10 citations · 36 across the 15 of their papers we have counts for
19 papers
Evaluating LLMs for Translating Legacy Fortran Atmospheric Physics to GPU-Accelerated JAX
Iris Linck, John Dennis, John Schreck +2
Legacy Fortran physics packages in atmospheric models such as CESM, WRF, and E3SM pose a barrier to GPU modernization, while manual porting requires accelerator-programming experti…
Understanding Perturbed Parameter Ensemble Sensitivities Using A Contrastive Learning Approach
Da Fan, David John Gagne, Gregory S Elsaesser +5
Perturbed parameter ensembles (PPEs) reveal how physics parameters affect climate simulations, but interpreting parameter sensitivities across multivariate, spatially structured ou…
Conditional Tropical Cyclogenesis Rates via Rare-Event Sampling in a Neural Weather Emulator
John S. Schreck, William Chapman, Charlie Becker +1
We couple Forward Flux Sampling (FFS), a non-equilibrium rare-event technique from statistical mechanics, to a neural weather emulator (SDL-WXFormer, 1° grid spacing) to estimate c…
Toward a Scientific Discovery Engine for Weather and Climate Data: A Visual Analytics Workbench for Embedding-Based Exploration
Nihanth W. Cherukuru, Matt Rehme, Kirsten J. Mayer +4
Earth system science is producing increasingly large, high-dimensional datasets from both physics-based and AI-driven models. While embedding-based representations make these data…
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…
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…
Winter Precipitation Type Diagnosis and Uncertainty Quantification with a Physically Consistent Machine Learning Method
Charlie Becker, David John Gagne, Julie Demuth +8
Accurately forecasting winter precipitation type and its transitions is critical for high-impact decision making. However, existing methods struggle in thermodynamically ambiguous…
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…
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…
Uncertainty Quantification of Wind Gust Predictions in the Northeast United States: An Evidential Neural Network and Explainable Artificial Intelligence Approach
Israt Jahan, John S. Schreck, David John Gagne +2
Machine learning algorithms have shown promise in reducing bias in wind gust predictions, while still underpredicting high gusts. Uncertainty quantification (UQ) supports this issu…
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…
Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations
Da Fan, Steven J. Greybush, David John Gagne +1
Convection initiation (CI) nowcasting remains a challenging problem for both numerical weather prediction models and existing nowcasting algorithms. In this study, object-based pro…
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…