Articles | Volume 15, issue 22
Geosci. Model Dev., 15, 8439–8452, 2022
https://doi.org/10.5194/gmd-15-8439-2022
Geosci. Model Dev., 15, 8439–8452, 2022
https://doi.org/10.5194/gmd-15-8439-2022
Model description paper
21 Nov 2022
Model description paper | 21 Nov 2022

Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement

Haochen Sun 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-2022-164', Anonymous Referee #1, 08 Aug 2022
    • AC3: 'Reply on RC1', Xingcheng Lu, 27 Sep 2022
  • RC2: 'Comment on gmd-2022-164', Anonymous Referee #2, 20 Aug 2022
    • AC4: 'Reply on RC2', Xingcheng Lu, 27 Sep 2022
  • CEC1: 'Comment on gmd-2022-164', Juan Antonio Añel, 23 Aug 2022
    • AC1: 'Reply on CEC1', Xingcheng Lu, 08 Sep 2022
  • CC1: 'Comment on gmd-2022-164', Anthony Fishwick, 14 Sep 2022
    • AC1: 'Reply on CEC1', Xingcheng Lu, 08 Sep 2022
    • AC2: 'Reply on CC1', Xingcheng Lu, 27 Sep 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Xingcheng Lu on behalf of the Authors (10 Oct 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (21 Oct 2022) by David Topping
AR by Xingcheng Lu on behalf of the Authors (31 Oct 2022)  Author's response    Manuscript
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Short summary
This study developed a novel deep-learning layer, the broadcasting layer, to build an end-to-end LSTM-based deep-learning model for regional air pollution forecast. By combining the ground observation, WRF-CMAQ simulation, and the broadcasting LSTM deep-learning model, forecast accuracy has been significantly improved when compared to other methods. The broadcasting layer and its variants can also be applied in other research areas to supersede the traditional numerical interpolation methods.