Search Results - "GagneII, David John"

Improving ensemble extreme precipitation forecasts using generative artificial intelligence by Sha, Yingkai, Sobash, Ryan A, GagneII, David John

“…An ensemble post-processing method is developed to improve the probabilistic forecasts of extreme precipitation events across the conterminous United States…”
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Generative ensemble deep learning severe weather prediction from a deterministic convection-allowing model by Sha, Yingkai, Sobash, Ryan A, GagneII, David John

“…An ensemble post-processing method is developed for the probabilistic prediction of severe weather (tornadoes, hail, and wind gusts) over the conterminous…”
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Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations by Fan, Da, Greybush, Steven J, GagneII, David John, Clothiaux, Eugene E

“…Convection initiation (CI) nowcasting remains a challenging problem for both numerical weather prediction models and existing nowcasting algorithms. In this…”
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Community Research Earth Digital Intelligence Twin (CREDIT) by Schreck, John, Sha, Yingkai, Chapman, William, Kimpara, Dhamma, Berner, Judith, McGinnis, Seth, Kazadi, Arnold, Sobhani, Negin, Kirk, Ben, GagneII, David John

“…Recent advancements in artificial intelligence (AI) for numerical weather prediction (NWP) have significantly transformed atmospheric modeling. AI NWP models…”
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The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences by McGovern, Amy, Ebert-Uphoff, Imme, GagneII, David John, Bostrom, Ann

“…Given the growing use of Artificial Intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we…”
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Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications by Schreck, John S, GagneII, David John, Becker, Charlie, Chapman, William E, Elmore, Kim, Fan, Da, Gantos, Gabrielle, Kim, Eliot, Kimpara, Dhamma, Martin, Thomas, Molina, Maria J, Pryzbylo, Vanessa M, Radford, Jacob, Saavedra, Belen, Willson, Justin, Wirz, Christopher

“…Robust quantification of predictive uncertainty is critical for understanding factors that drive weather and climate outcomes. Ensembles provide predictive…”
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Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model by GagneII, David John, Christensen, Hannah M, Subramanian, Aneesh C, Monahan, Adam H

“…Stochastic parameterizations account for uncertainty in the representation of unresolved sub-grid processes by sampling from the distribution of possible…”
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