Articles | Volume 18, issue 6
https://doi.org/10.5194/gmd-18-2051-2025
https://doi.org/10.5194/gmd-18-2051-2025
Development and technical paper
 | 
01 Apr 2025
Development and technical paper |  | 01 Apr 2025

Can AI be enabled to perform dynamical downscaling? A latent diffusion model to mimic kilometer-scale COSMO5.0_CLM9 simulations

Elena Tomasi, Gabriele Franch, and Marco Cristoforetti

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Cited articles

Abdalla, S., Isaksen, L., Janssen, P., and Wedi, N.: Effective spectral resolution of ECMWF atmospheric forecast models, ECMWF Newsletter, 137, 19–22, https://doi.org/10.21957/rue4o7ac, 2013. a
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Adinolfi, M., Raffa, M., Reder, A., and Mercogliano, P.: Investigation on potential and limitations of ERA5 Reanalysis downscaled on Italy by a convection-permitting model, Clim. Dynam., 61, 4319–4342, https://doi.org/10.1007/s00382-023-06803-w, 2023. a, b
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High-resolution weather data are crucial for many applications, typically generated via resource-intensive numerical models through dynamical downscaling. We developed an AI model using latent diffusion models (LDMs) to mimic this process, increasing weather data resolution over Italy from 25 to 2 km. LDM outperforms other methods, accurately capturing local patterns and extreme events. This approach offers a cost-effective alternative, with potential disruptive application in climate sciences.
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