Productive Performance Engineering for Weather and Climate Modeling with Python. - Tal Ben-Nun; Linus Groner; Florian Deconinck; Tobias Wicky; Eddie Davis; Johann Dahm; Oliver Elbert; Rhea George; Jeremy McGibbon; Lukas Trümper; Elynn Wu; Oliver Fuhrer; et al. (SC, 2022)

Machine-learned climate model corrections from a global storm-resolving model. - Anna Kwa; Spencer K. Clark; Brian Henn; Noah D. Brenowitz; Jeremy McGibbon; W. Andre Perkins; Oliver Watt-Meyer; Lucas Harris; Christopher S. Bretherton (CoRR, 2022)

ACE: A fast, skillful learned global atmospheric model for climate prediction. - Oliver Watt-Meyer; Gideon Dresdner; Jeremy McGibbon; Spencer K. Clark; Brian Henn; James Duncan 002; Noah D. Brenowitz; Karthik Kashinath; Michael S. Pritchard; Boris Bonev; Matthew E. Peters; Christopher S. Bretherton (CoRR, 2023)

Probabilistic Precipitation Downscaling with Optical Flow-Guided Diffusion. - Prakhar Srivastava 003; Ruihan Yang; Gavin Kerrigan; Gideon Dresdner; Jeremy McGibbon; Christopher S. Bretherton; Stephan Mandt (CoRR, 2023)

Precipitation Downscaling with Spatiotemporal Video Diffusion. - Prakhar Srivastava 003; Ruihan Yang; Gavin Kerrigan; Gideon Dresdner; Jeremy McGibbon; Christopher S. Bretherton; Stephan Mandt (NeurIPS, 2024)

ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses. - Oliver Watt-Meyer; Brian Henn; Jeremy McGibbon; Spencer K. Clark; Anna Kwa; W. Andre Perkins; Elynn Wu; Lucas Harris; Christopher S. Bretherton (CoRR, 2024)