Huge ensembles – Part 2: Properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators

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

doi:https://doi.org/10.5194/gmd-18-5605-2025

Articles | Volume 18, issue 17

https://doi.org/10.5194/gmd-18-5605-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Theoretical and computational aspects of ensemble design,...

https://doi.org/10.5194/gmd-18-5605-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 17

Model description paper

|

04 Sep 2025

Model description paper |

| 04 Sep 2025

Huge ensembles – Part 2: Properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators

Ankur Mahesh, William D. Collins, Boris Bonev, Noah Brenowitz, Yair Cohen, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis A. 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

Inference and Analysis Code 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

We use machine learning emulators to create a massive ensemble of simulated weather extremes. This ensemble provides a large sample size, which is essential to characterize the statistics of extreme weather events and study their physical mechanisms. Also, these ensembles can be beneficial to accurately forecast the probability of low-likelihood extreme weather.