Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 8
Articles | Volume 16, issue 8
https://doi.org/10.5194/gmd-16-2181-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-2181-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 8
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

Viewed

Total article views: 3,412 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,703 589 120 3,412 98 157
  • HTML: 2,703
  • PDF: 589
  • XML: 120
  • Total: 3,412
  • BibTeX: 98
  • EndNote: 157
Views and downloads (calculated since 11 Jul 2022)
Cumulative views and downloads (calculated since 11 Jul 2022)

Viewed (geographical distribution)

Total article views: 3,412 (including HTML, PDF, and XML) Thereof 3,325 with geography defined and 87 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 07 Mar 2026
Download
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.
Read more
Share