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pro vyhledávání: '"Meyer, Oliver"'
Autor:
Watt-Meyer, Oliver, Henn, Brian, McGibbon, Jeremy, Clark, Spencer K., Kwa, Anna, Perkins, W. Andre, Wu, Elynn, Harris, Lucas, Bretherton, Christopher S.
Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such as sea surface temperature and greenhouse gases. Here we present ACE2 (Ai2 Climate Emulator
Externí odkaz:
http://arxiv.org/abs/2411.11268
Data-driven deep learning models are transforming global weather forecasting. It is an open question if this success can extend to climate modeling, where the complexity of the data and long inference rollouts pose significant challenges. Here, we pr
Externí odkaz:
http://arxiv.org/abs/2406.14798
Autor:
Watt-Meyer, Oliver, Dresdner, Gideon, McGibbon, Jeremy, Clark, Spencer K., Henn, Brian, Duncan, James, Brenowitz, Noah D., Kashinath, Karthik, Pritchard, Michael S., Bonev, Boris, Peters, Matthew E., Bretherton, Christopher S.
Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existin
Externí odkaz:
http://arxiv.org/abs/2310.02074
Autor:
Sanford, Clayton, Kwa, Anna, Watt-Meyer, Oliver, Clark, Spencer, Brenowitz, Noah, McGibbon, Jeremy, Bretherton, Christopher
While previous works have shown that machine learning (ML) can improve the prediction accuracy of coarse-grid climate models, these ML-augmented methods are more vulnerable to irregular inputs than the traditional physics-based models they rely on. B
Externí odkaz:
http://arxiv.org/abs/2211.13354
Autor:
Kwa, Anna, Clark, Spencer K., Henn, Brian, Brenowitz, Noah D., McGibbon, Jeremy, Perkins, W. Andre, Watt-Meyer, Oliver, Harris, Lucas, Bretherton, Christopher S.
Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution (${\gtrsim}50$ km) than is optimal for accurately resolving important physical processes. Such processes are approximated i
Externí odkaz:
http://arxiv.org/abs/2211.11820
Autor:
Brenowitz, Noah D., Perkins, W. Andre, Nugent, Jacqueline M., Watt-Meyer, Oliver, Clark, Spencer K., Kwa, Anna, Henn, Brian, McGibbon, Jeremy, Bretherton, Christopher S.
Cloud microphysical parameterizations in atmospheric models describe the formation and evolution of clouds and precipitation, a central weather and climate process. Cloud-associated latent heating is a primary driver of large and small-scale circulat
Externí odkaz:
http://arxiv.org/abs/2211.10774
Autor:
Brenowitz, Noah D., Henn, Brian, McGibbon, Jeremy, Clark, Spencer K., Kwa, Anna, Perkins, W. Andre, Watt-Meyer, Oliver, Bretherton, Christopher S.
Climate models are complicated software systems that approximate atmospheric and oceanic fluid mechanics at a coarse spatial resolution. Typical climate forecasts only explicitly resolve processes larger than 100 km and approximate any process occurr
Externí odkaz:
http://arxiv.org/abs/2011.03081
Autor:
Watt‐Meyer, Oliver1 (AUTHOR) oliverwm@allenai.org, Brenowitz, Noah D.2 (AUTHOR), Clark, Spencer K.1,3 (AUTHOR), Henn, Brian1 (AUTHOR), Kwa, Anna1 (AUTHOR), McGibbon, Jeremy1 (AUTHOR), Perkins, W. Andre1 (AUTHOR), Harris, Lucas3 (AUTHOR), Bretherton, Christopher S.1 (AUTHOR)
Publikováno v:
Journal of Advances in Modeling Earth Systems. Feb2024, Vol. 16 Issue 2, p1-19. 19p.
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Akademický článek
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