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David John Gagne II
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- affiliation: National Center for Atmospheric Research, Boulder, CO, USA
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2020 – today
- 2024
- [i9]Yingkai Sha, Ryan A. Sobash, David John Gagne II:
Improving ensemble extreme precipitation forecasts using generative artificial intelligence. CoRR abs/2407.04882 (2024) - 2023
- [j3]John S. Schreck
, William Petzke, Pedro A. Jimenez, Thomas Brummet
Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management. Remote. Sens. 15(13): 3372 (2023) - [i8]John S. Schreck, Matthew Hayman, Gabrielle Gantos, Aaron Bansemer, David John Gagne
Mimicking non-ideal instrument behavior for hologram processing using neural style translation. CoRR abs/2301.02757 (2023) - [i7]John S. Schreck, William Petzke, Pedro A. Jimenez, Thomas Brummet, Jason C. Knievel, Eric James, Branko Kosovic, David John Gagne:
Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management. CoRR abs/2305.11910 (2023) - [i6]John S. Schreck, David John Gagne II
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications. CoRR abs/2309.13207 (2023) - [i5]Yingkai Sha, Ryan A. Sobash, David John Gagne II
Generative ensemble deep learning severe weather prediction from a deterministic convection-allowing model. CoRR abs/2310.06045 (2023) - [i4]Da Fan, Steven J. Greybush, David John Gagne II
Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations. CoRR abs/2310.16015 (2023) - 2022
- [i3]John S. Schreck, Gabrielle Gantos, Matthew Hayman, Aaron Bansemer, David John Gagne
Neural network processing of holographic images. CoRR abs/2203.08898 (2022) - 2021
- [i2]Amy McGovern, Imme Ebert-Uphoff, David John Gagne II, Ann Bostrom:
The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences. CoRR abs/2112.08453 (2021)
2010 – 2019
- 2019
- [i1]David John Gagne II, Hannah M. Christensen, Aneesh C. Subramanian, Adam H. Monahan:
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model. CoRR abs/1909.04711 (2019) - 2015
- [c5]David John Gagne II, Amy McGovern, Jerald Brotzge, Michael Coniglio, James Correia Jr., Ming Xue:
Day-Ahead Hail Prediction Integrating Machine Learning with Storm-Scale Numerical Weather Models. AAAI 2015: 3954-3960 - 2014
- [j2]Amy McGovern, David John Gagne II
Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning. Mach. Learn. 95(1): 27-50 (2014) - 2013
- [c4]David John Gagne II
Severe Hail Prediction within a Spatiotemporal Relational Data Mining Framework. ICDM Workshops 2013: 994-1001 - 2012
- [c3]David John Gagne II
Machine learning enhancement of Storm Scale Ensemble precipitation forecasts. CIDU 2012: 39-46 - 2011
- [j1]Amy McGovern, David John Gagne II
Using spatiotemporal relational random forests to improve our understanding of severe weather processes. Stat. Anal. Data Min. 4(4): 407-429 (2011) - 2010
- [c2]Amy McGovern, Timothy A. Supinie, David John Gagne II, Nathaniel Troutman, Matthew W. Collier, Rodger A. Brown, Jeffrey B. Basara, John K. Williams:
Severe Weather Processes through Spatiotemporal Relational Random Forests. CIDU 2010: 213-227
2000 – 2009
- 2008
- [c1]Amy McGovern, Nathan C. Hiers, Matthew W. Collier, David John Gagne II
Spatiotemporal Relational Probability Trees: An Introduction. ICDM 2008: 935-940
Coauthor Index
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