Articles | Volume 15, issue 20
Geosci. Model Dev., 15, 7533–7556, 2022
https://doi.org/10.5194/gmd-15-7533-2022
Geosci. Model Dev., 15, 7533–7556, 2022
https://doi.org/10.5194/gmd-15-7533-2022
Methods for assessment of models
17 Oct 2022
Methods for assessment of models | 17 Oct 2022

Recovery of sparse urban greenhouse gas emissions

Benjamin Zanger et al.

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-438', Anonymous Referee #1, 04 Mar 2022
  • RC2: 'Comment on gmd-2021-438', Anonymous Referee #2, 09 Mar 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Benjamin Zanger on behalf of the Authors (26 Jun 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (29 Jun 2022) by Jinkyu Hong
RR by Anonymous Referee #1 (05 Jul 2022)
RR by Anonymous Referee #3 (26 Jul 2022)
ED: Publish subject to minor revisions (review by editor) (05 Aug 2022) by Jinkyu Hong
AR by Benjamin Zanger on behalf of the Authors (26 Aug 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (31 Aug 2022) by Jinkyu Hong
Download
Short summary
Gaussian priors (GPs) used in least squares inversion do not reflect the true distributions of greenhouse gas emissions well. A method that does not rely on GPs is sparse reconstruction (SR). We show that necessary conditions for SR are satisfied for cities and that the application of a wavelet transform can further enhance sparsity. We apply the theory of compressed sensing to SR. Our results show that SR needs fewer measurements and is superior for assessing unknown emitters compared to GPs.