Articles | Volume 19, issue 2
https://doi.org/10.5194/gmd-19-755-2026
https://doi.org/10.5194/gmd-19-755-2026
Development and technical paper
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23 Jan 2026
Development and technical paper | Highlight paper |  | 23 Jan 2026

Operational numerical weather prediction with ICON on GPUs (version 2024.10)

Xavier Lapillonne, Daniel Hupp, Fabian Gessler, André Walser, Andreas Pauling, Annika Lauber, Benjamin Cumming, Carlos Osuna, Christoph Müller, Claire Merker, Daniel Leuenberger, David Leutwyler, Dmitry Alexeev, Gabriel Vollenweider, Guillaume Van Parys, Jonas Jucker, Lukas Jansing, Marco Arpagaus, Marco Induni, Marek Jacob, Matthias Kraushaar, Michael Jähn, Mikael Stellio, Oliver Fuhrer, Petra Baumann, Philippe Steiner, Pirmin Kaufmann, Remo Dietlicher, Ralf Müller, Sergey Kosukhin, Thomas C. Schulthess, Ulrich Schättler, Victoria Cherkas, and William Sawyer

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Cited articles

Adamidis, P., Pfister, E., Bockelmann, H., Zobel, D., Beismann, J.-O., and Jacob, M.: The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6), Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, 2025. a, b
Adams, S., Ford, R., Hambley, M., Hobson, J., Kavčič, I., Maynard, C., Melvin, T., Müller, E., Mullerworth, S., Porter, A., Rezny, M., Shipway, B., and Wong, R.: LFRic: Meeting the challenges of scalability and performance portability in Weather and Climate models, Journal of Parallel and Distributed Computing, 132, 383–396, https://doi.org/10.1016/j.jpdc.2019.02.007, 2019. a
Alps: Alps CSCS, https://www.cscs.ch/computers/alps (last access: 1 October 2025), 2025. a
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, 2015. a
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Executive editor
Increasingly, new supercomputers depend on GPUs for the vast bulk of their processing power. This makes the effective exploitation of GPUs an imperative across geoscientific modelling. This paper presents the port of a full numerical weather prediction system to GPU. It provides an excellent example of how such a port can be achieved in practice while delivering significant performance benefits. As such, this work offers particularly valuable guidance for the wider modelling community.
Short summary
The ICON climate and numerical weather prediction model was fully ported to Graphical Processing Units (GPUs) using OpenACC compiler directives, covering all components required for operational weather prediction. The GPU port together with several performance optimizations led to a speed-up of 5.6× when comparing to traditional Central Processing Units (CPUs) . Thanks to this adaptation effort, MeteoSwiss became the first national weather service to run the ICON model operationally on GPUs.
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