Articles | Volume 16, issue 23
https://doi.org/10.5194/amt-16-5863-2023
https://doi.org/10.5194/amt-16-5863-2023
Research article
 | 
07 Dec 2023
Research article |  | 07 Dec 2023

Simultaneous retrieval of aerosol and ocean properties from PACE HARP2 with uncertainty assessment using cascading neural network radiative transfer models

Meng Gao, Bryan A. Franz, Peng-Wang Zhai, Kirk Knobelspiesse, Andrew M. Sayer, Xiaoguang Xu, J. Vanderlei Martins, Brian Cairns, Patricia Castellanos, Guangliang Fu, Neranga Hannadige, Otto Hasekamp, Yongxiang Hu, Amir Ibrahim, Frederick Patt, Anin Puthukkudy, and P. Jeremy Werdell

Related authors

3-D Cloud Masking Across a Broad Swath using Multi-angle Polarimetry and Deep Learning
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
EGUsphere, https://doi.org/10.5194/egusphere-2023-2392,https://doi.org/10.5194/egusphere-2023-2392, 2024
Short summary
Performance evaluation of three bio-optical models in aerosol and ocean color joint retrievals
Neranga K. Hannadige, Peng-Wang Zhai, Meng Gao, Yongxiang Hu, P. Jeremy Werdell, Kirk Knobelspiesse, and Brian Cairns
Atmos. Meas. Tech., 16, 5749–5770, https://doi.org/10.5194/amt-16-5749-2023,https://doi.org/10.5194/amt-16-5749-2023, 2023
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
Atmos. Meas. Tech., 16, 2067–2087, https://doi.org/10.5194/amt-16-2067-2023,https://doi.org/10.5194/amt-16-2067-2023, 2023
Short summary
Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean
Meng Gao, Kirk Knobelspiesse, Bryan A. Franz, Peng-Wang Zhai, Andrew M. Sayer, Amir Ibrahim, Brian Cairns, Otto Hasekamp, Yongxiang Hu, Vanderlei Martins, P. Jeremy Werdell, and Xiaoguang Xu
Atmos. Meas. Tech., 15, 4859–4879, https://doi.org/10.5194/amt-15-4859-2022,https://doi.org/10.5194/amt-15-4859-2022, 2022
Short summary
Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model
Meng Gao, Bryan A. Franz, Kirk Knobelspiesse, Peng-Wang Zhai, Vanderlei Martins, Sharon Burton, Brian Cairns, Richard Ferrare, Joel Gales, Otto Hasekamp, Yongxiang Hu, Amir Ibrahim, Brent McBride, Anin Puthukkudy, P. Jeremy Werdell, and Xiaoguang Xu
Atmos. Meas. Tech., 14, 4083–4110, https://doi.org/10.5194/amt-14-4083-2021,https://doi.org/10.5194/amt-14-4083-2021, 2021
Short summary

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Stratospheric aerosol characteristics from SCIAMACHY limb observations: two-parameter retrieval
Christine Pohl, Felix Wrana, Alexei Rozanov, Terry Deshler, Elizaveta Malinina, Christian von Savigny, Landon A. Rieger, Adam E. Bourassa, and John P. Burrows
Atmos. Meas. Tech., 17, 4153–4181, https://doi.org/10.5194/amt-17-4153-2024,https://doi.org/10.5194/amt-17-4153-2024, 2024
Short summary
Retrieval and analysis of the composition of an aerosol mixture through Mie–Raman–fluorescence lidar observations
Igor Veselovskii, Boris Barchunov, Qiaoyun Hu, Philippe Goloub, Thierry Podvin, Mikhail Korenskii, Gaël Dubois, William Boissiere, and Nikita Kasianik
Atmos. Meas. Tech., 17, 4137–4152, https://doi.org/10.5194/amt-17-4137-2024,https://doi.org/10.5194/amt-17-4137-2024, 2024
Short summary
Transport of the Hunga volcanic aerosols inferred from Himawari-8/9 limb measurements
Fred Prata
Atmos. Meas. Tech., 17, 3751–3764, https://doi.org/10.5194/amt-17-3751-2024,https://doi.org/10.5194/amt-17-3751-2024, 2024
Short summary
A near-global multiyear climate data record of the fine-mode and coarse-mode components of atmospheric pure dust
Emmanouil Proestakis, Antonis Gkikas, Thanasis Georgiou, Anna Kampouri, Eleni Drakaki, Claire L. Ryder, Franco Marenco, Eleni Marinou, and Vassilis Amiridis
Atmos. Meas. Tech., 17, 3625–3667, https://doi.org/10.5194/amt-17-3625-2024,https://doi.org/10.5194/amt-17-3625-2024, 2024
Short summary
Innovative aerosol hygroscopic growth study from Mie–Raman–fluorescence lidar and microwave radiometer synergy
Robin Miri, Olivier Pujol, Qiaoyun Hu, Philippe Goloub, Igor Veselovskii, Thierry Podvin, and Fabrice Ducos
Atmos. Meas. Tech., 17, 3367–3375, https://doi.org/10.5194/amt-17-3367-2024,https://doi.org/10.5194/amt-17-3367-2024, 2024
Short summary

Cited articles

Agagliate, J., Foster, R., Ibrahim, A., and Gilerson, A.: A neural network approach to the estimation of in-water attenuation to absorption ratios from PACE mission measurements, Frontiers in Remote Sensing, 4, 1–20, https://doi.org/10.3389/frsen.2023.1060908, 2023. a
Aggarwal, C. C.: Neural Networks and Deep Learning: A Textbook, Springer, Cham, Switzerland, ISBN 3319944622, 2018. a
Anderson, G., Clough, S., Kneizys, F., Chetwynd, J., and Shettle, E.: AFGL Atmospheric Constituent Profiles (0.120 km), Air Force Geophysics Lab., Hanscom AFB, MA (USA), AFGL-TR-86-0110, 1986. a
Aryal, K., Zhai, P.-W., Gao, M., and Franz, B. A.: Instantaneous photosynthetically available radiation models for ocean waters using neural networks, Appl. Opt., 61, 9985–9995, https://doi.org/10.1364/AO.474914, 2022. a, b
Baydin, A. G., Pearlmutter, B. A., Radul, A. A., and Siskind, J. M.: Automatic differentiation in machine learning: a survey, J. Mach. Learn. Res., 18, 1–43, 2018. a
Short summary
This study evaluated the retrievability and uncertainty of aerosol and ocean properties from PACE's HARP2 instrument using enhanced neural network models with the FastMAPOL algorithm. A cascading retrieval method is developed to improve retrieval performance. A global set of simulated HARP2 data is generated and used for uncertainty evaluations.  The performance assessment demonstrates that the FastMAPOL algorithm is a viable approach for operational application to HARP2 data after PACE launch.