Zobrazeno 1 - 10
of 4 487
pro vyhledávání: '"Molina María"'
Autor:
Riera-Sadurní Josep, Cañameras Carles, Molina María, Urrutia Marina, Paul-Martínez Javier, Graterol Fredzzia, Perezpayá Inés, Omar Taco, Gelpi Rosana, Casas Ángela, Cañas Laura, Juega Javier, Tovar Gerardo, Sampere Jaume, Esteban Carlos, Areal Joan, González Satué Carlos, Bover Jordi, Vila Anna
Publikováno v:
Nefrología (English Edition), Vol 43, Iss , Pp 135-137 (2023)
Externí odkaz:
https://doaj.org/article/29821000774548d9a498119c34260eb1
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
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
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
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
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
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