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

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

doi:https://doi.org/10.5194/amt-18-3747-2025

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

Data sets

ERA5 hourly data on single levels from 1940 to present Hans Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

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.