Articles | Volume 18, issue 17
https://doi.org/10.5194/gmd-18-5575-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-18-5575-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
04 Sep 2025
Model description paper |
| 04 Sep 2025
| 04 Sep 2025
Huge ensembles – Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators
Ankur Mahesh
CORRESPONDING AUTHOR
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory (LBNL), Berkeley, California, USA
Department of Earth and Planetary Science, University of California, Berkeley, USA
William D. Collins
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory (LBNL), Berkeley, California, USA
Department of Earth and Planetary Science, University of California, Berkeley, USA
Boris Bonev
NVIDIA Corporation, Santa Clara, California, USA
Noah Brenowitz
NVIDIA Corporation, Santa Clara, California, USA
Yair Cohen
NVIDIA Corporation, Santa Clara, California, USA
Joshua Elms
Department of Earth and Atmospheric Sciences, Indiana University, Bloomington, Indiana, USA
Peter Harrington
National Energy Research Scientific Computing Center (NERSC), LBNL, Berkeley, California, USA
Karthik Kashinath
NVIDIA Corporation, Santa Clara, California, USA
Thorsten Kurth
NVIDIA Corporation, Santa Clara, California, USA
Joshua North
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory (LBNL), Berkeley, California, USA
Travis O'Brien
Department of Earth and Atmospheric Sciences, Indiana University, Bloomington, Indiana, USA
Michael Pritchard
NVIDIA Corporation, Santa Clara, California, USA
Department of Earth System Science, University of California, Irvine, USA
David Pruitt
NVIDIA Corporation, Santa Clara, California, USA
Mark Risser
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory (LBNL), Berkeley, California, USA
Shashank Subramanian
National Energy Research Scientific Computing Center (NERSC), LBNL, Berkeley, California, USA
Jared Willard
National Energy Research Scientific Computing Center (NERSC), LBNL, Berkeley, California, USA
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We draw from traditional climate modeling practices to make recommendations for machine-learning (ML)-driven climate science. Our intended audience is climate modelers who are relatively new to ML. We show how component-level understanding – obtained when scientists can link model behavior to parts within the overall model – should guide the development and evaluation of ML models. Better understanding yields a stronger basis for trust in the models. We highlight several examples to demonstrate.
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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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Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create
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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Mark D. Risser and Michael F. Wehner
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Evaluation of modern high-resolution global climate models often does not account for the geographic location of the underlying weather station data. In this paper, we quantify the impact of geographic sampling on the relative performance of climate model representations of precipitation extremes over the United States. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance.
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Short summary
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.
Simulating extreme weather events in a warming world is a challenging task for current weather...
