Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 15, issue 11
Articles | Volume 15, issue 11
https://doi.org/10.5194/essd-15-5105-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-5105-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 11
Data description paper
 | 
24 Nov 2023
Data description paper |  | 24 Nov 2023

Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets

Sandip S. Dhomse and Martyn P. Chipperfield

Viewed

Total article views: 1,759 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,385 305 69 1,759 134 53 71
  • HTML: 1,385
  • PDF: 305
  • XML: 69
  • Total: 1,759
  • Supplement: 134
  • BibTeX: 53
  • EndNote: 71
Views and downloads (calculated since 29 Mar 2023)
Cumulative views and downloads (calculated since 29 Mar 2023)

Viewed (geographical distribution)

Total article views: 1,759 (including HTML, PDF, and XML) Thereof 1,698 with geography defined and 61 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Nov 2024
Download
Short summary
There are no long-term stratospheric profile data sets for two very important greenhouse gases: methane (CH4) and nitrous oxide (N2O). Along with radiative feedback, these species play an important role in controlling ozone loss in the stratosphere. Here, we use machine learning to fuse satellite measurements with a chemical model to construct long-term gap-free profile data sets for CH4 and N2O. We aim to construct similar data sets for other important trace gases (e.g. O3, Cly, NOy species).
Read more
Altmetrics
Final-revised paper
Preprint