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Probabilistic Precipitation Downscaling with Optical Flow-Guided Diffusion
Prakhar Srivastava, Ruihan Yang, Gavin Kerrigan, Gideon Dresdner, Jeremy McGibbon, Christopher Bretherton, S. MandtarXiv • 2023 In climate science and meteorology, local precipitation predictions are limited by the immense computational costs induced by the high spatial resolution that simulation methods require. A common workaround is statistical downscaling (aka superresolution…A machine learning parameterization of clouds in a coarse-resolution climate model for unbiased radiation
Brian Henn, Y. R. Jauregui, Spencer K. Clark, Noah Brenowitz, J. McGibbon, Oliver Watt‐Meyer, Andrew G. Pauling, C. BrethertonESSOAr • 2023 Coarse-grid weather and climate models rely particularly on parameterizations of cloud fields, and coarse-grained cloud fields from a fine-grid reference model are a natural target for a machine-learned parameterization. We machine-learn the coarsened-fine…Global Precipitation Correction Across a Range of Climates Using CycleGAN
Jeremy J McGibbon, Spencer K. Clark, Brian Henn, Anna Kwa, Oliver Watt-Meyer, W. Andre Perkins, Christopher S. BrethertonESSOAr • 2023 Accurate precipitation simulations for various climate scenarios are critical for understanding and predicting the impacts of climate change. This study employs a Cycle-generative adversarial network (CycleGAN) to improve global 3-hour-average precipitation…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. BrethertonJAMES (Journal of Advances in Modeling Earth Systems) • 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. BrethertonGeophysical Research Letters • 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…A Global Survey of Rotating Convective Updrafts in the GFDL X‐SHiELD 2021 Global Storm Resolving Model
Lucas Harris, Linjiong Zhou, Alex Kaltenbaugh, Spencer K. Clark, Kai-Yuan Cheng, Christopher S. BrethertonJournal of Geophysical Research: Atmospheres • 2023 We present the global characteristics of rotating convective updrafts in the 2021 version of GFDL's eXperimental System for High‐resolution prediction on Earth‐to‐Local Domains (X‐SHiELD), a kilometer‐scale global storm resolving model (GSRM). Rotation is…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…