Articles | Volume 10, issue 3
Research article
08 Mar 2017
Research article |  | 08 Mar 2017

An exploratory study on the aerosol height retrieval from OMI measurements of the 477  nm O2 − O2 spectral band using a neural network approach

Julien Chimot, J. Pepijn Veefkind, Tim Vlemmix, Johan F. de Haan, Vassilis Amiridis, Emmanouil Proestakis, Eleni Marinou, and Pieternel F. Levelt

Related authors

Monitoring multiple satellite aerosol optical depth (AOD) products within the Copernicus Atmosphere Monitoring Service (CAMS) data assimilation system
Sebastien Garrigues, Samuel Remy​​​​​​​, Julien Chimot, Melanie Ades, Antje Inness, Johannes Flemming, Zak Kipling, Istvan Laszlo, Angela Benedetti, Roberto Ribas, Soheila Jafariserajehlou, Bertrand Fougnie, Shobha Kondragunta, Richard Engelen, Vincent-Henri Peuch, Mark Parrington, Nicolas Bousserez, Margarita Vazquez Navarro, and Anna Agusti-Panareda
Atmos. Chem. Phys., 22, 14657–14692,,, 2022
Short summary
Defining aerosol layer height for UVAI interpretation using aerosol vertical distributions characterized by MERRA-2
Jiyunting Sun, J. Pepijn Veefkind, Peter van Velthoven, L. Gijsbert Tilstra, Julien Chimot, Swadhin Nanda, and Pieternel F. Levelt
Atmos. Chem. Phys. Discuss.,,, 2020
Revised manuscript not accepted
Short summary
Minimizing aerosol effects on the OMI tropospheric NO2 retrieval – An improved use of the 477 nm O2 − O2 band and an estimation of the aerosol correction uncertainty
Julien Chimot, J. Pepijn Veefkind, Johan F. de Haan, Piet Stammes, and Pieternel F. Levelt
Atmos. Meas. Tech., 12, 491–516,,, 2019
Short summary
Improved aerosol correction for OMI tropospheric NO2 retrieval over East Asia: constraint from CALIOP aerosol vertical profile
Mengyao Liu, Jintai Lin, K. Folkert Boersma, Gaia Pinardi, Yang Wang, Julien Chimot, Thomas Wagner, Pinhua Xie, Henk Eskes, Michel Van Roozendael, François Hendrick, Pucai Wang, Ting Wang, Yingying Yan, Lulu Chen, and Ruijing Ni
Atmos. Meas. Tech., 12, 1–21,,, 2019
Short summary
Spatial distribution analysis of the OMI aerosol layer height: a pixel-by-pixel comparison to CALIOP observations
Julien Chimot, J. Pepijn Veefkind, Tim Vlemmix, and Pieternel F. Levelt
Atmos. Meas. Tech., 11, 2257–2277,,, 2018
Short summary

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
The CALIPSO version 4.5 stratospheric aerosol subtyping algorithm
Jason L. Tackett, Jayanta Kar, Mark A. Vaughan, Brian J. Getzewich, Man-Hae Kim, Jean-Paul Vernier, Ali H. Omar, Brian E. Magill, Michael C. Pitts, and David M. Winker
Atmos. Meas. Tech., 16, 745–768,,, 2023
Short summary
Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case
Ilaria Petracca, Davide De Santis, Matteo Picchiani, Stefano Corradini, Lorenzo Guerrieri, Fred Prata, Luca Merucci, Dario Stelitano, Fabio Del Frate, Giorgia Salvucci, and Giovanni Schiavon
Atmos. Meas. Tech., 15, 7195–7210,,, 2022
Short summary
The impact and estimation of uncertainty correlation for multi-angle polarimetric remote sensing of aerosols and ocean color
Meng Gao, Kirk Knobelspiesse, Bryan A. Franz, Peng-Wang Zhai, Brian Cairns, Xiaoguang Xu, and J. Vanderlei Martins
EGUsphere,,, 2022
Short summary
Ground-based remote sensing of aerosol properties using high resolution infrared emission and Lidar observations in the high Arctic
Denghui Ji, Mathias Palm, Christoph Ritter, Philipp Richter, Xiaoyu Sun, Matthias Buschmann, and Justus Notholt
Atmos. Meas. Tech. Discuss.,,, 2022
Revised manuscript accepted for AMT
Short summary
The new MISR research aerosol retrieval algorithm: a multi-angle, multi-spectral, bounded-variable least squares retrieval of aerosol particle properties over both land and water
James A. Limbacher, Ralph A. Kahn, and Jaehwa Lee
Atmos. Meas. Tech., 15, 6865–6887,,, 2022
Short summary

Cited articles

Acarreta, J. R., de Haan, J. F., and Stammes, P.: Cloud pressure retrieval using the O2 − O2 absorption band at 477 nm, J. Geophys. Res.-Atmos., 109, D05204,, 2004.
Ahn, C., Torres, O., and Jethva, H.: Assessment of OMI near-UV aerosol optical depth over land, J. Geophys. Res.-Atmos., 119, 2457–2473, 2013JD020188,, 2014.
Atkinson, P. M. and Tatnall, A. R. L.: Introduction Neural networks in remote sensing, Int. J. Remote Sens., 18, 699–709,, 1997.
Barkley, M. P., Kurosu, T. P., Chance, K., De Smedt, I., Van Roozendael, M., Arneth, A., Hagberg, D., and Guenther, A.: Assessing sources of uncertainty in formaldehyde air mass factors over tropical South America: Implications for top-down isoprene emission estimates, J. Geophys. Res.-Atmos., 117, D13304,, 2012.
Short summary
We have developed artificial neural network algorithms to retrieve aerosol layer height from satellite OMI observations of the 477 nm O2–O2 spectral band. Based on 3-year (2005–2007) cloud-free scenes over north-east Asia, the results show uncertainties of 260–800 m when aerosol optical thickness is larger than 1. These algorithms also enable aerosol optical thickness retrievals by exploring the OMI continuum reflectance. These results may be used for future trace gas retrievals from TROPOMI.