Articles | Volume 14, issue 12
Atmos. Meas. Tech., 14, 7511–7524, 2021
https://doi.org/10.5194/amt-14-7511-2021
Atmos. Meas. Tech., 14, 7511–7524, 2021
https://doi.org/10.5194/amt-14-7511-2021
Research article
03 Dec 2021
Research article | 03 Dec 2021

Assessing the feasibility of using a neural network to filter Orbiting Carbon Observatory 2 (OCO-2) retrievals at northern high latitudes

Joseph Mendonca et al.

Viewed

Total article views: 1,402 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,008 364 30 1,402 61 22 16
  • HTML: 1,008
  • PDF: 364
  • XML: 30
  • Total: 1,402
  • Supplement: 61
  • BibTeX: 22
  • EndNote: 16
Views and downloads (calculated since 19 Jul 2021)
Cumulative views and downloads (calculated since 19 Jul 2021)

Viewed (geographical distribution)

Total article views: 1,402 (including HTML, PDF, and XML) Thereof 1,365 with geography defined and 37 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Discussed (final revised paper)

Latest update: 01 Feb 2023
Download
Short summary
Machine learning has become an important tool for pattern recognition in many applications. In this study, we used a neural network to improve the data quality of OCO-2 measurements made at northern high latitudes. The neural network was trained and used as a binary classifier to filter out bad OCO-2 measurements in order to increase the accuracy and precision of OCO-2 XCO2 measurements in the Boreal and Arctic regions.