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High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator
Haiwen Guan, Dibyajyoti Chakraborty, Moein Darman +3
The proliferation of data-driven models in weather and climate sciences has marked a significant paradigm shift, with advanced models demonstrating exceptional skill in medium-rang…
OmniCast: A Masked Latent Diffusion Model for Weather Forecasting Across Time Scales
Tung Nguyen, Tuan Pham, Troy Arcomano +4
Accurate weather forecasting across time scales is critical for anticipating and mitigating the impacts of climate change. Recent data-driven methods based on deep learning have ac…
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…
Multimodal Atmospheric Super-Resolution With Deep Generative Models
Dibyajyoti Chakraborty, Haiwen Guan, Jason Stock +3
Score-based diffusion modeling is a generative machine learning algorithm that can be used to sample from complex distributions. They achieve this by learning a score function, i.e…
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-3D: A three-dimensional climate emulator for forced responses
Haiwen Guan, Troy Arcomano, Ashesh Chattopadhyay +1
We introduce LUCIE-3D, a lightweight three-dimensional climate emulator designed to capture the vertical structure of the atmosphere, respond to climate change forcings, and mainta…