1 citations · 1 across the 4 of their papers we have counts for
10 papers
Controllable Probabilistic Forecasting with Stochastic Decomposition Layers
John S. Schreck, William E. Chapman, Charlie Becker +6
AI weather prediction ensembles with latent noise injection and optimized with the continuous ranked probability score (CRPS) have produced both accurate and well-calibrated predic…
Bayesian Deep Learning for Convective Initiation Nowcasting Uncertainty Estimation
Da Fan, David John Gagne, Steven J. Greybush +3
This study evaluated the probability and uncertainty forecasts of five recently proposed Bayesian deep learning methods relative to a deterministic residual neural network (ResNet)…
Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control
Junjie Yu, John S. Schreck, David John Gagne +7
Reinforcement learning (RL)-based heating, ventilation, and air conditioning (HVAC) control has emerged as a promising technology for reducing building energy consumption while mai…
Improving Medium Range Severe Weather Prediction through Transformer Post-processing of AI Weather Forecasts
Zhanxiang Hua, Ryan Sobash, David John Gagne +2
Improving the skill of medium-range (3-8 day) severe weather prediction is crucial for mitigating societal impacts. This study introduces a novel approach leveraging decoder-only t…
CAMulator: Fast Emulation of the Community Atmosphere Model
William E. Chapman, John S. Schreck, Yingkai Sha +5
We introduce CAMulator version 1, an auto-regressive machine-learned (ML) emulator of the Community Atmosphere Model version 6 (CAM6) that simulates the next atmospheric state give…
Data-Driven Probabilistic Air-Sea Flux Parameterization
Jiarong Wu, Pavel Perezhogin, David John Gagne +4
Accurately quantifying air-sea fluxes is important for understanding air-sea interactions and improving coupled weather and climate systems. This study introduces a probabilistic f…