Articles | Volume 15, issue 18
Research article
27 Sep 2022
Research article |  | 27 Sep 2022

A new machine-learning-based analysis for improving satellite-retrieved atmospheric composition data: OMI SO2 as an example

Can Li, Joanna Joiner, Fei Liu, Nickolay A. Krotkov, Vitali Fioletov, and Chris McLinden


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-135', Anonymous Referee #2, 03 Jun 2022
    • AC1: 'Reply on RC1', Can Li, 12 Jul 2022
  • RC2: 'Comment on amt-2022-135', Anonymous Referee #1, 13 Jun 2022
    • AC2: 'Reply on RC2', Can Li, 12 Jul 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Can Li on behalf of the Authors (27 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (21 Aug 2022) by Ilse Aben
RR by Anonymous Referee #1 (01 Sep 2022)
ED: Publish as is (04 Sep 2022) by Ilse Aben
Short summary
Satellite observations provide information on the sources of SO2, an important pollutant that affects both air quality and climate. However, these observations suffer from relatively poor data quality due to weak signals of SO2. Here, we use a machine learning technique to analyze satellite SO2 observations in order to reduce the noise and artifacts over relatively clean areas while keeping the signals near pollution sources. This leads to significant improvement in satellite SO2 data.