Zobrazeno 1 - 10
of 245
pro vyhledávání: '"O'Gorman, Paul A."'
Autor:
Finkel, Justin, O'Gorman, Paul A.
A leading goal for climate science and weather risk management is to accurately model both the physics and statistics of extreme events. These two goals are fundamentally at odds: the higher a computational model's resolution, the more expensive are
Externí odkaz:
http://arxiv.org/abs/2402.01823
Publikováno v:
Journal of Advances in Modeling Earth Systems, 14(10), e2022MS002984
Subgrid processes in global climate models are represented by parameterizations which are a major source of uncertainties in simulations of climate. In recent years, it has been suggested that machine-learning (ML) parameterizations based on high-res
Externí odkaz:
http://arxiv.org/abs/2201.00417
Autor:
Beucler, Tom, Gentine, Pierre, Yuval, Janni, Gupta, Ankitesh, Peng, Liran, Lin, Jerry, Yu, Sungduk, Rasp, Stephan, Ahmed, Fiaz, O'Gorman, Paul A., Neelin, J. David, Lutsko, Nicholas J., Pritchard, Michael
Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model
Externí odkaz:
http://arxiv.org/abs/2112.08440
Autor:
Kohl, Matthieu1 (AUTHOR) mkohl@mit.edu, O'Gorman, Paul A.1 (AUTHOR)
Publikováno v:
Journal of the Atmospheric Sciences. Mar2024, Vol. 81 Issue 3, p545-559. 15p.
Tropical precipitation clusters exhibit power-law frequency distributions in area and volume (integrated precipitation), implying a lack of characteristic scale in tropical convective organization. However, it remains unknown what gives rise to the p
Externí odkaz:
http://arxiv.org/abs/2108.04457
Publikováno v:
Geophysical Research Letters, 48, e2020GL091363 (2021)
A promising approach to improve climate-model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms that are data-driven. However, neural networks (NNs) often lead to instab
Externí odkaz:
http://arxiv.org/abs/2010.09947
Autor:
Yuval, Janni, O'Gorman, Paul A.
Publikováno v:
Nature communications. 2020 Jul 3;11(1):1-0
Global climate models represent small-scale processes such as clouds and convection using quasi-empirical models known as parameterizations, and these parameterizations are a leading cause of uncertainty in climate projections. A promising alternativ
Externí odkaz:
http://arxiv.org/abs/2001.03151
Autor:
Li, Ziwei, O'Gorman, Paul
Publikováno v:
J. Climate (2020) 33 (16): 7125-7139
Precipitation extremes intensify in most regions in climate-model projections. Changes in vertical velocities contribute to the changes in intensity of precipitation extremes but remain poorly understood. Here, we find that mid-tropospheric vertical
Externí odkaz:
http://arxiv.org/abs/1910.05644
Autor:
O'Gorman, Paul A., Dwyer, John G.
Publikováno v:
Journal of Advances in Modeling Earth Systems, 10, 2548-2563, 2018
The parameterization of moist convection contributes to uncertainty in climate modeling and numerical weather prediction. Machine learning (ML) can be used to learn new parameterizations directly from high-resolution model output, but it remains poor
Externí odkaz:
http://arxiv.org/abs/1806.11037
Publikováno v:
Journal of Climate, 2021 Feb 01. 34(3), 1135-1146.
Externí odkaz:
https://www.jstor.org/stable/27076166