Zobrazeno 1 - 10
of 1 127
pro vyhledávání: '"A. Beucler"'
Autor:
Beucler, Tom, Grundner, Arthur, Shamekh, Sara, Ukkonen, Peter, Chantry, Matthew, Lagerquist, Ryan
While the added value of machine learning (ML) for weather and climate applications is measurable, explaining it remains challenging, especially for large deep learning models. Inspired by climate model hierarchies, we propose that a full hierarchy o
Externí odkaz:
http://arxiv.org/abs/2408.02161
Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI-based weather models produces medium-range forecasts within seconds, with a skill similar to stat
Externí odkaz:
http://arxiv.org/abs/2406.09474
Autor:
Behrens, Gunnar, Beucler, Tom, Iglesias-Suarez, Fernando, Yu, Sungduk, Gentine, Pierre, Pritchard, Michael, Schwabe, Mierk, Eyring, Veronika
Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty
Externí odkaz:
http://arxiv.org/abs/2402.03079
Cloud radiative feedback impacts early tropical cyclone (TC) intensification, but limitations in existing diagnostic frameworks make them unsuitable for studying asymmetric or transient radiative heating. We propose a linear Variational Encoder-Decod
Externí odkaz:
http://arxiv.org/abs/2401.09493
Autor:
Gomez, Milton S., Beucler, Tom
While extensive guidance exists for ensuring the reproducibility of one's own study, there is little discussion regarding the reproduction and replication of external studies within one's own research. To initiate this discussion, drawing lessons fro
Externí odkaz:
http://arxiv.org/abs/2401.03736
Accurate and computationally-viable representations of clouds and turbulence are a long-standing challenge for climate model development. Traditional parameterizations that crudely but efficiently approximate these processes are a leading source of u
Externí odkaz:
http://arxiv.org/abs/2401.02098
We review how machine learning has transformed our ability to model the Earth system, and how we expect recent breakthroughs to benefit end-users in Switzerland in the near future. Drawing from our review, we identify three recommendations. Recommend
Externí odkaz:
http://arxiv.org/abs/2311.13691
Autor:
Lin, Jerry, Yu, Sungduk, Peng, Liran, Beucler, Tom, Wong-Toi, Eliot, Hu, Zeyuan, Gentine, Pierre, Geleta, Margarita, Pritchard, Mike
Machine-learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high-resolution physics without the cost of explicit simulation. However, the
Externí odkaz:
http://arxiv.org/abs/2309.16177
Autor:
Yu, Sungduk, Hu, Zeyuan, Subramaniam, Akshay, Hannah, Walter, Peng, Liran, Lin, Jerry, Bhouri, Mohamed Aziz, Gupta, Ritwik, Lütjens, Björn, Will, Justus C., Behrens, Gunnar, Busecke, Julius J. M., Loose, Nora, Stern, Charles I., Beucler, Tom, Harrop, Bryce, Heuer, Helge, Hillman, Benjamin R., Jenney, Andrea, Liu, Nana, White, Alistair, Zheng, Tian, Kuang, Zhiming, Ahmed, Fiaz, Barnes, Elizabeth, Brenowitz, Noah D., Bretherton, Christopher, Eyring, Veronika, Ferretti, Savannah, Lutsko, Nicholas, Gentine, Pierre, Mandt, Stephan, Neelin, J. David, Yu, Rose, Zanna, Laure, Urban, Nathan, Yuval, Janni, Abernathey, Ryan, Baldi, Pierre, Chuang, Wayne, Huang, Yu, Iglesias-Suarez, Fernando, Jantre, Sanket, Ma, Po-Lun, Shamekh, Sara, Zhang, Guang, Pritchard, Michael
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderstorms that occur on the sub-resolution scale. Hybrid methods combining
Externí odkaz:
http://arxiv.org/abs/2306.08754
Autor:
Iglesias-Suarez, Fernando, Gentine, Pierre, Solino-Fernandez, Breixo, Beucler, Tom, Pritchard, Michael, Runge, Jakob, Eyring, Veronika
Publikováno v:
Journal of Geophysical Research: Atmospheres, 129, e2023JD039202
Climate models are essential to understand and project climate change, yet long-standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid-scale processes, particularly clouds and conv
Externí odkaz:
http://arxiv.org/abs/2304.12952