Articles | Volume 15, issue 15
https://doi.org/10.5194/gmd-15-6259-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6259-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
12 Aug 2022
Model description paper |
| 12 Aug 2022
| 12 Aug 2022
Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs
California Institute of Technology, Pasadena, California, USA
Yassine Tissaoui
New Jersey Institute of Technology, Newark, New Jersey, USA
Simone Marras
New Jersey Institute of Technology, Newark, New Jersey, USA
Zhaoyi Shen
California Institute of Technology, Pasadena, California, USA
Charles Kawczynski
California Institute of Technology, Pasadena, California, USA
Simon Byrne
California Institute of Technology, Pasadena, California, USA
Kiran Pamnany
California Institute of Technology, Pasadena, California, USA
Maciej Waruszewski
Naval Postgraduate School, Monterey, California, USA
Thomas H. Gibson
University of Illinois Urbana–Champaign, Urbana–Champaign, Illinois, USA
Jeremy E. Kozdon
Naval Postgraduate School, Monterey, California, USA
Valentin Churavy
Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Lucas C. Wilcox
Naval Postgraduate School, Monterey, California, USA
Francis X. Giraldo
Naval Postgraduate School, Monterey, California, USA
Tapio Schneider
California Institute of Technology, Pasadena, California, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
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Machine learning is playing an increasingly important role in hydrological modeling. In this paper, we introduce an adaptation of existing machine learning models for simulating streamflow in river basins, redesigning them with the goal of integrating them in climate models. We demonstrate the effectiveness of our adapted model by showing that it outperforms a physics-based river model. These results motivate further studies of the use of machine-learning-based river models inside climate models.
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Climate models are crucial for predicting climate change in detail. This paper proposes a balanced approach to improving their accuracy by combining traditional process-based methods with modern artificial intelligence (AI) techniques while maximizing the resolution to allow for ensemble simulations. The authors propose using AI to learn from both observational and simulated data while incorporating existing physical knowledge to reduce data demands and improve climate prediction reliability.
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To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance computing infrastructure, we provide an accessible software package able to rapidly model tsunami inundation over real topography by leveraging Google's Tensor Processing Unit, a high-performance hardware. Minimally trained users can take advantage of the rapid modeling abilities provided by this package via a web browser thanks to the ease of use of Google Cloud Platform.
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Short summary
ClimateMachine is a new open-source Julia-language atmospheric modeling code. We describe its limited-area configuration and the model equations, and we demonstrate applicability through benchmark problems, including atmospheric flow in the shallow cumulus regime. We show that the discontinuous Galerkin numerics and model equations allow global conservation of key variables (up to sources and sinks). We assess CPU strong scaling and GPU weak scaling to show its suitability for large simulations.
ClimateMachine is a new open-source Julia-language atmospheric modeling code. We describe its...
