Articles | Volume 13, issue 2
https://doi.org/10.5194/amt-13-373-2020
https://doi.org/10.5194/amt-13-373-2020
Research article
 | 
03 Feb 2020
Research article |  | 03 Feb 2020

A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing

Andrew M. Sayer, Yves Govaerts, Pekka Kolmonen, Antti Lipponen, Marta Luffarelli, Tero Mielonen, Falguni Patadia, Thomas Popp, Adam C. Povey, Kerstin Stebel, and Marcin L. Witek

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
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
Atmos. Meas. Tech., 16, 5863–5881, https://doi.org/10.5194/amt-16-5863-2023,https://doi.org/10.5194/amt-16-5863-2023, 2023
Short summary
Uncertainty in aerosol–cloud radiative forcing is driven by clean conditions
Edward Gryspeerdt, Adam C. Povey, Roy G. Grainger, Otto Hasekamp, N. Christina Hsu, Jane P. Mulcahy, Andrew M. Sayer, and Armin Sorooshian
Atmos. Chem. Phys., 23, 4115–4122, https://doi.org/10.5194/acp-23-4115-2023,https://doi.org/10.5194/acp-23-4115-2023, 2023
Short summary
The CHROMA cloud-top pressure retrieval algorithm for the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission
Andrew M. Sayer, Luca Lelli, Brian Cairns, Bastiaan van Diedenhoven, Amir Ibrahim, Kirk D. Knobelspiesse, Sergey Korkin, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 969–996, https://doi.org/10.5194/amt-16-969-2023,https://doi.org/10.5194/amt-16-969-2023, 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

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Validation and Intercomparisons
Retrievals of aerosol optical depth over the western North Atlantic Ocean during ACTIVATE
Leong Wai Siu, Joseph S. Schlosser, David Painemal, Brian Cairns, Marta A. Fenn, Richard A. Ferrare, Johnathan W. Hair, Chris A. Hostetler, Longlei Li, Mary M. Kleb, Amy Jo Scarino, Taylor J. Shingler, Armin Sorooshian, Snorre A. Stamnes, and Xubin Zeng
Atmos. Meas. Tech., 17, 2739–2759, https://doi.org/10.5194/amt-17-2739-2024,https://doi.org/10.5194/amt-17-2739-2024, 2024
Short summary
Characterization of dust aerosols from ALADIN and CALIOP measurements
Rui Song, Adam Povey, and Roy G. Grainger
Atmos. Meas. Tech., 17, 2521–2538, https://doi.org/10.5194/amt-17-2521-2024,https://doi.org/10.5194/amt-17-2521-2024, 2024
Short summary
Lidar depolarization characterization using a reference system
Alkistis Papetta, Franco Marenco, Maria Kezoudi, Rodanthi-Elisavet Mamouri, Argyro Nisantzi, Holger Baars, Ioana Elisabeta Popovici, Philippe Goloub, Stéphane Victori, and Jean Sciare
Atmos. Meas. Tech., 17, 1721–1738, https://doi.org/10.5194/amt-17-1721-2024,https://doi.org/10.5194/amt-17-1721-2024, 2024
Short summary
Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols
Otto Hasekamp, Pavel Litvinov, Guangliang Fu, Cheng Chen, and Oleg Dubovik
Atmos. Meas. Tech., 17, 1497–1525, https://doi.org/10.5194/amt-17-1497-2024,https://doi.org/10.5194/amt-17-1497-2024, 2024
Short summary
Validation of initial observation from the first spaceborne high-spectral-resolution lidar with a ground-based lidar network
Qiantao Liu, Zhongwei Huang, Jiqiao Liu, Weibiao Chen, Qingqing Dong, Songhua Wu, Guangyao Dai, Meishi Li, Wuren Li, Ze Li, Xiaodong Song, and Yuan Xie
Atmos. Meas. Tech., 17, 1403–1417, https://doi.org/10.5194/amt-17-1403-2024,https://doi.org/10.5194/amt-17-1403-2024, 2024
Short summary

Cited articles

Ahn, C., Torres, O., and Jethva, H.: Assessment of OMI near‐UV aerosol optical depth over land, J. Geophys. Res.-Atmos., 119, 2457–2473, https://doi.org/10.1002/2013JD020188, 2013. a
Baum, B. A., Menzel, W. P., Frey, R. A., Tobin, D. C., Holz, R. E., Ackerman, S. A., Heidinger, A. K., and Yang, P.: MODIS Cloud-Top Property Refinements for Collection 6, J. Appl. Meteorol. Clim., 51, 1145–1163, https://doi.org/10.1175/JAMC-D-11-0203.1, 2012. a
Benedetti, A., Morcrette, J.‐J., Boucher, O., Dethof, A., Engelen, R. J., Fisher, M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J. W., Kinne, S., Mangold, A., Razinger, M., Simmons, A. J., and Suttie, M.: Aerosol analysis and forecast in the European Centre for Medium‐Range Weather Forecasts Integrated Forecast System: 2. Data assimilation, J. Geophys. Res., 114, D13205, https://doi.org/10.1029/2008JD011115, 2009. a
Benedetti, A., Reid, J. S., Knippertz, P., Marsham, J. H., Di Giuseppe, F., Rémy, S., Basart, S., Boucher, O., Brooks, I. M., Menut, L., Mona, L., Laj, P., Pappalardo, G., Wiedensohler, A., Baklanov, A., Brooks, M., Colarco, P. R., Cuevas, E., da Silva, A., Escribano, J., Flemming, J., Huneeus, N., Jorba, O., Kazadzis, S., Kinne, S., Popp, T., Quinn, P. K., Sekiyama, T. T., Tanaka, T., and Terradellas, E.: Status and future of numerical atmospheric aerosol prediction with a focus on data requirements, Atmos. Chem. Phys., 18, 10615–10643, https://doi.org/10.5194/acp-18-10615-2018, 2018. a
Bevan, S. L., North, P. R. J., Los, S. O., and Grey, W. M. F.: A global dataset of atmospheric aerosol optical depth and surface reflectance from AATSR, Remote Sens. Environ., 116, 199–210, https://doi.org/10.1016/j.rse.2011.05.024, 2012. a
Download
Short summary
Satellite measurements of the Earth are routinely processed to estimate useful quantities; one example is the amount of atmospheric aerosols (which are particles such as mineral dust, smoke, volcanic ash, or sea spray). As with all measurements and inferred quantities, there is some degree of uncertainty in this process. There are various methods to estimate these uncertainties. A related question is the following: how reliable are these estimates? This paper presents a method to assess them.