39 citations · 87 across the 6 of their papers we have counts for
6 papers
ACE: A fast, skillful learned global atmospheric model for climate prediction
Oliver Watt-Meyer, Gideon Dresdner, Jeremy McGibbon +9
Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a…
Improving the predictions of ML-corrected climate models with novelty detection
Clayton Sanford, Anna Kwa, Oliver Watt-Meyer +4
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 irre…
Machine-learned climate model corrections from a global storm-resolving model
Anna Kwa, Spencer K. Clark, Brian Henn +6
Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution ( km) than is optimal for accurately res…
Emulating Fast Processes in Climate Models
Noah D. Brenowitz, W. Andre Perkins, Jacqueline M. Nugent +6
Cloud microphysical parameterizations in atmospheric models describe the formation and evolution of clouds and precipitation, a central weather and climate process. Cloud-associate…
Productive Performance Engineering for Weather and Climate Modeling with Python
Tal Ben-Nun, Linus Groner, Florian Deconinck +11
Earth system models are developed with a tight coupling to target hardware, often containing specialized code predicated on processor characteristics. This coupling stems from usin…
Machine Learning Climate Model Dynamics: Offline versus Online Performance
Noah D. Brenowitz, Brian Henn, Jeremy McGibbon +5
Climate models are complicated software systems that approximate atmospheric and oceanic fluid mechanics at a coarse spatial resolution. Typical climate forecasts only explicitly r…