4 citations · 7 across the 4 of their papers we have counts for
5 papers
HiRO-ACE: Fast and skillful AI emulation and downscaling trained on a 3 km global storm-resolving model
W. Andre Perkins, Anna Kwa, Jeremy McGibbon +5
Kilometer-scale simulations of the atmosphere are an important tool for assessing local weather extremes and climate impacts, but computational expense limits their use to small re…
SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators
James P. C. Duncan, Elynn Wu, Surya Dheeshjith +15
Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land sur…
Applying the ACE2 Emulator to SST Green's Functions for the E3SMv3 Global Atmosphere Model
Elynn Wu, Finn Rebassoo, Pappu Paul +5
Green's functions are a useful technique for interpreting atmospheric state responses to changes in the spatial pattern of sea surface temperature (SST). Here we train version 2 of…
ACE2-SOM: Coupling an ML atmospheric emulator to a slab ocean and learning the sensitivity of climate to changed CO
Spencer K. Clark, Oliver Watt-Meyer, Anna Kwa +6
While autoregressive machine-learning-based emulators have been trained to produce stable and accurate rollouts in the climate of the present-day and recent past, none so far have…
ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses
Oliver Watt-Meyer, Brian Henn, Jeremy McGibbon +6
Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such as sea surface temper…