Articles | Volume 15, issue 4
Geosci. Model Dev., 15, 1583–1594, 2022
https://doi.org/10.5194/gmd-15-1583-2022
Geosci. Model Dev., 15, 1583–1594, 2022
https://doi.org/10.5194/gmd-15-1583-2022

Development and technical paper 22 Feb 2022

Development and technical paper | 22 Feb 2022

Deep-learning spatial principles from deterministic chemical transport models for chemical reanalysis: an application in China for PM2.5

Baolei Lyu et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-253', Anonymous Referee #1, 21 Aug 2021
    • AC1: 'Comment on gmd-2021-253', Baolei Lyu, 02 Oct 2021
  • RC2: 'Comment on gmd-2021-253', Anonymous Referee #2, 11 Sep 2021
  • AC1: 'Comment on gmd-2021-253', Baolei Lyu, 02 Oct 2021
    • RC3: 'Reply on AC1', Anonymous Referee #1, 02 Oct 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Baolei Lyu on behalf of the Authors (03 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (14 Jan 2022) by David Topping
AR by Baolei Lyu on behalf of the Authors (21 Jan 2022)  Author's response    Manuscript
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Short summary
Data fusion is used to estimate spatially completed and smooth reanalysis fields from multiple data sources of observations and model simulations. We developed a well-designed deep-learning model framework to embed spatial correlation principles of atmospheric physics and chemical models. The deep-learning model has very high accuracy to predict reanalysis data fields from isolated observation data points. It is also feasible for operational applications due to computational efficiency.