Publications (20)
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…
Divide And Conquer: Learning Chaotic Dynamical Systems With Multistep Penalty Neural Ordinary Differential Equations
Dibyajyoti Chakraborty, Seung Whan Chung, Troy Arcomano +1
Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), wh…
Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting
Jason Stock, Troy Arcomano, Rao Kotamarthi
Diffusion models offer a physically grounded framework for probabilistic weather forecasting, but their typical reliance on slow, iterative solvers during inference makes them impr…
AERIS: Argonne Earth Systems Model for Reliable and Skillful Predictions
Väinö Hatanpää, Eugene Ku, Jason Stock +12
Generative machine learning offers new opportunities to better understand complex Earth system dynamics. Recent diffusion-based methods address spectral biases and improve ensemble…
LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles
Haiwen Guan, Troy Arcomano, Ashesh Chattopadhyay +1
We present a lightweight, easy-to-train, low-resolution, fully data-driven climate emulator, LUCIE, that can be trained on as low as years of -hourly ERA5 data. Unlike most…
Disentangling the effects of sea surface temperature and CO in global machine learned weather-climate emulators
Spencer K. Clark, Troy Arcomano, James P. C. Duncan +8
While previous versions of the Ai2 Climate Emulator (ACE) have been trained with CO as a forcing, they are only accurate within a narrow range of scenarios, for example climate…