in climate models, but ocean mesoscales ...
Niraj Agarwal +4 more
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Resampling with neural networks for stochastic parameterization in multiscale systems
Physica D: Nonlinear Phenomena, 2021 27 pages, 17 figures ...
D.T. Crommelin (Daan) +1 more
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Frontiers in Earth Science, 2022 Proper representations of stochastic processes in tropical cyclone (TC) models are critical for capturing TC intensity variability in real-time applications.
Mahashweta Patra +3 more
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Stochastic Parameterization with Dynamic Mode Decomposition
, 2022 AbstractA physical stochastic parameterization is adopted in this work to account for the effects of the unresolved small-scale on the large-scale flow dynamics. This random model is based on a stochastic transport principle, which ensures a strong energy conservation. The dynamic mode decomposition (DMD) is performed on high-resolution data to learn a
Li, Long +2 more
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Global climate impacts of stochastic deep convection parameterization in the NCAR CAM5
Journal of Advances in Modeling Earth Systems, 2016 In this study, the stochastic deep convection parameterization of Plant and Craig (PC) is implemented in the Community Atmospheric Model version 5 (CAM5) to incorporate the stochastic processes of convection into the Zhang‐McFarlane (ZM) deterministic ...
Yong Wang, Guang J. Zhang
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Journal of Advances in Modeling Earth Systems, 2023 Subgrid parameterizations of mesoscale eddies continue to be in demand for climate simulations. These subgrid parameterizations can be powerfully designed using physics and/or data‐driven methods, with uncertainty quantification.
Pavel Perezhogin +2 more
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A stochastic maximum principle approach for reinforcement learning with parameterized environment
Journal of Computational Physics, 2023 In this work, we introduce a stochastic maximum principle (SMP) approach for solving the reinforcement learning problem with the assumption that the unknowns in the environment can be parameterized based on physics knowledge. For the development of numerical algorithms, we shall apply an effective online parameter estimation method as our exploration ...
Richard Archibald +2 more
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Intermittency in a stochastic parameterization of nonorographic gravity waves [PDF]
Journal of Geophysical Research: Atmospheres, 2014 AbstractA multiwave stochastic parameterization of nonorographic gravity waves (GWs), representing GWs produced by convection and a background of GWs in the midlatitudes, is tuned and tested against momentum fluxes derived from long‐duration balloon flights.
de la Cámara, A., Lott, F., Hertzog, A.
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Geophysical Research Letters, 2016 The Plant‐Craig (PC) stochastic convective parameterization scheme is implemented into the National Center for Atmospheric Research Community Atmosphere Model version 5 (CAM5) to couple with the Zhang‐McFarlane deterministic convection scheme.
Yong Wang +2 more
doaj +1 more source