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

Viewed

Total article views: 1,566 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,147 396 23 1,566 101 15 15
  • HTML: 1,147
  • PDF: 396
  • XML: 23
  • Total: 1,566
  • Supplement: 101
  • BibTeX: 15
  • EndNote: 15
Views and downloads (calculated since 19 Apr 2021)
Cumulative views and downloads (calculated since 19 Apr 2021)

Viewed (geographical distribution)

Total article views: 1,584 (including HTML, PDF, and XML) Thereof 1,584 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Oct 2022
Download
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.
Altmetrics
Final-revised paper
Preprint