Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 17
Articles | Volume 16, issue 17
https://doi.org/10.5194/gmd-16-5251-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-16-5251-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 17
Model description paper
 | 
13 Sep 2023
Model description paper |  | 13 Sep 2023

Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0

Junsu Gil, Meehye Lee, Jeonghwan Kim, Gangwoong Lee, Joonyoung Ahn, and Cheol-Hee Kim

Viewed

Total article views: 2,327 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,843 399 85 2,327 161 79 77
  • HTML: 1,843
  • PDF: 399
  • XML: 85
  • Total: 2,327
  • Supplement: 161
  • BibTeX: 79
  • EndNote: 77
Views and downloads (calculated since 10 Dec 2021)
Cumulative views and downloads (calculated since 10 Dec 2021)

Viewed (geographical distribution)

Total article views: 2,327 (including HTML, PDF, and XML) Thereof 2,186 with geography defined and 141 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 06 May 2025
Download
Short summary
In this study, the framework for calculating reactive nitrogen species using a deep neural network (RND) was developed. It works through simple Python codes and provides high-accuracy reactive nitrogen oxide data. In the first version (RNDv1.0), the model calculates the nitrous acid (HONO) in urban areas, which has an important role in producing O3 and fine aerosol.
Read more
Share
Special issue
Benchmark datasets and machine learning algorithms for Earth...