David John Gagne II

According to our database, David John Gagne II authored at least 18 papers between 2008 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Uncertainty Quantification of Wind Gust Predictions in the Northeast US: An Evidential Neural Network and Explainable Artificial Intelligence Approach.

Israt Jahan, John S. Schreck, David John Gagne, Charlie Becker, Marina Astitha

CoRR, February, 2025

2024

Community Research Earth Digital Intelligence Twin (CREDIT).

John S. Schreck, Yingkai Sha, William E. Chapman, Dhamma Kimpara, Judith Berner, Seth McGinnis, Arnold Kazadi, Negin Sobhani, Ben Kirk, David John Gagne II

CoRR, 2024

Improving ensemble extreme precipitation forecasts using generative artificial intelligence.

Yingkai Sha, Ryan A. Sobash, David John Gagne II

CoRR, 2024

2023

Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management.

John S. Schreck, William Petzke, Pedro A. Jimenez, Thomas Brummet, Jason C. Knievel, Eric James, Branko Kosovic, David John Gagne

Remote. Sens., July, 2023

Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations.

Da Fan, Steven J. Greybush, David John Gagne II, Eugene E. Clothiaux

CoRR, 2023

Generative ensemble deep learning severe weather prediction from a deterministic convection-allowing model.

Yingkai Sha, Ryan A. Sobash, David John Gagne II

CoRR, 2023

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications.

John S. Schreck, David John Gagne II, Charlie Becker, William E. Chapman, Kim Elmore, Gabrielle Gantos, Eliot Kim, Dhamma Kimpara, Thomas Martin, Maria J. Molina, Vanessa M. Pryzbylo, Jacob Radford, Belen Saavedra, Justin Willson, Christopher D. Wirz

CoRR, 2023

Mimicking non-ideal instrument behavior for hologram processing using neural style translation.

John S. Schreck, Matthew Hayman, Gabrielle Gantos, Aaron Bansemer, David John Gagne

CoRR, 2023

2022

Neural network processing of holographic images.

John S. Schreck, Gabrielle Gantos, Matthew Hayman, Aaron Bansemer, David John Gagne

CoRR, 2022

2021

The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences.

Amy McGovern, Imme Ebert-Uphoff, David John Gagne II, Ann Bostrom

CoRR, 2021

2019

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model.

David John Gagne II, Hannah M. Christensen, Aneesh C. Subramanian, Adam H. Monahan

CoRR, 2019

2015

Day-Ahead Hail Prediction Integrating Machine Learning with Storm-Scale Numerical Weather Models.

David John Gagne II, Amy McGovern, Jerald Brotzge, Michael Coniglio, James Correia Jr., Ming Xue

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning.

Amy McGovern, David John Gagne II, John K. Williams, Rodger A. Brown, Jeffrey B. Basara

Mach. Learn., 2014

2013

Severe Hail Prediction within a Spatiotemporal Relational Data Mining Framework.

David John Gagne II, Amy McGovern, Jerald Brotzge, Ming Xue

Proceedings of the 13th IEEE International Conference on Data Mining Workshops, 2013

2012

Machine learning enhancement of Storm Scale Ensemble precipitation forecasts.

David John Gagne II, Amy McGovern, Ming Xue

Proceedings of the 2012 Conference on Intelligent Data Understanding, 2012

2011

Using spatiotemporal relational random forests to improve our understanding of severe weather processes.

Amy McGovern, David John Gagne II, Nathaniel Troutman, Rodger A. Brown, Jeffrey B. Basara, John K. Williams

Stat. Anal. Data Min., 2011

2010

Severe Weather Processes through Spatiotemporal Relational Random Forests.

Amy McGovern, Timothy A. Supinie, David John Gagne II, Nathaniel Troutman, Matthew W. Collier, Rodger A. Brown, Jeffrey B. Basara, John K. Williams

Proceedings of the 2010 Conference on Intelligent Data Understanding, 2010

2008

Spatiotemporal Relational Probability Trees: An Introduction.

Amy McGovern, Nathan C. Hiers, Matthew W. Collier, David John Gagne II, Rodger A. Brown

Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008