Huge Ensembles (HENS) of Weather Extremes using the Fourier Forecasting Neural Network (FourCastNet)

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Studying low-likelihood high-impact extreme weather and climate events in a warming world requires massive ensembles to capture long tails of multi-variate distributions. In combination, it is simply impossible to generate massive ensembles, of say 1000 members, using traditional numerical simulations of climate models at high resolution.

We* describe how to bring the power of machine learning (ML) to replace traditional numerical simulations for short week-long hindcasts of massive ensembles, where ML has proven to be successful in terms of accuracy and fidelity, at five orders-of-magnitude lower computational cost than numerical methods. Because the ensembles are reproducible to machine precision, ML also provides a data compression mechanism to avoid storing the data produced from massive ensembles.

The machine learning algorithm FourCastNet is based on Fourier Neural Operators (FNO) and Transformers, proven to be efficient and powerful in modeling a wide range of chaotic dynamical systems, including turbulent flows and atmospheric dynamics. FourCastNet has already been proven to be highly scalable on NVIDIA-GPU HPC (High Performance Computing) systems.

Until today, generating 1,000- or 10,000-member ensembles of hindcasts was simply impossible because of prohibitive compute and data storage costs. For the first time, we can now generate such massive ensembles using ML at five orders-of-magnitude less computational intensity than traditional numerical simulations.

*Authors: William D. Collins, Lawrence Berkeley National Laboratory, U.S.;
Karthik Kashinath, NVIDIA; Mike Pritchard, Nvidia Corporation, U.S.; Jaideep Pathak, Lawrence Berkeley National Laboratory, U.S.; Thorsten Kurth, NVIDIA, U.S.; Anima Anandkumar, California Institute of Technology & NVIDIA, U.S.; Travis O’Brien, Indiana University

This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.

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Start date
5 April 2023 at 17:00 CEST Geneva | 08:00-09:30 PDT, San Francisco | 23:00-00:30 CST, Beijing
End date
5 April 2023 at 18:30 CEST Geneva | 08:00-09:30 PDT, San Francisco | 23:00-00:30 CST, Beijing
Duration
90 minutes (including 30 minutes networking)
Programme stream
- Discovery - AI and Climate Science
Topics
- Environment & Climate change
UN SDGs
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Huge Ensembles (HENS) of Weather Extremes using the Fourier Forecasting Neural Network (FourCastNet)