Articles | Volume 15, issue 18
https://doi.org/10.5194/amt-15-5289-2022
© Author(s) 2022. 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-15-5289-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Sep 2022
Research article |
| 19 Sep 2022
| 19 Sep 2022
Extended validation and evaluation of the OLCI–SLSTR SYNERGY aerosol product (SY_2_AOD) on Sentinel-3
Larisa Sogacheva
CORRESPONDING AUTHOR
Climate Programme, Finnish Meteorological Institute, Helsinki,
00540, Finland
Matthieu Denisselle
ACRI-ST, Sophia-Antipolis, 06410, France
Pekka Kolmonen
Climate Programme, Finnish Meteorological Institute, Helsinki,
00540, Finland
Timo H. Virtanen
Climate Programme, Finnish Meteorological Institute, Helsinki,
00540, Finland
Peter North
Global Environmental Modelling and Earth Observation (GEMEO), Dept.
of Geography, Swansea University, Swansea SA28PP, UK
Claire Henocq
ACRI-ST, Sophia-Antipolis, 06410, France
Silvia Scifoni
Serco Italia SpA for European Space Agency (ESA), European Space
Research Institute (ESRIN), 00044 Frascati, Italy
Steffen Dransfeld
European Space Agency (ESA), European Space Research Institute
(ESRIN), Frascati, Italy
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Short summary
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Jonas Gliß, Augustin Mortier, Michael Schulz, Elisabeth Andrews, Yves Balkanski, Susanne E. Bauer, Anna M. K. Benedictow, Huisheng Bian, Ramiro Checa-Garcia, Mian Chin, Paul Ginoux, Jan J. Griesfeller, Andreas Heckel, Zak Kipling, Alf Kirkevåg, Harri Kokkola, Paolo Laj, Philippe Le Sager, Marianne Tronstad Lund, Cathrine Lund Myhre, Hitoshi Matsui, Gunnar Myhre, David Neubauer, Twan van Noije, Peter North, Dirk J. L. Olivié, Samuel Rémy, Larisa Sogacheva, Toshihiko Takemura, Kostas Tsigaridis, and Svetlana G. Tsyro
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Short summary
Short summary
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Atmos. Chem. Phys., 20, 12431–12457, https://doi.org/10.5194/acp-20-12431-2020, https://doi.org/10.5194/acp-20-12431-2020, 2020
Short summary
Short summary
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Cited articles
Arbor, A., Briley, L., Dougherty, R., Wells, K., Hercula, T., Notaro, M.,
Rood, R., Andresen, J., Marsik, F., Prosperi, A., Jorns, J., Channell, K.,
Hutchinson, S., Kemp, C., and Gates, O.: A Practitioner's Guide to Climate
Model Scenarios, Great Lakes Integrated Sciences and Assessments (GLISA), University of Michigan, Michigan State University, NOAA,
https://glisa.umich.edu/wp-content/uploads/2021/03/A_Practitioners_Guide_to_Climate_Model_Scenarios.pdf (last access: 9 September 2022), 2021.
Bergquist, P. and Warshaw, C.: Does global warming increase public concern
about climate change?, J. Polit., 81, 686–691, 2019.
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. Environm., 116, 119–210, 2012.
Borowitz, M.: Open space. The global effort for open access to environmental
satellite data, Cambridge, MA, MIT Press, 432 pp., ISBN 9780262037181, 2018.
Chu, D. A., Kaufman, Y. J., Ichoku, C., Remer, L. A., Tanre, D., and Holben,
B. N.: Validation of MODIS aerosol optical depth retrieval over land,
Geophys. Res. Lett., 29, 8007, https://doi.org/10.1029/2001GL013205, 2002.
Committee on Earth Observation Satellites (CEOS): Space Agency Response to GCOS Implementation Plan, Coordination Group for Meteorological Satellites (CGMS), The Joint CEOS/CGMS Working Group on Climate (WGClimate), http://ceos.org/document_management/Working_Groups/WGClimate/Documents/Space%20Agency%20Response%20to%20GCOS%20IP%20v2.2.1.pdf (lsat access: 9 September 2022), 2017.
Cox, C. and Munk, W.: Measurements of the roughness of the sea surface from
photographs of the Sun's glitter, J. Opt. Soc. Am., 44, 838–850, 1954.
Davies, W. H. and North, P. R. J.: Synergistic angular and spectral estimation of aerosol properties using CHRIS/PROBA-1 and simulated Sentinel-3 data, Atmos. Meas. Tech., 8, 1719–1731, https://doi.org/10.5194/amt-8-1719-2015, 2015.
de Leeuw, G., Holzer-Popp, T., Bevan, S., Davies, W., Descloitres, J.,
Grainger, R.G., Griesfeller, J., Heckel, A., Kinne, S., Klüser, L.,
Kolmonen, P., Litvinov, P., Martynenko, D., North, P. J. R., Ovigneur, B.,
Pascal, N., Poulsen, C., Ramon, D., Schulz, M., Siddans, R., Sogacheva, L.,
Tanré, D., Thomas, G. E., Virtanen, T. H., von Hoyningen Huene, W.,
Vountas, M., and Pinnock, S.: Evaluation of seven European aerosol optical
depth retrieval algorithms for climate analysis, Remote Sens. Environ., 162, 295–315, https://doi.org/10.1016/j.rse.2013.04.023, 2015.
Dubovik, O., Schuster, G. L., Xu, F., Hu, Y., Bösch, H., Landgraf, J.,
and Li, Z.: Grand Challenges in Satellite Remote Sensing, Front. Remote
Sens., 2, 619818, https://doi.org/10.3389/frsen.2021.619818, 2021.
Eck, T. F., Holben, B. N., Reid, J. S., Dubovik, O., Smirnov, A., O'Neill,
N. T., Slutsker, I., and Kinne, S.: Wavelength dependence of the optical
depth of biomass burning, urban, and desert dust aerosols, J. Geophys. Res.,
104, 31333–31349, https://doi.org/10.1029/1999JD900923, 1999.
ESA climate office: Aerosol portal,
https://climate.esa.int/en/projects/aerosol/key-documents/, last access: 25 February 2022.
Eyre, J. R., Bell, W., Cotton, J., English, S. J., Forsythe, M., Healy, S.
B., and Pavelin, E. G.: Assimilation of satellite data in numerical weather
prediction. Part II: Recent years, Q. J. Roy. Meteor. Soc., 146, 1–36, https://doi.org/10.1002/qj.4228, 2022.
GCOS: https://ane4bf-datap1.s3.eu-west-1.amazonaws.com/wmod8_gcos/s3fs-public/aerosols_ecv_factsheet_201905.pdf?Sv_8X3rsnl_rqNQVLEIg5gzig53zTHox (last access: 25 February 2022), 2016.
Giles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A., Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R., Welton, E. J., Korkin, S. V., and Lyapustin, A. I.: Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements, Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, 2019.
Gliß, J., Mortier, A., Schulz, M., Andrews, E., Balkanski, Y., Bauer, S. E., Benedictow, A. M. K., Bian, H., Checa-Garcia, R., Chin, M., Ginoux, P., Griesfeller, J. J., Heckel, A., Kipling, Z., Kirkevåg, A., Kokkola, H., Laj, P., Le Sager, P., Lund, M. T., Lund Myhre, C., Matsui, H., Myhre, G., Neubauer, D., van Noije, T., North, P., Olivié, D. J. L., Rémy, S., Sogacheva, L., Takemura, T., Tsigaridis, K., and Tsyro, S. G.: AeroCom phase III multi-model evaluation of the aerosol life cycle and optical properties using ground- and space-based remote sensing as well as surface in situ observations, Atmos. Chem. Phys., 21, 87–128, https://doi.org/10.5194/acp-21-87-2021, 2021.
Harris, R. and Baumann, I.: Open data policies and satellite Earth
observation, Space Policy, 32, 44–53, https://doi.org/10.1016/j.spacepol.2015.01.001, 2015.
Hoffmann, R., Muttarak, R., Peisker, J., and Stanig, P.: Climate change
experiences raise environmental concerns and promote Green voting, Nat.
Clim. Chang., 12, 148–155, https://doi.org/10.1038/s41558-021-01263-8, 2022.
Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J. P., Setzer,
A., Vermote, E., Reagan, J.A., Kaufman, Y., Nakajima, T., Lavenu, F.,
Jankowiak, I., and Smirnov, A.: AERONET – A federated instrument network
and data archive for aerosol characterization, Remote Sens. Environ., 66,
1–16, 1998.
Holzer-Popp, T., de Leeuw, G., Griesfeller, J., Martynenko, D., Klüser, L., Bevan, S., Davies, W., Ducos, F., Deuzé, J. L., Graigner, R. G., Heckel, A., von Hoyningen-Hüne, W., Kolmonen, P., Litvinov, P., North, P., Poulsen, C. A., Ramon, D., Siddans, R., Sogacheva, L., Tanre, D., Thomas, G. E., Vountas, M., Descloitres, J., Griesfeller, J., Kinne, S., Schulz, M., and Pinnock, S.: Aerosol retrieval experiments in the ESA Aerosol_cci project, Atmos. Meas. Tech., 6, 1919–1957, https://doi.org/10.5194/amt-6-1919-2013, 2013.
Ichoku, C., Chu, D. A., Chu, S., Kaufman, Y. J., Remer, L. A., Tanré,
D., Slutsker, I., and Holben, B. N.: A spatio-temporal approach for global
validation and analysis of MODIS aerosol products, Geophys. Res. Lett.,
29, 8006, https://doi.org/10.1029/2001GL013206, 2002.
Julien, Y. and Sobrino, J. A.: NOAA-AVHRR Orbital Drift Correction:
Validating Methods Using MSG-SEVIRI Data as a Benchmark Dataset, Remote
Sens., 13, 925, https://doi.org/10.3390/rs13050925, 2021.
Khaki, M., Hendricks Franssen, H. J., and Han, S. C.: Multi-mission satellite
remote sensing data for improving land hydrological models via data
assimilation, Sci. Rep. 10, 18791, https://doi.org/10.1038/s41598-020-75710-5, 2020.
Kinne, S., O'Donnel, D., Stier, P., Kloster, S., Zhang, K., Schmidt, H., Rast, S., Giorgetta, M., Eck, T. F., and Stevens, B.: MAC-v1: A new global aerosol climatology for climate studies, J. Adv. Model. Earth Syst., 5, 704–740, https://doi.org/10.1002/jame.20035, 2013.
Koepke, P.: Effective Reflectance of Oceanic Whitecaps, Appli. Optics,
23, 1816–1824, 1984.
LAW consortium: Collocated AOD Sentinel 3 and ground-based measurements, https://law.acri-st.fr/home, last access: 10 January 2022.
Leiserowitz, A., Maibach, E., Rosenthal, S., Kotcher, J., Bergquist, P.,
Ballew, M., Goldberg, M., Gustafson, A., and Wang, X.: Climate Change in the
American Mind: April 2020, Yale University and George Mason University, Yale Program on Climate Change Communication, New Haven, CT,
https://climatecommunication.yale.edu/publications/climate-change-in-the-american-mind-april-2020/
(last access: 14 February 2022), 2020.
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, 2013.
Loew, A., Bell, W., Brocca, L., Bulgin, C. E., Burdanowitz, J., Calbet, X.,
Donner, R. V., Ghent, D., Gruber, A., Kaminski, T., Kinzel, J., Klepp, C.,
Lambert, J.-C., Schaepman-Strub, G., Schröder, M., and Verhoelst, T.:
Validation practices for satellite-based Earth observation data across
communities, Rev. Geophys., 55, 779–817, https://doi.org/10.1002/2017RG000562, 2017.
Meehl, G.A., Stocker, T. F., Collins, W. D., Friedlingstein, P., Gaye, A.
T., Gregory, J. M., Kitoh, A., Knutti, R., Murphy, J. M., Noda, A., Raper,
S. C. B., Watterson, I. G., Weaver, A. J., and Zhao, Z.-C.: Global Climate
Projections, in: Climate Change 2007: The Physical Science Basis.
Contribution of Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, Cambridge University Press,
Cambridge, UK, 747–846, https://www.ipcc.ch/report/ar4/wg1/ (last access: 13 March 2022), 2007.
Monahan, E. C. and O'Muircheartaigh, I.: Optimal power-law description of
oceanic whitecap dependence on wind speed, J. Phys. Oceanogr.,
10, 2094–2099, 1980.
Morel, A.: Optical modeling of the upper ocean in relation to its biogenous
matter content (case I waters), J. Geoph. Res., 93, 10749–10768, 1988.
Morys, M., Mims III, F. M., Hagerup, S., Anderson, E., Baker, A., Kia, J.,
and Walkup, T.: Design, calibration, and performance of MICROTOPS II
handheld ozone S. monitor and Sun photometer, J. Geophys. Res., 106,
14573–14582, https://doi.org/10.1029/2001JD900103, 2001.
North, P. and Heckel, A.: AOD-SYN Algorithm Theoretical Basis Document, V 1.12, S3-L2-AOD-SYN-ATBD, Swansea University,
https://sentinels.copernicus.eu/documents/247904/0/SYN_L2-3_ATBD.pdf/8dfd9043-5881-4b38-aae5-86fb9034a94d (last access: 9 September 2022), 2019.
North, P. R. J.: Estimation of aerosol opacity and land surface
bidirectional reflectance from ATSR-2 dual-angle imagery: Operational method
and validation, J. Geophys. Res., 107, 4149, https://doi.org/10.1029/2000JD000207, 2002.
North, P. R. J., Brockmann, C., Fischer, J., Gomez-Chova, L., Grey, W., Heckle A., Moreno, J., Preusker, R., and Regner, P.: MERIS/AATSR synergy algorithms for cloud screening, aerosol retrieval and atmospheric correction, in: Proc. 2nd MERIS/AATSR User Workshop, ESRIN, Frascati, 22–26 September 2008 (CD-ROM), ESA SP-666, ESA Publications Division, European Space Agency,
Noordwijk, the Netherlands, http://ggluck.swansea.ac.uk/ftp/SYNERGY/Synergy_Aerosol_Land_ATBD_20100316.pdf (last access: 16 September 2022), 2008.
Olbrich, P.: Open space: The global effort for open access to environmental
satellite data, Astropolitics, 16, 230–236, 2018.
O'Neill, N., Eck, T. F., Smirnov, A., Holben, B. N., and Thulasiraman, S.: Spectral discrimination of coarse and fine mode optical depth, J. Geophys. Res.-Atmos., 108, 4559, https://doi.org/10.1029/2002JD002975, 2003.
Popp, T., de Leeuw, G., Bingen, C., Brühl, C., Capelle, V., Chedin, A.,
Clarisse, L., Dubovik, O., Grainger, R., Griesfeller, J., Heckel, A., Kinne,
S., Klüser, L., Kosmale, M., Kolmonen, P., Lelli, L., Litvinov, P., Mei,
L., North, P., Pinnock, S., Povey, A., Robert, C., Schulz, M., Sogacheva,
L., Stebel, K., Stein Zweers, D., Thomas, G., Tilstra, L.G., Vandenbussche,
S., Veefkind, P., Vountas, M., and Xue, Y.: Development, Production and
Evaluation of Aerosol Climate Data Records from European Satellite
Observations (Aerosol_cci), Remote Sens., 8, 421,
https://doi.org/10.3390/rs8050421, 2016.
Remer, L. A., Kaufman, Y. J., Tanré, D., Mattoo, S., Chu, D. A.,
Martins, J. V., Li, R.-R., Ichoku, C., Levy, R. C., Kleidman, R. G., Eck, T.
F., Vermote, E., and Holben, B. N.: The MODIS Aerosol Algorithm, Products,
and Validation, J. Atmos. Sci., 62, 947–973, 2005.
Remer, L. A., Mattoo, S., Levy, R. C., and Munchak, L. A.: MODIS 3 km aerosol product: algorithm and global perspective, Atmos. Meas. Tech., 6, 1829–1844, https://doi.org/10.5194/amt-6-1829-2013, 2013.
S3 Production Service-ACRI: SENTINEL-3 OPTICAL SYNERGY AOD Package, S3A, https://scihub.copernicus.eu/dhus/#/home, last access: 13 March 2022.
S3 Production Service-SERCO: SENTINEL-3 OPTICAL SYNERGY AOD Package, S3B, https://scihub.copernicus.eu/dhus/#/home, last access: 13 March 2022.
Sayer, A. M., Hsu, N. C., Bettenhausen, C., Ahmad, Z., Holben, B. N.,
Smirnov, A., Thomas, G. E., and Zhang, J.: SeaWiFS Ocean Aerosol Retrieval
(SOAR): Algorithm, validation, and comparison with other data sets, J.
Geophys. Res., 117, D03206, https://doi.org/10.1029/2011JD016599, 2012a.
Sayer, A. M., Hsu, N. C., Bettenhausen, C., Jeong, M.-J., Holben, B. N., and Zhang, J.: Global and regional evaluation of over-land spectral aerosol optical depth retrievals from SeaWiFS, Atmos. Meas. Tech., 5, 1761–1778, https://doi.org/10.5194/amt-5-1761-2012, 2012b.
Sayer, A. M., Hsu, N. C., Bettenhausen, C., and Jeong, M.-J.: Validation and
uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data, J.
Geophys. Res., 118, 7864–7872, https://doi.org/10.1002/jgrd.50600, 2013.
Sayer, A. M., Hsu, N. C., Lee, J., Kim, W. V., Dubovik, O., Dutcher, S. T.,
Huang, D., Litvinov, P., Lyapustin, A., Tackett, J. L., and Winker, D. M.:
Validation of SOAR VIIRS over-water aerosol retrievals and context within
the global satellite aerosol data record, J. Geophys. Res.-Atmos., 123,
13496–13526, https://doi.org/10.1029/2018JD029465, 2018.
Sayer, A. M., Hsu, N. C., Lee, J., Kim, W., and Dutcher, S.: Validation,
stability, and consistency of MODIS Collection 6.1 and VIIRS Version 1 Deep
Blue aerosol data over land, J. Geophys. Res.-Atmos., 124, 4658–4688,
https://doi.org/10.1029/2018JD029598, 2019.
Sayer, A. M., Govaerts, Y., Kolmonen, P., Lipponen, A., Luffarelli, M., Mielonen, T., Patadia, F., Popp, T., Povey, A. C., Stebel, K., and Witek, M. L.: A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing, Atmos. Meas. Tech., 13, 373–404, https://doi.org/10.5194/amt-13-373-2020, 2020.
Shi, Y., Zhang, J., Reid, J. S., Hyer, E. J., and Hsu, N. C.: Critical evaluation of the MODIS Deep Blue aerosol optical depth product for data assimilation over North Africa, Atmos. Meas. Tech., 6, 949–969, https://doi.org/10.5194/amt-6-949-2013, 2013.
Smirnov, A., Holben, B. N., Sakerin, S. M., Kabanov, D. M., Slutsker, I., Chin, M., Diehl, T. L., Remer, L. A., Kahn, R., Ignatov, A., Liu, L., Mishchenko, M., Eck, T. F., Kucsera, T. L., Giles, D., and Kopelevich, O. V.: Ship-Based Aerosol Optical Depth Measurements in the Atlantic Ocean: Comparison with Satellite Retrievals and Gocart Model, Geophys. Res. Lett., 33, L14817, https://doi.org/10.1029/2006GL026051, 2006.
Smirnov, A., Holben, B. N., Slutsker, I., Giles, D. M., McClain, C. R.,
Eck, T. F., Sakerin, S. M., Macke, A., Croot, P., Zibordi, G., Quinn, P. K., Sciare, J., Kinne, S., Harvey, M., Smyth, T. J., Piketh, S., Zielinski, T., Proshutinsky, A., Goes, J. I., Nelson, N. B., Larouche, P., Radionov, V. F., Goloub, P., Krishna Moorthy, K., Matarrese, R., Robertson, E. J., and Jourdin, F.: Maritime Aerosol
Network as a component of Aerosol Robotic Network, J. Geophys. Res., 114,
D06204, https://doi.org/10.1029/2008JD011257, 2009.
Smirnov, A., Holben, B. N., Giles, D. M., Slutsker, I., O'Neill, N. T., Eck, T. F., Macke, A., Croot, P., Courcoux, Y., Sakerin, S. M., Smyth, T. J., Zielinski, T., Zibordi, G., Goes, J. I., Harvey, M. J., Quinn, P. K., Nelson, N. B., Radionov, V. F., Duarte, C. M., Losno, R., Sciare, J., Voss, K. J., Kinne, S., Nalli, N. R., Joseph, E., Krishna Moorthy, K., Covert, D. S., Gulev, S. K., Milinevsky, G., Larouche, P., Belanger, S., Horne, E., Chin, M., Remer, L. A., Kahn, R. A., Reid, J. S., Schulz, M., Heald, C. L., Zhang, J., Lapina, K., Kleidman, R. G., Griesfeller, J., Gaitley, B. J., Tan, Q., and Diehl, T. L.: Maritime aerosol network as a component of AERONET – first results and comparison with global aerosol models and satellite retrievals, Atmos. Meas. Tech., 4, 583–597, https://doi.org/10.5194/amt-4-583-2011, 2011.
Sogacheva, L., Kolmonen, P., Virtanen, T. H., Rodriguez, E., Saponaro, G., and de Leeuw, G.: Post-processing to remove residual clouds from aerosol optical depth retrieved using the Advanced Along Track Scanning Radiometer, Atmos. Meas. Tech., 10, 491–505, https://doi.org/10.5194/amt-10-491-2017, 2017.
Sogacheva, L., de Leeuw, G., Rodriguez, E., Kolmonen, P., Georgoulias, A. K., Alexandri, G., Kourtidis, K., Proestakis, E., Marinou, E., Amiridis, V., Xue, Y., and van der A, R. J.: Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations – Part 1: ATSR (1995–2011) and MODIS C6.1 (2000–2017), Atmos. Chem. Phys., 18, 11389–11407, https://doi.org/10.5194/acp-18-11389-2018, 2018a.
Sogacheva, L., Rodriguez, E., Kolmonen, P., Virtanen, T. H., Saponaro, G., de Leeuw, G., Georgoulias, A. K., Alexandri, G., Kourtidis, K., and van der A, R. J.: Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations – Part 2: AOD time series for 1995–2017 combined from ATSR ADV and MODIS C6.1 and AOD tendency estimations, Atmos. Chem. Phys., 18, 16631–16652, https://doi.org/10.5194/acp-18-16631-2018, 2018b.
Sogacheva, L., Popp, T., Sayer, A. M., Dubovik, O., Garay, M. J., Heckel, A., Hsu, N. C., Jethva, H., Kahn, R. A., Kolmonen, P., Kosmale, M., de Leeuw, G., Levy, R. C., Litvinov, P., Lyapustin, A., North, P., Torres, O., and Arola, A.: Merging regional and global aerosol optical depth records from major available satellite products, Atmos. Chem. Phys., 20, 2031–2056, https://doi.org/10.5194/acp-20-2031-2020, 2020.
Takamura, T. and Nakajima, T.: Overview of SKYNET and its activities, Opt.
Pura Apl. 37, 3303–3308, 2004.
Wei, J., Li, Z. Q., Peng, Y. R., and Sun, L.: MODIS Collection 6.1 aerosol
optical depth products over land and ocean: Validation and comparison,
Atmos. Environ., 201, 428–440, https://doi.org/10.1016/j.atmosenv.2018.12.004, 2019.
Short summary
The aim of this study was to provide global characterisation of a new SYNERGY aerosol product derived from the data from the OLCI and SLSTR sensors aboard the Sentinel-3A and Sentinel-3B satellites. Over ocean, the performance of SYNERGY-retrieved AOD is good. Reduced performance over land was expected since the surface reflectance and angular distribution of scattering are more difficult to treat. Validation statistics are often slightly better for S3B and in the Southern Hemisphere.
The aim of this study was to provide global characterisation of a new SYNERGY aerosol product...
