11 citations · 13 across the 5 of their papers we have counts for
5 papers
Diffusion-Based, Data-Assimilation-Enabled Super-Resolution of Hub-height Winds
Xiaolong Ma, Xu Dong, Ashley Tarrant +5
High-quality observations of hub-height winds are valuable but sparse in space and time. Simulations are widely available on regular grids but are generally biased and too coarse t…
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…
A Deep Learning Approach to Probabilistic Forecasting of Weather
Nick Rittler, Carlo Graziani, Jiali Wang +1
We discuss an approach to probabilistic forecasting based on two chained machine-learning steps: a dimensional reduction step that learns a reduction map of predictor information t…
Fast and accurate learned multiresolution dynamical downscaling for precipitation
Jiali Wang, Zhengchun Liu, Ian Foster +3
This study develops a neural network-based approach for emulating high-resolution modeled precipitation data with comparable statistical properties but at greatly reduced computati…