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Viewing 1-10 of 24 papers
Improving the reliability of ML-corrected climate models with novelty detection.
Clayton Sanford, Anna Kwa, Oliver Watt-Meyer, Spencer K. Clark, Noah D. Brenowitz, Jeremy McGibbon, and Christopher S. BrethertonESSOAr • 2023 The use of machine learning (ML) for the online correction of coarse-resolution atmospheric models has proven effective in reducing biases in near-surface temperature and precipitation rate. However, this often introduces biases in the upper atmosphere and…Pace v0.2: a Python-based performance-portable atmospheric model
Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky,Elynn Wu,Christopher Kung, Tal Ben-Nun, Lucas Harris , Linus Groner, Oliver FuhrerGeoscientific Model Development • 2023 Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software productivity gap. With the end of Moore's law driving forward…Machine-Learned Climate Model Corrections From a Global Storm-Resolving Model: Performance Across the Annual Cycle
Anna Kwa, Spencer K. Clark, Brian Henn, Noah D. Brenowitz, Jeremy McGibbon, Oliver Watt-Meyer, W. Andre Perkins, Lucas Harris, Christopher S. BrethertonJournal of Advances in Modeling Earth Systems • 2023 One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned (ML) state-dependent corrections to the prognosed model tendencies, such that the climate model evolves more like a reference fine-grid global storm…Old dog, new trick: Reservoir computing advances machine learning for climate modeling
Christopher S. BrethertonESSOAr • 2023 Physics-informed machine learning (ML) applied to geophysical simulation is developing explosively. Recently, graph neural net and vision transformer architectures have shown 1-7 day global weather forecast skill superior to any conventional model with…Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics
Liran Peng, Michael Pritchard, Peter N. Blossey, Walter M. Hannah, Christopher S. Bretherton, Christopher R. Terai, and Andrea M. JenneyESSOAr (submitted to the American Geophysical Union journal JAMES) • 2023 High-Resolution Multi-scale Modeling Frameworks (HR) -- global climate models that embed separate, convection-resolving models with high enough resolution to resolve boundary layer eddies -- have exciting potential for investigating low cloud feedback…Emulating Fast Processes in Climate Models
Noah Brenowitz, W. Perkins, J. M. Nugent, Oliver Watt‐Meyer, S. Clark, Anna Kwa, B. Henn, J. McGibbon, C. BrethertonNeurIPS•Machine Learning and Physical Sciences • 2022 Cloud microphysical parameterizations in atmospheric models describe the formation and evolution of clouds and precipitation, a central weather and climate process. Cloud-associated latent heating is a primary driver of large and small-scale circulations…Improving the predictions of ML-corrected climate models with novelty detection
Clayton Sanford, Anna Kwa, Oliver Watt‐Meyer, S. Clark, Noah Brenowitz, J. McGibbon, C. BrethertonNeurIPS•Climate Change AI • 2022 While previous works have shown that machine learning (ML) can improve the prediction accuracy of coarse-grid climate models, these ML-augmented methods are more vulnerable to irregular inputs than the traditional physics-based models they rely on. Because ML…Machine-learned climate model corrections from a global storm-resolving model
Anna Kwa, S. Clark, B. Henn, Noah Brenowitz, J. McGibbon, W. Perkins, Oliver Watt‐Meyer, L. Harris, C. BrethertonNeurIPS•Machine Learning and Physical Sciences • 2022 Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution ( (cid:38) 50 km) than is optimal for accurately resolving important physical processes. Such processes are approximated in GCMs via…Machine-learned climate model corrections from a global storm-resolving model: Performance across the annual cycle
Anna Kwa, Spencer. K. Clark, Brian Henn, Noah D. Brenowitz, Jeremy McGibbon, Oliver Watt-Meyer, W. Andre Perkins, Lucas Harris, and Christopher S. BrethertonESSOAr • 2022 One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned state-dependent corrections to the prognosed model tendencies, such that the climate model evolves more like a reference fine-grid global storm-resolving…Pace v0.1: A python-based performance-portable implementation of the FV3 dynamical core
Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, and Oliver FuhrerEGUsphere • 2022 Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software productivity gap. With the end of Moore's Law driving forward…