Articles | Volume 18, issue 17
https://doi.org/10.5194/gmd-18-5575-2025
https://doi.org/10.5194/gmd-18-5575-2025
Model description paper
 | 
04 Sep 2025
Model description paper |  | 04 Sep 2025

Huge ensembles – Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators

Ankur Mahesh, William D. Collins, Boris Bonev, Noah Brenowitz, Yair Cohen, Joshua Elms, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis O'Brien, Michael Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, and Jared Willard

Data sets

Trained Machine Learning Model Weights Ankur Mahesh et al. https://doi.org/10.5061/dryad.2rbnzs80n

Model code and software

Code for inference and analysis of the machine learning models Ankur Mahesh et al. https://doi.org/10.5061/dryad.2rbnzs80n

NVIDIA Earth2Studio NVIDIA https://github.com/NVIDIA/earth2studio

Huge ensembles part I design of ensemble weather forecasts with spherical Fourier neural operators; Huge ensembles part II properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators A. Mahesh et al. https://github.com/ankurmahesh/earth2mip-fork

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.
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