Jeremy McGibbon

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All published works
Action Title Year Authors
+ PDF Chat ACE2-SOM: Coupling to a slab ocean and learning the sensitivity of climate to changes in CO$_2$ 2024 Spencer K. Clark
Oliver Watt‐Meyer
Anna Kwa
Jeremy McGibbon
Brian Henn
W. A. Perkins
Elynn Wu
Christopher S. Bretherton
Lucas Harris
+ PDF Chat ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses 2024 Oliver Watt‐Meyer
Brian Henn
Jeremy McGibbon
Spencer K. Clark
Anna Kwa
W. A. Perkins
Elynn Wu
Lucas Harris
Christopher S. Bretherton
+ ACE: A fast, skillful learned global atmospheric model for climate prediction 2023 Oliver Watt‐Meyer
Gideon Dresdner
Jeremy McGibbon
Spencer K. Clark
Brian Henn
John S. Duncan
Noah Brenowitz
Karthik Kashinath
Michael S. Pritchard
Boris Bonev
+ Probabilistic Precipitation Downscaling with Optical Flow-Guided Diffusion 2023 Prakhar Srivastava
Ruihan Yang
Gavin Kerrigan
Gideon Dresdner
Jeremy McGibbon
Christopher S. Bretherton
Stephan Mandt
+ PDF Chat Productive Performance Engineering for Weather and Climate Modeling with Python 2022 Tal Ben‐Nun
Linus Groner
Florian Deconinck
Tobias Wicky
Eddie C. Davis
Johann Dahm
Oliver D. Elbert
Rhea C. George
Jeremy McGibbon
Lukas TrĂŒmper
+ Productive Performance Engineering for Weather and Climate Modeling with Python 2022 Tal Ben‐Nun
Linus Groner
Florian Deconinck
Tobias Wicky
Eddie Davis
Johann Dahm
Oliver D. Elbert
Rhea George
Jeremy McGibbon
Lukas TrĂŒmper
+ Emulating Fast Processes in Climate Models 2022 Noah Brenowitz
W. A. Perkins
Jacqueline M. Nugent
Oliver Watt‐Meyer
Spencer K. Clark
Anna Kwa
Brian Henn
Jeremy McGibbon
Christopher S. Bretherton
+ Machine-learned climate model corrections from a global storm-resolving model 2022 Anna Kwa
Spencer K. Clark
Brian Henn
Noah Brenowitz
Jeremy McGibbon
W. A. Perkins
Oliver Watt‐Meyer
Lucas Harris
Christopher S. Bretherton
+ Improving the predictions of ML-corrected climate models with novelty detection 2022 Clayton Sanford
Anna Kwa
Oliver Watt‐Meyer
Spencer K. Clark
Noah Brenowitz
Jeremy McGibbon
Christopher S. Bretherton
+ PDF Chat fv3gfs-wrapper: a Python wrapper of the FV3GFS atmospheric model 2021 Jeremy McGibbon
Noah Brenowitz
Mark Cheeseman
Spencer K. Clark
Johann Dahm
Eddie Davis
Oliver D. Elbert
Rhea George
Lucas Harris
Brian Henn
+ Machine Learning Climate Model Dynamics: Offline versus Online Performance 2020 Noah Brenowitz
Brian Henn
Jeremy McGibbon
Spencer K. Clark
Anna Kwa
W. A. Perkins
Oliver Watt‐Meyer
Christopher S. Bretherton
+ PDF Chat Single‐Column Emulation of Reanalysis of the Northeast Pacific Marine Boundary Layer 2019 Jeremy McGibbon
Christopher S. Bretherton
+ PDF Chat Single-Column Emulation of Reanalysis of the Northeast Pacific Marine Boundary Layer 2019 Jeremy McGibbon
Christopher S. Bretherton
Common Coauthors
Commonly Cited References
Action Title Year Authors # of times referenced
+ PDF Chat Deep learning to represent subgrid processes in climate models 2018 Stephan Rasp
Michael S. Pritchard
Pierre Gentine
3
+ PDF Chat Prognostic Validation of a Neural Network Unified Physics Parameterization 2018 Noah Brenowitz
Christopher S. Bretherton
3
+ PDF Chat A Fortran-Keras Deep Learning Bridge for Scientific Computing 2020 Jordan Ott
Mike Pritchard
Natalie Best
Erik Linstead
Milan Curcic
Pierre Baldi
2
+ PDF Chat A parallel Fortran framework for neural networks and deep learning 2019 Milan Curcic
2
+ Adam: A Method for Stochastic Optimization 2014 Diederik P. Kingma
Jimmy Ba
1
+ PDF Chat Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions 2020 Janni Yuval
Paul A. O’Gorman
1
+ Interpreting and Stabilizing Machine-Learning Parametrizations of Convection 2020 Noah Brenowitz
Tom Beucler
Michael S. Pritchard
Christopher S. Bretherton
1
+ PDF Chat Array programming with NumPy 2020 C. R. Harris
K. Jarrod Millman
Stéfan van der Walt
Ralf Gommers
Pauli Virtanen
David Cournapeau
Eric Wieser
Julian Taylor
Sebastian Berg
Nathaniel J. Smith
1
+ PDF Chat Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse‐Graining 2019 Noah Brenowitz
Christopher S. Bretherton
1
+ PDF Chat Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events 2018 Paul A. O’Gorman
J. G. Dwyer
1
+ PDF Chat Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision 2021 Janni Yuval
Paul A. O’Gorman
Chris Hill
1
+ Using Machine Learning at Scale in HPC Simulations with SmartSim: An Application to Ocean Climate Modeling 2021 Sam Partee
Matthew J. Ellis
Alessandro Rigazzi
Scott Bachman
Gustavo Marques
Andrew Shao
Benjamin Robbins
1
+ PDF Chat Domain-Specific Multi-Level IR Rewriting for GPU 2021 Tobias Gysi
Christoph MĂŒller
Oleksandr Zinenko
Stephan Herhut
Eddie C. Davis
Tobias Wicky
Oliver Fuhrer
Torsten Hoefler
Tobias Grosser
1
+ PDF Chat A data-centric optimization framework for machine learning 2022 Oliver Rausch
Tal Ben‐Nun
Nikoli Dryden
Andrei Ivanov
Shigang Li
Torsten Hoefler
1
+ Scikit-learn: Machine Learning in Python 2012 FabiĂĄn Pedregosa
Gaël Varoquaux
Alexandre Gramfort
Vincent Michel
Bertrand Thirion
Olivier Grisel
Mathieu Blondel
Peter Prettenhofer
Ron J. Weiss
Vincent Dubourg
1
+ PDF Chat Lifting C semantics for dataflow optimization 2022 Alexandru Calotoiu
Tal Ben‐Nun
Grzegorz Kwaƛniewski
Johannes de Fine Licht
Timo Schneider
Philipp Schaad
Torsten Hoefler
1
+ PDF Chat A “Vertically Lagrangian” Finite-Volume Dynamical Core for Global Models 2004 Shian‐Jiann Lin
1
+ On the Convergence of Adam and Beyond 2019 Sashank J. Reddi
Satyen Kale
Sanjiv Kumar
1
+ PDF Chat LFRic: Meeting the challenges of scalability and performance portability in Weather and Climate models 2019 Samantha V. Adams
Rupert Ford
M. Hambley
J. M. Hobson
Iva Kavčič
Christopher Maynard
Thomas Melvin
Eike H. MĂŒller
Steven Mullerworth
Andrew Porter
1