Articles | Volume 21, issue 17
Atmos. Chem. Phys., 21, 13149–13166, 2021
https://doi.org/10.5194/acp-21-13149-2021
Atmos. Chem. Phys., 21, 13149–13166, 2021
https://doi.org/10.5194/acp-21-13149-2021

Research article 06 Sep 2021

Research article | 06 Sep 2021

Forecasting and identifying the meteorological and hydrological conditions favoring the occurrence of severe hazes in Beijing and Shanghai using deep learning

Chien Wang

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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 acp-2021-196', Anonymous Referee #2, 17 May 2021
  • RC2: 'Comment on acp-2021-196', Anonymous Referee #1, 17 May 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Chien Wang on behalf of the Authors (15 Jun 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (03 Jul 2021) by Yun Qian
RR by Anonymous Referee #2 (21 Jul 2021)
RR by Anonymous Referee #1 (22 Jul 2021)
ED: Publish subject to minor revisions (review by editor) (02 Aug 2021) by Yun Qian
AR by Chien Wang on behalf of the Authors (02 Aug 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (16 Aug 2021) by Yun Qian
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Short summary
Haze caused by abundant atmospheric aerosols has become a serious environmental issue in many countries. An innovative deep-learning machine has been developed to forecast the occurrence of hazes in two Asian megacities (Beijing and Shanghai) and has achieved good overall accuracy. Using this machine, typical regional meteorological and hydrological regimes associated with haze and non-haze events in the two cities have also been, arguably for the first time, successfully categorized.
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