Articles | Volume 12, issue 4
https://doi.org/10.5194/amt-12-2485-2019
https://doi.org/10.5194/amt-12-2485-2019
Research article
 | 
23 Apr 2019
Research article |  | 23 Apr 2019

FRESCO-B: a fast cloud retrieval algorithm using oxygen B-band measurements from GOME-2

Marine Desmons, Ping Wang, Piet Stammes, and L. Gijsbert Tilstra

Related authors

Assessment of the spectral misalignment effect (SMILE) on EarthCARE's Multi-Spectral Imager aerosol and cloud property retrievals
Nicole Docter, Anja Hünerbein, David P. Donovan, Rene Preusker, Jürgen Fischer, Jan Fokke Meirink, Piet Stammes, and Michael Eisinger
Atmos. Meas. Tech., 17, 2507–2519, https://doi.org/10.5194/amt-17-2507-2024,https://doi.org/10.5194/amt-17-2507-2024, 2024
Short summary
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-40,https://doi.org/10.5194/amt-2024-40, 2024
Preprint under review for AMT
Short summary
Evaluation of Aeolus feature mask and particle extinction coefficient profile products using CALIPSO data
Ping Wang, David Patrick Donovan, Gerd-Jan van Zadelhoff, Jos de Kloe, Dorit Huber, and Katja Reissig
EGUsphere, https://doi.org/10.5194/egusphere-2024-731,https://doi.org/10.5194/egusphere-2024-731, 2024
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
The EarthCARE lidar cloud and aerosol profile processor (A-PRO): the A-AER, A-EBD, A-TC and A-ICE products
David Patrick Donovan, Gerd-Jan van Zadelhoff, and Ping Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-218,https://doi.org/10.5194/egusphere-2024-218, 2024
Short summary
Monitoring the regional impact of forest loss and gain on carbon uptake with solar-induced fluorescence measurements from the GOME-2A and TROPOMI sensors
Juliëtte C. S. Anema, Klaas Folkert Boersma, Piet Stammes, Gerbrand Koren, William Woodgate, Philipp Köhler, Christian Frankenberg, and Jacqui Stol
EGUsphere, https://doi.org/10.5194/egusphere-2023-1930,https://doi.org/10.5194/egusphere-2023-1930, 2023
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024,https://doi.org/10.5194/amt-17-1739-2024, 2024
Short summary
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024,https://doi.org/10.5194/amt-17-1197-2024, 2024
Short summary
Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024,https://doi.org/10.5194/amt-17-999-2024, 2024
Short summary
Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024,https://doi.org/10.5194/amt-17-961-2024, 2024
Short summary
The EarthCARE mission: science data processing chain overview
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024,https://doi.org/10.5194/amt-17-839-2024, 2024
Short summary

Cited articles

Andrews, T., Gregory, J., Webb, M. J., and Taylor, K. E.: Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models, Geophys. Res. Lett., 39, L09712, https://doi.org/10.1029/2012GL051607, 2012. a
Boersma, K. F., Eskes, H. J., and Brinksma, E. J.: Error analysis for tropospheric NO2 retrieval from space, J. Geophys. Res., 109, D04311, https://doi.org/10.1029/2003JD003962, 2004. a
Bony, S. and Dufresne, J.-L.: Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models, Geophys. Res. Lett., 32, L20806, https://doi.org/10.1029/2005GL023851, 2005. a
De Haan, J. F., Bosma, P. B, and Hovenier, J. W.: The adding method for multiple scattering calculations of polarized light, Astron. Astrophys., 183, 371–391, 1987. a
Desmons, M., Ferlay, N., Parol, F., Mcharek, L., and Vanbauce, C.: Improved information about the vertical location and extent of monolayer clouds from POLDER3 measurements in the oxygen A-band, Atmos. Meas. Tech., 6, 2221–2238, https://doi.org/10.5194/amt-6-2221-2013, 2013. a
Download
Short summary
The FRESCO algorithm is a simple, fast and robust algorithm used to retrieve cloud information during operational satellite data processing. FRESCO retrieves effective cloud fraction and cloud pressure from measurements in the oxygen A band around 761 nm. In this paper, we propose a new version of the algorithm, called FRESCO-B, which is based on measurements in the oxygen B band around 687 nm. Such a method leads to more accurate retrievals for vegetated surfaces.