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Mahesh, Ankur, D. Collins, William 
ORCID: https://orcid.org/0000-0002-4463-9848, 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 and Willard, Jared
(2025)
Huge ensembles – Part 2: Properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators.
Geoscientific Model Development, 18 (17).
pp. 5605-5633.
ISSN 1991-9603
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Abstract
In Part 1, we created an ensemble based on spherical Fourier neural operators. As initial condition perturbations, we used bred vectors, and as model perturbations, we used multiple checkpoints trained independently from scratch. Based on diagnostics that assess the ensemble's physical fidelity, our ensemble has comparable performance to operational weather forecasting systems. However, it requires orders-of-magnitude fewer computational resources. Here in Part 2, we generate a huge ensemble (HENS), with 7424 members initialized each day of summer 2023. We enumerate the technical requirements for running huge ensembles at this scale. HENS precisely samples the tails of the forecast distribution and presents a detailed sampling of internal variability. HENS has two primary applications: (1) as a large dataset with which to study the statistics and drivers of extreme weather and (2) as a weather forecasting system. For extreme climate statistics, HENS samples events 4σ away from the ensemble mean. At each grid cell, HENS increases the skill of the most accurate ensemble member and enhances coverage of possible future trajectories. As a weather forecasting model, HENS issues extreme weather forecasts with better uncertainty quantification. It also reduces the probability of outlier events, in which the verification value lies outside the ensemble forecast distribution.
| Item Type: | Article |
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| Additional Information: | Code and data availability: The code, datasets, and models used to produce the results used in this paper are archived on DataDryad under https://doi.org/10.5061/dryad.2rbnzs80n (Mahesh et al., 2025b). The code is integrated with Zenodo at the aforementioned DOI, and it is also available at https://github.com/ankurmahesh/earth2mip-fork (Mahesh et al., 2025c) as an additional download location. We include the code to train SFNO, conduct ensemble inference with bred vectors and multiple checkpoints, and scoring and analysis code. We also open-source the model weights of the trained SFNO. See the README of the DOI for information on how to use the code base and for the permission license associated with the code and data. The code is available via the Lawrence Berkeley Lab BSD variant license, and the data are available with a CC0 license. To run the ensemble for inference, a current version of the project is available from the project website at https://github.com/NVIDIA/earth2studio (NVIDIA, 2025) under the Apache-2.0 license. |
| Faculty \ School: | Faculty of Science > School of Environmental Sciences |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 21 Aug 2026 15:58 |
| Last Modified: | 23 Aug 2026 05:37 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/104296 |
| DOI: | 10.5194/gmd-18-5605-2025 |
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