Articles | Volume 17, issue 8
https://doi.org/10.5194/gmd-17-3533-2024
© Author(s) 2024. 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-17-3533-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
02 May 2024
Model description paper |
| 02 May 2024
| 02 May 2024
Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1
Ankur Mahesh
CORRESPONDING AUTHOR
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Department of Earth and Planetary Science, University of California, Berkeley, Berkeley, CA, USA
Travis A. O'Brien
Department of Earth and Atmospheric Sciences, Indiana University, Bloomington, IN, USA
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Burlen Loring
Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Abdelrahman Elbashandy
Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
William Boos
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Department of Earth and Planetary Science, University of California, Berkeley, Berkeley, CA, USA
William D. Collins
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Department of Earth and Planetary Science, University of California, Berkeley, Berkeley, CA, USA
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Simulating extreme weather events in a warming world is a challenging task for current weather and climate models. These models' computational cost poses a challenge in studying low-probability extreme weather. We use machine learning to construct a new probabilistic system. We give an in-depth explanation of how we constructed this system. We present a thorough pipeline to validate our method. Our method requires fewer computational resources than existing weather and climate models.
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Simulating extreme weather events in a warming world is a challenging task for current weather and climate models. These models' computational cost poses a challenge in studying low-probability extreme weather. We use machine learning to construct a new probabilistic system. We give an in-depth explanation of how we constructed this system. We present a thorough pipeline to validate our method. Our method requires fewer computational resources than existing weather and climate models.
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Ryan J. O'Loughlin, Dan Li, Richard Neale, and Travis A. O'Brien
Geosci. Model Dev., 18, 787–802, https://doi.org/10.5194/gmd-18-787-2025, https://doi.org/10.5194/gmd-18-787-2025, 2025
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Michael P. Byrne, William R. Boos, and Shineng Hu
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In this study we investigate why climate change is amplified in mountain regions, a phenomenon known as elevation-dependent warming (EDW). We examine EDW using observations and models and assess the roles of radiative forcing vs. internal variability in driving the historical signal. Using a forcing–feedback framework we also quantify for the first time the processes driving EDW on large scales. Our results have important implications for understanding future climate change in mountain regions.
Arjun Babu Nellikkattil, Danielle Lemmon, Travis Allen O'Brien, June-Yi Lee, and Jung-Eun Chu
Geosci. Model Dev., 17, 301–320, https://doi.org/10.5194/gmd-17-301-2024, https://doi.org/10.5194/gmd-17-301-2024, 2024
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Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
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ClimateNet– an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.
Travis A. O'Brien, Mark D. Risser, Burlen Loring, Abdelrahman A. Elbashandy, Harinarayan Krishnan, Jeffrey Johnson, Christina M. Patricola, John P. O'Brien, Ankur Mahesh, Prabhat, Sarahí Arriaga Ramirez, Alan M. Rhoades, Alexander Charn, Héctor Inda Díaz, and William D. Collins
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Researchers utilize various algorithms to identify extreme weather features in climate data, and we seek to answer this question: given a
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Short summary
Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. We train convolutional neural networks (CNNs) to detect ARs with an estimate of the uncertainty. We present a framework to generalize these CNNs to a variety of datasets of past, present, and future climate. Using a simplified simulation of the Earth's atmosphere, we validate the CNNs. We explore the role of ARs in maintaining energy balance in the Earth system.
Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions...
