Articles | Volume 17, issue 21
https://doi.org/10.5194/amt-17-6485-2024
https://doi.org/10.5194/amt-17-6485-2024
Research article
 | 
13 Nov 2024
Research article |  | 13 Nov 2024

NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations

Leon Kuhn, Steffen Beirle, Sergey Osipov, Andrea Pozzer, and Thomas Wagner

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Review of "NitroNet – A deep-learning NO2 profile retrieval prototype for the TROPOMI satellite instrument"', Anonymous Referee #1, 18 Jun 2024
    • AC1: 'Reply on RC1', Leon Kuhn, 22 Jul 2024
  • RC2: 'Comment on egusphere-2024-1196', Anonymous Referee #2, 25 Jun 2024
    • AC2: 'Reply on RC2', Leon Kuhn, 22 Jul 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Leon Kuhn on behalf of the Authors (22 Jul 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (19 Aug 2024) by Robyn Schofield
RR by Robert Ryan (23 Aug 2024)
RR by Anonymous Referee #1 (05 Sep 2024)
ED: Publish as is (20 Sep 2024) by Robyn Schofield
AR by Leon Kuhn on behalf of the Authors (25 Sep 2024)  Manuscript 
Download
Short summary
This paper presents a new machine learning model that allows us to compute NO2 concentration profiles from satellite observations. A neural network was trained on synthetic data from the regional chemistry and transport model WRF-Chem. This is the first model of its kind. We present a thorough model validation study, covering various seasons and regions of the world.