Machine Learning to Construct Daily, Gap-Free, Long-Term Stratospheric Trace Gases Data Sets

Sandip Dhomse; Martyn Chipperfield

doi:https://doi.org/10.5194/egusphere-egu25-2394

[Back] [Session ITS1.1/CL0.9]

EGU25-2394, updated on 14 Mar 2025

https://doi.org/10.5194/egusphere-egu25-2394

EGU General Assembly 2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Machine Learning to Construct Daily, Gap-Free, Long-Term Stratospheric Trace Gases Data Sets

Sandip Dhomse

^1,2 and Martyn Chipperfield

^1,2

Sandip Dhomse and Martyn Chipperfield

¹University of Leeds, School of Earth and Environmental Sciences, United Kingdom of Great Britain – England, Scotland, Wales (s.s.dhomse@leeds.ac.uk)
²University of Leeds, National Centre of Earth Observation, Leeds, United Kingdom of Great Britain – England, Scotland, Wales

How to cite: Dhomse, S. and Chipperfield, M.: Machine Learning to Construct Daily, Gap-Free, Long-Term Stratospheric Trace Gases Data Sets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2394, https://doi.org/10.5194/egusphere-egu25-2394, 2025.

This abstract has been withdrawn on 25 Jul 2025.