Articles | Volume 19, issue 5
https://doi.org/10.5194/gmd-19-1867-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-1867-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
04 Mar 2026
Methods for assessment of models |
| 04 Mar 2026
| 04 Mar 2026
Data-driven discovery and model reduction methods for the atmospheric effects of high altitude emissions
Jurriaan A. van 't Hoff
Operations & Environment, Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, the Netherlands
Tom S. van Cranenburgh
Operations & Environment, Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, the Netherlands
Urban Fasel
Department of Aeronautics, Faculty of Engineering, Imperial College London, Exhibition Road, SW7 2AZ, London, UK
Operations & Environment, Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, the Netherlands
Department of Engineering, University of Cambridge, 1 JJ Thomson Avenue, Cambridge, CB3 0DY, Cambridge, UK
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Short summary
Chemistry transport models (CTMs) are critical in environmental assessments, but their computational cost often limits direct use in decision-making. We evaluate data-driven model discovery and reduction methods as reduced-order models for CTM simulations, showing they can reconstruct and forecast changes in global ozone distribution from supersonic aircraft emissions for several years at a fraction of the CTM cost while also being more accessible.
Chemistry transport models (CTMs) are critical in environmental assessments, but their...
