Articles | Volume 14, issue 4
https://doi.org/10.5194/amt-14-2981-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-2981-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Apr 2021
Research article |
| 22 Apr 2021
| 22 Apr 2021
Model-enforced post-process correction of satellite aerosol retrievals
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Ville Kolehmainen
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Pekka Kolmonen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Antti Kukkurainen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Tero Mielonen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Neus Sabater
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Larisa Sogacheva
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Timo H. Virtanen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Antti Arola
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
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Cited
12 citations as recorded by crossref.
- Trends and classification of aerosol observed from MODIS sensor over Northern Europe and the Arctic K. Han et al. https://doi.org/10.1016/j.apr.2024.102329
- High‐Resolution Post‐Process Corrected Satellite AOD H. Taskinen et al. https://doi.org/10.1029/2022GL099733
- Post-process correction improves the accuracy of satellite PM2.5 retrievals A. Porcheddu et al. https://doi.org/10.5194/amt-17-5747-2024
- Characterization of Historical Aerosol Optical Depth Dynamics Using LSTM and Peak Enhancement Techniques H. Cămărășan et al. https://doi.org/10.3390/atmos16060743
- First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia Y. Cho et al. https://doi.org/10.5194/amt-17-4369-2024
- Improving Fengyun-3D satellite atmospheric precipitable water vapor products through machine learning-based post-processing correction M. Li et al. https://doi.org/10.1016/j.atmosres.2025.108133
- Aerosol Fine-Mode-Fraction Retrieval From GEO-KOMPSAT-2A/AMI Using a Deep Neural Network and Spectral Deconvolution Algorithm M. Kim et al. https://doi.org/10.1109/TGRS.2025.3591177
- Machine Learning-Based Calibration Study of MERSI-II Atmospheric Precipitable Water Vapor Product M. Li https://doi.org/10.54097/xnwft166
- A Bayesian Framework to Quantify Uncertainty in Aerosol Optical Model Selection Applied to TROPOMI Measurements A. Kauppi et al. https://doi.org/10.3390/rs16111945
- 基于机器学习的风云三号中分辨率光谱成像仪-II陆地气溶胶产品偏差校正方法（特邀） 李. Li Mengnan et al. https://doi.org/10.3788/AOS252008
- Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product A. Lipponen et al. https://doi.org/10.5194/amt-15-895-2022
- Bias correction of MODIS retrieved Aerosol Optical Depth using Machine Learning and Deep Learning Techniques M. Anitha & L. Kumar https://doi.org/10.1080/01431161.2025.2571235
12 citations as recorded by crossref.
- Trends and classification of aerosol observed from MODIS sensor over Northern Europe and the Arctic K. Han et al. https://doi.org/10.1016/j.apr.2024.102329
- High‐Resolution Post‐Process Corrected Satellite AOD H. Taskinen et al. https://doi.org/10.1029/2022GL099733
- Post-process correction improves the accuracy of satellite PM2.5 retrievals A. Porcheddu et al. https://doi.org/10.5194/amt-17-5747-2024
- Characterization of Historical Aerosol Optical Depth Dynamics Using LSTM and Peak Enhancement Techniques H. Cămărășan et al. https://doi.org/10.3390/atmos16060743
- First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia Y. Cho et al. https://doi.org/10.5194/amt-17-4369-2024
- Improving Fengyun-3D satellite atmospheric precipitable water vapor products through machine learning-based post-processing correction M. Li et al. https://doi.org/10.1016/j.atmosres.2025.108133
- Aerosol Fine-Mode-Fraction Retrieval From GEO-KOMPSAT-2A/AMI Using a Deep Neural Network and Spectral Deconvolution Algorithm M. Kim et al. https://doi.org/10.1109/TGRS.2025.3591177
- Machine Learning-Based Calibration Study of MERSI-II Atmospheric Precipitable Water Vapor Product M. Li https://doi.org/10.54097/xnwft166
- A Bayesian Framework to Quantify Uncertainty in Aerosol Optical Model Selection Applied to TROPOMI Measurements A. Kauppi et al. https://doi.org/10.3390/rs16111945
- 基于机器学习的风云三号中分辨率光谱成像仪-II陆地气溶胶产品偏差校正方法（特邀） 李. Li Mengnan et al. https://doi.org/10.3788/AOS252008
- Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product A. Lipponen et al. https://doi.org/10.5194/amt-15-895-2022
- Bias correction of MODIS retrieved Aerosol Optical Depth using Machine Learning and Deep Learning Techniques M. Anitha & L. Kumar https://doi.org/10.1080/01431161.2025.2571235
Saved (final revised paper)
Latest update: 14 Nov 2026
Short summary
We have developed a new computational method to post-process-correct the satellite aerosol retrievals. The proposed method combines the conventional satellite aerosol retrievals relying on physics-based models and machine learning. The results show significantly improved accuracy in the aerosol data over the operational satellite data products. The correction can be applied to the existing satellite aerosol datasets with no need to fully reprocess the much larger original radiance data.
We have developed a new computational method to post-process-correct the satellite aerosol...
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