Data-driven discovery and model reduction methods  for the atmospheric effects of high altitude emissions

van 't Hoff, Jurriaan A.; van Cranenburgh, Tom S.; Fasel, Urban; Dedoussi, Irene C.

doi:10.5194/gmd-19-1867-2026

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.

https://doi.org/10.5194/gmd-19-1867-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 5

Methods for assessment of models

|

04 Mar 2026

Methods for assessment of models |

| 04 Mar 2026

Data-driven discovery and model reduction methods for the atmospheric effects of high altitude emissions

Jurriaan A. van 't Hoff, Tom S. van Cranenburgh, Urban Fasel, and Irene C. Dedoussi

Data sets

Supporting data and code for "Data-driven discovery and model reduction methods for the atmospheric effects of high altitude emissions" Jurriaan van 't Hoff et al. https://doi.org/10.4121/d7c8091b-fc2b-4c21-a498-d4a01c9a7a40

Supporting Dataset for "Sensitivities of Atmospheric Ozone and Radiative Forcing to Supersonic Aircraft Emissions across Two Flight Corridors'' Jurriaan van 't Hoff et al. https://doi.org/10.4121/D5947A0D-F34D-400B-87DE-46EBDA16EC44.V1

Supporting Dataset for "Multi-model Assessment of the Atmospheric and Radiative Effects of Supersonic Transport Aircraft'' Jurriaan van 't Hoff et al. https://doi.org/10.4121/DD38833D-6C5D-47D8-BB10-7535CE1EECF1.V1

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.