Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 4
Articles | Volume 15, issue 4
https://doi.org/10.5194/gmd-15-1583-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1583-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 4
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, Ran Huang, Xinlu Wang, Weiguo Wang, and Yongtao Hu

Viewed

Total article views: 2,532 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,956 498 78 2,532 141 29 33
  • HTML: 1,956
  • PDF: 498
  • XML: 78
  • Total: 2,532
  • Supplement: 141
  • BibTeX: 29
  • EndNote: 33
Views and downloads (calculated since 10 Aug 2021)
Cumulative views and downloads (calculated since 10 Aug 2021)

Viewed (geographical distribution)

Total article views: 2,532 (including HTML, PDF, and XML) Thereof 2,354 with geography defined and 178 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
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.
Read more