3 citations · 4 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
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…
Investigating the use of terrain-following coordinates in AI-driven precipitation forecasts
Yingkai Sha, John S. Schreck, William Chapman +1
Artificial Intelligence (AI) weather prediction (AIWP) models often produce ``blurry'' precipitation forecasts. This study presents a novel solution to tackle this problem -- integ…
Improving AI weather prediction models using global mass and energy conservation schemes
Yingkai Sha, John S. Schreck, William Chapman +1
Artificial Intelligence (AI) weather prediction (AIWP) models are powerful tools for medium-range forecasts but often lack physical consistency, leading to outputs that violate con…