10 citations · 20 across the 9 of their papers we have counts for
10 papers
Kilometer-Scale AI Downscaling of Atlantic Hurricanes with Generative Ensembles
Yingkai Sha, Talea L. Mayo, Ethan D. Gutmann +2
This study presents an AI-based dynamical downscaling system for Tropical Cyclones (TCs). The system incorporates an AI-based limited-area model that downscales 3-hourly low-resolu…
Hard conservation correctors can hide a degrading model when training autoregressive emulators
William E. Chapman, John Schreck, Yingkai Sha
AI weather and climate emulators increasingly incorporate physical principles into their formulation. One approach is to apply hard correctors that modify network outputs so that g…
AI-Based Regional Emulation for Kilometer-Scale Dynamical Downscaling
Yingkai Sha, Tracy Hertneky, Ethan Gutmann +6
An AI-based Limited-Area Model (LAM) is developed for dynamical downscaling over the Southern Great Plains and the southeastern United States, with strong generalization abilities…
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…
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…
Community Research Earth Digital Intelligence Twin (CREDIT)
John Schreck, Yingkai Sha, William Chapman +7
Recent advancements in artificial intelligence (AI) for numerical weather prediction (NWP) have significantly transformed atmospheric modeling. AI NWP models outperform traditional…
Improving ensemble extreme precipitation forecasts using generative artificial intelligence
Yingkai Sha, Ryan A. Sobash, David John Gagne
An ensemble post-processing method is developed to improve the probabilistic forecasts of extreme precipitation events across the conterminous United States (CONUS). The method com…
Generative ensemble deep learning severe weather prediction from a deterministic convection-allowing model
Yingkai Sha, Ryan A. Sobash, David John Gagne
An ensemble post-processing method is developed for the probabilistic prediction of severe weather (tornadoes, hail, and wind gusts) over the conterminous United States (CONUS). Th…