Articles | Volume 10, issue 3
Atmos. Meas. Tech., 10, 783–809, 2017
https://doi.org/10.5194/amt-10-783-2017
Atmos. Meas. Tech., 10, 783–809, 2017
https://doi.org/10.5194/amt-10-783-2017

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 et al.

Related authors

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., https://doi.org/10.5194/acp-2020-39,https://doi.org/10.5194/acp-2020-39, 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, https://doi.org/10.5194/amt-12-491-2019,https://doi.org/10.5194/amt-12-491-2019, 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, https://doi.org/10.5194/amt-12-1-2019,https://doi.org/10.5194/amt-12-1-2019, 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, https://doi.org/10.5194/amt-11-2257-2018,https://doi.org/10.5194/amt-11-2257-2018, 2018
Short summary
Nine-year spatial and temporal evolution of desert dust aerosols over South and East Asia as revealed by CALIOP
Emmanouil Proestakis, Vassilis Amiridis, Eleni Marinou, Aristeidis K. Georgoulias, Stavros Solomos, Stelios Kazadzis, Julien Chimot, Huizheng Che, Georgia Alexandri, Ioannis Binietoglou, Vasiliki Daskalopoulou, Konstantinos A. Kourtidis, Gerrit de Leeuw, and Ronald J. van der A
Atmos. Chem. Phys., 18, 1337–1362, https://doi.org/10.5194/acp-18-1337-2018,https://doi.org/10.5194/acp-18-1337-2018, 2018
Short summary

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Retrievals of dust-related particle mass and ice-nucleating particle concentration profiles with ground-based polarization lidar and sun photometer over a megacity in central China
Yun He, Yunfei Zhang, Fuchao Liu, Zhenping Yin, Yang Yi, Yifan Zhan, and Fan Yi
Atmos. Meas. Tech., 14, 5939–5954, https://doi.org/10.5194/amt-14-5939-2021,https://doi.org/10.5194/amt-14-5939-2021, 2021
Short summary
Introducing the MISR level 2 near real-time aerosol product
Marcin L. Witek, Michael J. Garay, David J. Diner, Michael A. Bull, Felix C. Seidel, Abigail M. Nastan, and Earl G. Hansen
Atmos. Meas. Tech., 14, 5577–5591, https://doi.org/10.5194/amt-14-5577-2021,https://doi.org/10.5194/amt-14-5577-2021, 2021
Short summary
Estimation of PM2.5 concentration in China using linear hybrid machine learning model
Zhihao Song, Bin Chen, Yue Huang, Li Dong, and Tingting Yang
Atmos. Meas. Tech., 14, 5333–5347, https://doi.org/10.5194/amt-14-5333-2021,https://doi.org/10.5194/amt-14-5333-2021, 2021
Short summary
Species correlation measurements in turbulent flare plumes: considerations for field measurements
Scott P. Seymour and Matthew R. Johnson
Atmos. Meas. Tech., 14, 5179–5197, https://doi.org/10.5194/amt-14-5179-2021,https://doi.org/10.5194/amt-14-5179-2021, 2021
Short summary
Retrieval of aerosol microphysical properties from atmospheric lidar sounding: an investigation using synthetic measurements and data from the ACEPOL campaign
William G. K. McLean, Guangliang Fu, Sharon P. Burton, and Otto P. Hasekamp
Atmos. Meas. Tech., 14, 4755–4771, https://doi.org/10.5194/amt-14-4755-2021,https://doi.org/10.5194/amt-14-4755-2021, 2021
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, https://doi.org/10.1029/2003JD003915, 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, https://doi.org/10.1002/2013JD020188, 2014.
Atkinson, P. M. and Tatnall, A. R. L.: Introduction Neural networks in remote sensing, Int. J. Remote Sens., 18, 699–709, https://doi.org/10.1080/014311697218700, 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, https://doi.org/10.1029/2011JD016827, 2012.
Download
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.