Výsledky vyhledávání - "MOLINA, MARIA"

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Recommendations for Comprehensive and Independent Evaluation of Machine Learning-Based Earth System Models

Autor: Ullrich, Paul A., Barnes, Elizabeth A., Collins, William D., Dagon, Katherine, Duan, Shiheng, Elms, Joshua, Lee, Jiwoo, Leung, L. Ruby, Lu, Dan, Molina, Maria J., O'Brien, Travis A.

Machine learning (ML) is a revolutionary technology with demonstrable applications across multiple disciplines. Within the Earth science community, ML has been most visible for weather forecasting, producing forecasts that rival modern physics-based

Externí odkaz: http://arxiv.org/abs/2410.19882

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Can Transfer Learning be Used to Identify Tropical State-Dependent Bias Relevant to Midlatitude Subseasonal Predictability?

Autor: Mayer, Kirsten J., Dagon, Katherine, Molina, Maria J.

Previous research has demonstrated that specific states of the climate system can lead to enhanced subseasonal predictability (i.e., state-dependent predictability). However, biases in Earth system models can affect the representation of these states

Externí odkaz: http://arxiv.org/abs/2409.10755

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An Earth-System-Oriented View of the S2S Predictability of North American Weather Regimes

Autor: Pérez-Carrasquilla, Jhayron S., Molina, Maria J.

It is largely understood that subseasonal-to-seasonal (S2S) predictability arises from the atmospheric initial state during early lead times, the land during intermediate lead times, and the ocean during later lead times. We examine whether this hypo

Externí odkaz: http://arxiv.org/abs/2409.08174

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Using Generative Artificial Intelligence Creatively in the Classroom: Examples and Lessons Learned

Autor: Molina, Maria J., McGovern, Amy, Perez-Carrasquilla, Jhayron S., Tanamachi, Robin L.

Although generative artificial intelligence (AI) is not new, recent technological breakthroughs have transformed its capabilities across many domains. These changes necessitate new attention from educators and specialized training within the atmosphe

Externí odkaz: http://arxiv.org/abs/2409.05176

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Estimating Epistemic and Aleatoric Uncertainty with a Single Model

Autor: Chan, Matthew A., Molina, Maria J., Metzler, Christopher A.

Estimating and disentangling epistemic uncertainty, uncertainty that is reducible with more training data, and aleatoric uncertainty, uncertainty that is inherent to the task at hand, is critically important when applying machine learning to high-sta

Externí odkaz: http://arxiv.org/abs/2402.03478

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

Autor: Schreck, John S., Gagne II, 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 uncertainty estimates and can be decomposed physically, but both physics and machine learning

Externí odkaz: http://arxiv.org/abs/2309.13207

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