Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 15
Articles | Volume 18, issue 15
https://doi.org/10.5194/amt-18-3747-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-3747-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 15
Research article
 | 
11 Aug 2025
Research article |  | 11 Aug 2025

Hourly surface nitrogen dioxide retrieval from GEMS tropospheric vertical column densities: benefit of using time-contiguous input features for machine learning models

Janek Gödeke, Andreas Richter, Kezia Lange, Peter Maaß, Hyunkee Hong, Hanlim Lee, and Junsung Park

Viewed

Total article views: 652 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
342 122 188 652 22 37
  • HTML: 342
  • PDF: 122
  • XML: 188
  • Total: 652
  • BibTeX: 22
  • EndNote: 37
Views and downloads (calculated since 04 Nov 2024)
Cumulative views and downloads (calculated since 04 Nov 2024)

Viewed (geographical distribution)

Total article views: 652 (including HTML, PDF, and XML) Thereof 652 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 11 Aug 2025
Download
Short summary
The Korean Geostationary Environmental Monitoring Spectrometer (GEMS) monitors trace gases over Asia, e.g., NO2. GEMS provides hourly data, improving the time resolution compared to the daily overpasses by other satellites. For the prediction of hourly surface NO2 over South Korea from GEMS observations and meteorological data, this study shows that machine learning models benefit from this higher time resolution. This is achieved by using observations from previous hours as additional inputs.
Read more
Share