Non‐Local Parameterization of Atmospheric Subgrid Processes With Neural Networks

Non‐Local Parameterization of Atmospheric Subgrid Processes With Neural Networks

Bibliographic Details
Main Authors:	Wang, Peidong, Yuval, Janni, O’Gorman, Paul A
Other Authors:	Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Format:	Article
Language:	English
Published:	American Geophysical Union (AGU) 2023
Online Access:	https://hdl.handle.net/1721.1/148137

Similar Items

Neural‐Network Parameterization of Subgrid Momentum Transport in the Atmosphere
by: Janni Yuval, et al.
Published: (2023-04-01)

Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision
by: Yuval, Janni, et al.
Published: (2021)

Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
by: Yuval, Janni, et al.
Published: (2020)

Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
by: Janni Yuval, et al.
Published: (2020-07-01)

Parameterization of subgrid plume dilution for use in large-scale atmospheric simulations
by: A. D. Naiman, et al.
Published: (2010-03-01)

A subgrid parameterization scheme for precipitation
by: S. Turner, et al.
Published: (2012-04-01)

Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation
by: Oliver Watt‐Meyer, et al.
Published: (2024-02-01)

Applications of deep learning to ocean data inference and subgrid parameterization
by: Bolton, T, et al.
Published: (2019)

Subgrid Parameterizations of Ocean Mesoscale Eddies Based on Germano Decomposition
by: Pavel Perezhogin, et al.
Published: (2023-10-01)

Benchmarking of Machine Learning Ocean Subgrid Parameterizations in an Idealized Model
by: Andrew Ross, et al.
Published: (2023-01-01)

Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization
by: Thomas Bolton, et al.
Published: (2019-01-01)

Parameterization for subgrid-scale motion of ice-shelf calving fronts
by: T. Albrecht, et al.
Published: (2011-01-01)

Climate impacts of parameterizing subgrid variation and partitioning of land surface heat fluxes to the atmosphere with the NCAR CESM1.2
by: M. Yin, et al.
Published: (2023-01-01)

Stochastic parameterization of subgrid-scale velocity enhancement of sea surface fluxes
by: Bessac, J, et al.
Published: (2019)

A Gradient Based Subgrid‐Scale Parameterization for Ocean Mesoscale Eddies
by: Sina Khani, et al.
Published: (2023-02-01)

Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1
by: P.-L. Ma, et al.
Published: (2022-04-01)

Subgrid Parameterization of Eddy, Meanfield and Topographic Interactions in Simulations of an Idealized Antarctic Circumpolar Current
by: V. Kitsios, et al.
Published: (2023-05-01)

Generative Data‐Driven Approaches for Stochastic Subgrid Parameterizations in an Idealized Ocean Model
by: Pavel Perezhogin, et al.
Published: (2023-10-01)

Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography
by: R. Pimentel, et al.
Published: (2017-02-01)

Data-driven subgrid scale modelling with neural networks
by: Gangu,Vaishnavi
Published: (2020)

A simplified PDF parameterization of subgrid‐scale clouds and turbulence for cloud‐resolving models
by: Peter A. Bogenschutz, et al.
Published: (2013-06-01)

Spatial Covariance Modeling for Stochastic Subgrid‐Scale Parameterizations Using Dynamic Mode Decomposition
by: Federica Gugole, et al.
Published: (2020-08-01)

Finite-volume models with implicit subgrid-scale parameterization for the differentially heated rotating annulus
by: Sebastian Borchert, et al.
Published: (2015-01-01)

Landsurface hydrology parameterization for atmospheric general circulation models : inclusion of subgrid scale spatial variability and screening with a simple climate model
by: Entekhabi, Dara
Published: (2005)

Landsurface Hydrology Parameterization for Atmospheric General Circulation Models: Inclusion of Subgrid Scale Spatial Variability and Screening with a Simple Climate Model
by: Entekhabi, Dara, et al.
Published: (2022)

Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events
by: O'Gorman, Paul, et al.
Published: (2022)

The Effective Static Stability Experienced by Eddies in a Moist Atmosphere
by: O'Gorman, Paul
Published: (2011)

Scale‐aware space‐time stochastic parameterization of subgrid‐scale velocity enhancement of sea surface fluxes
by: Bessac, J, et al.
Published: (2021)

Response of extreme precipitation to uniform surface warming in quasi-global aquaplanet simulations at high resolution
by: O'Gorman, Paul Ambrose, et al.
Published: (2021)

Mass-flux subgrid-scale parameterization in analogy with multi-component flows: a formulation towards scale independence
by: J.-I. Yano
Published: (2012-11-01)

Alleviation of an Arctic Sea Ice Bias in a Coupled Model Through Modifications in the Subgrid‐Scale Orographic Parameterization
by: Guillaume Gastineau, et al.
Published: (2020-09-01)

Theoretical comparison of subgrid turbulence in atmospheric and oceanic quasi-geostrophic models
by: V. Kitsios, et al.
Published: (2016-04-01)

Atmospheric Boundary Layer Processes, Characteristics and Parameterization
by: Yubin Li, et al.
Published: (2023-04-01)

Artificial neural network-based subgrid-scale models for LES of compressible turbulent channel flow
by: Qingjia Meng, et al.
Published: (2023-01-01)

Spatial artificial neural network model for subgrid-scale stress and heat flux of compressible turbulence
by: Chenyue Xie, et al.
Published: (2020-01-01)

Upward Shift of the Atmospheric General Circulation under Global Warming: Theory and Simulations
by: Singh, Martin Simran, et al.
Published: (2013)

Tracer water transport and subgrid precipitation variation within atmospheric general circulation models
by: Koster, Randal Dean
Published: (2005)

A new dynamic subgrid-scale model using artificial neural network for compressible flow
by: Han Qi, et al.
Published: (2022-05-01)

On dynamic computational subgrid modeling
by: Hoffman, J, et al.
Published: (1999)

Entropy, Closures and Subgrid Modeling
by: Terence J. OÃ¢Â€Â™Kane, et al.
Published: (2008-11-01)