Preprints
https://doi.org/10.5194/egusphere-2022-441
https://doi.org/10.5194/egusphere-2022-441
11 Jul 2022
 | 11 Jul 2022

Improving trajectory calculations by FLEXPART 10.4+ using deep learning inspired single image superresolution

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

Abstract. Lagrangian trajectory or particle dispersion models as well as semi-Lagrangian advection schemes require meteorological data such as wind, temperature and geopotential at the exact spatio-temporal locations of the particles that move independently from a regular grid. Traditionally, this high-resolution data has been obtained by interpolating the meteorological parameters from the gridded data of a meteorological model or reanalysis, e.g. using linear interpolation in space and time. However, interpolation errors are a large source of error for these models. Reducing them requires meteorological input fields with high space and time resolution, which may not always be available and can cause severe data storage and transfer problems. Here, we interpret this problem as a single image superresolution task. That is, we interpret meteorological fields available at their native resolution as low-resolution images and train deep neural networks to up-scale them to higher resolution, thereby providing more accurate data for Lagrangian models. We train various versions of the state-of-the-art Enhanced Deep Residual Networks for Superresolution (EDSR) on low-resolution ERA5 reanalysis data with the goal to up-scale these data to arbitrary spatial resolution. We show that the resulting up-scaled wind fields have root-mean-squared errors half the size of the winds obtained with linear spatial interpolation at acceptable computational inference costs. In a test setup using the Lagrangian particle dispersion model FLEXPART and reduced-resolution wind fields, we demonstrate that absolute horizontal transport deviations of calculated trajectories from "ground-truth'' trajectories calculated with undegraded 0.5° x 0.5° winds are reduced by at least 49.5 % (21.8 %) after 48 hours relative to trajectories using linear interpolation of the wind data when training on 2° x 2° to 1° x 1° (4° x 4° to 2° x 2°) resolution data.

Journal article(s) based on this preprint

21 Apr 2023
Improving trajectory calculations by FLEXPART 10.4+ using single-image super-resolution
Rüdiger Brecht, Lucie Bakels, Alex Bihlo, and Andreas Stohl
Geosci. Model Dev., 16, 2181–2192, https://doi.org/10.5194/gmd-16-2181-2023,https://doi.org/10.5194/gmd-16-2181-2023, 2023
Short summary

Rüdiger Brecht et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2022-441', Juan Antonio Añel, 23 Aug 2022
    • AC1: 'Reply on CEC1', Rüdiger Brecht, 01 Sep 2022
    • AC2: 'Reply on CEC1', Rüdiger Brecht, 13 Sep 2022
      • CEC2: 'Reply on AC2', Juan Antonio Añel, 13 Sep 2022
        • AC3: 'Reply on CEC2', Rüdiger Brecht, 11 Oct 2022
          • CEC3: 'Reply on AC3', Juan Antonio Añel, 11 Oct 2022
            • CC1: 'Reply on CEC3', Andreas Stohl, 11 Oct 2022
              • CEC4: 'Reply on CC1', Juan Antonio Añel, 25 Oct 2022
                • AC4: 'Reply on CEC4', Rüdiger Brecht, 24 Nov 2022
                • AC5: 'Reply on CEC4', Rüdiger Brecht, 24 Nov 2022
                • AC6: 'Reply on CEC4', Rüdiger Brecht, 24 Nov 2022
  • RC1: 'Comment on egusphere-2022-441', Anonymous Referee #1, 12 Sep 2022
    • AC7: 'Reply on RC1', Rüdiger Brecht, 22 Dec 2022
  • RC2: 'Comment on egusphere-2022-441', Anonymous Referee #2, 14 Nov 2022
    • AC8: 'Reply on RC2', Rüdiger Brecht, 22 Dec 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2022-441', Juan Antonio Añel, 23 Aug 2022
    • AC1: 'Reply on CEC1', Rüdiger Brecht, 01 Sep 2022
    • AC2: 'Reply on CEC1', Rüdiger Brecht, 13 Sep 2022
      • CEC2: 'Reply on AC2', Juan Antonio Añel, 13 Sep 2022
        • AC3: 'Reply on CEC2', Rüdiger Brecht, 11 Oct 2022
          • CEC3: 'Reply on AC3', Juan Antonio Añel, 11 Oct 2022
            • CC1: 'Reply on CEC3', Andreas Stohl, 11 Oct 2022
              • CEC4: 'Reply on CC1', Juan Antonio Añel, 25 Oct 2022
                • AC4: 'Reply on CEC4', Rüdiger Brecht, 24 Nov 2022
                • AC5: 'Reply on CEC4', Rüdiger Brecht, 24 Nov 2022
                • AC6: 'Reply on CEC4', Rüdiger Brecht, 24 Nov 2022
  • RC1: 'Comment on egusphere-2022-441', Anonymous Referee #1, 12 Sep 2022
    • AC7: 'Reply on RC1', Rüdiger Brecht, 22 Dec 2022
  • RC2: 'Comment on egusphere-2022-441', Anonymous Referee #2, 14 Nov 2022
    • AC8: 'Reply on RC2', Rüdiger Brecht, 22 Dec 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Rüdiger Brecht on behalf of the Authors (22 Dec 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (04 Jan 2023) by Christopher Horvat
RR by Anonymous Referee #1 (03 Feb 2023)
ED: Reconsider after major revisions (08 Feb 2023) by Christopher Horvat
AR by Rüdiger Brecht on behalf of the Authors (01 Mar 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (13 Mar 2023) by Christopher Horvat
AR by Rüdiger Brecht on behalf of the Authors (14 Mar 2023)

Journal article(s) based on this preprint

21 Apr 2023
Improving trajectory calculations by FLEXPART 10.4+ using single-image super-resolution
Rüdiger Brecht, Lucie Bakels, Alex Bihlo, and Andreas Stohl
Geosci. Model Dev., 16, 2181–2192, https://doi.org/10.5194/gmd-16-2181-2023,https://doi.org/10.5194/gmd-16-2181-2023, 2023
Short summary

Rüdiger Brecht et al.

Rüdiger Brecht et al.

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Short summary
We use neural network based single image super resolution to improve 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 of the computed trajectories of the particles.