Articles | Volume 18, issue 15
https://doi.org/10.5194/amt-18-3747-2025
https://doi.org/10.5194/amt-18-3747-2025
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

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Cited articles

Ahmad, N., Lin, C., Lau, A. K. H., Kim, J., Zhang, T., Yu, F., Li, C., Li, Y., Fung, J. C. H., and Lao, X. Q.: Estimation of ground-level NO2 and its spatiotemporal variations in China using GEMS measurements and a nested machine learning model, Atmos. Chem. Phys., 24, 9645–9665, https://doi.org/10.5194/acp-24-9645-2024, 2024. a, b
Bechle, M. J., Millet, D. B., and Marshall, J. D.: Remote sensing of exposure to NO2: Satellite versus ground-based measurement in a large urban area, Atmos. Environ., 69, 345–353, https://doi.org/10.1016/j.atmosenv.2012.11.046, 2013. a
Beirle, S., Hörmann, C., Jöckel, P., Liu, S., Penning de Vries, M., Pozzer, A., Sihler, H., Valks, P., and Wagner, T.: The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO2 from nadir-viewing satellites by weighted convolution, Atmos. Meas. Tech., 9, 2753–2779, https://doi.org/10.5194/amt-9-2753-2016, 2016. a
Bovensmann, H., Burrows, J., Buchwitz, M., Frerick, J., Noel, S., Rozanov, V., Chance, Kelly, and Goede, A.: SCIAMACHY: mission objectives and measurement modes, J. Atmos. Sci., 56, 127–150 https://doi.org/10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2, 1999. a
Breiman, L.: Random Forests, Machine Learning, 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
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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.
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