Articles | Volume 14, issue 12
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, Ray Nassar, Christopher W. O'Dell, Rigel Kivi, Isamu Morino, Justus Notholt, Christof Petri, Kimberly Strong, and Debra Wunch


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-145', Anonymous Referee #1, 09 Aug 2021
    • AC1: 'Reply on RC1', Joseph Mendonca, 06 Oct 2021
  • RC2: 'Comment on amt-2021-145', François-Marie Bréon, 09 Sep 2021
    • AC2: 'Reply on RC2', Joseph Mendonca, 06 Oct 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Joseph Mendonca on behalf of the Authors (06 Oct 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (13 Oct 2021) by Joanna Joiner
RR by François-Marie Bréon (14 Oct 2021)
RR by Anonymous Referee #1 (18 Oct 2021)
ED: Publish as is (22 Oct 2021) by Joanna Joiner
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.