Articles | Volume 18, issue 2
https://doi.org/10.5194/gmd-18-529-2025
https://doi.org/10.5194/gmd-18-529-2025
Development and technical paper
 | 
30 Jan 2025
Development and technical paper |  | 30 Jan 2025

Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes

Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli

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Cited articles

Adams, S. V., Ford, R. W., Hambley, M., Hobson, J., Kavčič, I., Maynard, C. M., Melvin, T., Müller, E. H., Mullerworth, S., Porter, A. R., Rezny, M., Shipway, B. J., and Wong, R.: LFRic: Meeting the challenges of scalability and performance portability in Weather and Climate models, J. Parallel Distr. Com., 132, 383–396, https://doi.org/10.1016/j.jpdc.2019.02.007, 2019. a
Afanasyev, A., Bianco, M., Mosimann, L., Osuna, C., Thaler, F., Vogt, H., Fuhrer, O., VandeVondele, J., and Schulthess, T. C.: GridTools: A framework for portable weather and climate applications, SoftwareX, 15, 100707, https://doi.org/10.1016/j.softx.2021.100707, 2021. a, b
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., Quintino, T., Wedi, N. P., Bonanni, A., Chrust, M., Deconinck, W., Diamantakis, M., Dueben, P. D., English, S., Flemming, J., Gillies, P., Hadade, I., Hawkes, J., Hawkins, M., Iffrig, O., Kühnlein, C., Lange, M., Lean, P., Maciel, P., Marsden, O., Müller, A., Saarinen, S., Sarmany, D., Sleigh, M., Smart, S., Smolarkiewicz, P. K., Thiemert, D., Tumolo, G., Weihrauch, C., and Zanna, C.: The ECMWF scalability programme: Progress and plans, ECMWF Technical Memo No. 857, https://doi.org/10.21957/gdit22ulm, 2020. a, b
Bauer, P., Dueben, P. D., Hoefler, T., Quintino, T., Schulthess, T. C., and Wedi, N. P.: The digital revolution of Earth-system science, Nature Comput. Sci., 1, 104–113, https://doi.org/10.1038/s43588-021-00023-0, 2021. a
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Short summary
We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
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