Articles | Volume 16, issue 8
https://doi.org/10.5194/gmd-16-2181-2023
https://doi.org/10.5194/gmd-16-2181-2023
Development and technical paper
 | 
21 Apr 2023
Development and technical paper |  | 21 Apr 2023

Improving trajectory calculations by FLEXPART 10.4+ using single-image super-resolution

Rüdiger Brecht, Lucie Bakels, Alex Bihlo, and Andreas Stohl

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Cited articles

Bihlo, A.: A generative adversarial network approach to (ensemble) weather prediction, Neural Netw., 139, 1–16, 2021. a
Bihlo, A. and Popovych, R. O.: Physics-informed neural networks for the shallow-water equations on the sphere, J. Comput. Phys., 456, 111024, https://doi.org/10.1016/j.jcp.2022.111024, 2022. a
Brecht, R. and Bihlo, A.: Computing the ensemble spread from deterministic weather predictions using conditional generative adversarial networks, arXiv, arXiv:2205.09182, 2022. a
Brecht, R., Bakels, L., Bihlo, A., and Stohl, A.: Improving trajectory calculations using SISR, Zenodo [code], https://doi.org/10.5281/zenodo.7350568, 2022. a, b
Chen, H., He, X., Qing, L., Wu, Y., Ren, C., Sheriff, R. E., and Zhu, C.: Real-world single image super-resolution: A brief review, Inf. Fusion, 79, 124–145, 2022. a
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Short summary
We use neural-network-based single-image super-resolution to improve the upscaling of meteorological wind fields to be used for particle dispersion models. This deep-learning-based methodology improves the standard linear interpolation typically used in particle dispersion models. The improvement of wind fields leads to substantial improvement in the computed trajectories of the particles.
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