26 citations · 36 across the 7 of their papers we have counts for
8 papers
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…
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…
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…
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
John S. Schreck, David John Gagne, Charlie Becker +13
Robust quantification of predictive uncertainty is critical for understanding factors that drive weather and climate outcomes. Ensembles provide predictive uncertainty estimates an…
Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management
John S. Schreck, William Petzke, Pedro A. Jimenez +5
Monitoring the fuel moisture content (FMC) of vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations with numer…
Mimicking non-ideal instrument behavior for hologram processing using neural style translation
John S. Schreck, Matthew Hayman, Gabrielle Gantos +2
Holographic cloud probes provide unprecedented information on cloud particle density, size and position. Each laser shot captures particles within a large volume, where images can…