Articles | Volume 16, issue 2
https://doi.org/10.5194/os-16-513-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-16-513-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimation of phytoplankton pigments from ocean-color satellite observations in the Senegalo–Mauritanian region by using an advanced neural classifier
Khalil Yala
CORRESPONDING AUTHOR
IPSL/LOCEAN, Sorbonne Université (Université Paris 6, CNRS,
IRD, MNHN), 4 Place Jussieu, 75005 Paris, France
N'Dèye Niang
CEDRIC, CNAM, 292 rue Saint Martin, 75003 Paris, France
Julien Brajard
IPSL/LOCEAN, Sorbonne Université (Université Paris 6, CNRS,
IRD, MNHN), 4 Place Jussieu, 75005 Paris, France
Nansen Center, Thormøhlensgate 47, 5006 Bergen, Norway
Carlos Mejia
IPSL/LOCEAN, Sorbonne Université (Université Paris 6, CNRS,
IRD, MNHN), 4 Place Jussieu, 75005 Paris, France
Mory Ouattara
CEDRIC, CNAM, 292 rue Saint Martin, 75003 Paris, France
Roy El Hourany
IPSL/LOCEAN, Sorbonne Université (Université Paris 6, CNRS,
IRD, MNHN), 4 Place Jussieu, 75005 Paris, France
Michel Crépon
IPSL/LOCEAN, Sorbonne Université (Université Paris 6, CNRS,
IRD, MNHN), 4 Place Jussieu, 75005 Paris, France
Sylvie Thiria
IPSL/LOCEAN, Sorbonne Université (Université Paris 6, CNRS,
IRD, MNHN), 4 Place Jussieu, 75005 Paris, France
UVSQ-LATMOS, 78035 Versailles, France
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Stéphane Doléac, Marina Lévy, Roy El Hourany, and Laurent Bopp
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Georges Baaklini, Julien Brajard, Leila Issa, Gina Fifani, Laurent Mortier, and Roy El Hourany
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Understanding the flow of the Levantine Sea surface current is not straightforward. We propose a study based on learning techniques to follow interactions between water near the shore and further out at sea. Our results show changes in the coastal currents past 33.8° E, with frequent instances of water breaking away along the Lebanese coast. These events happen quickly and sometimes lead to long-lasting eddies. This study underscores the need for direct observations to improve our knowledge.
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The Cryosphere, 18, 2161–2176, https://doi.org/10.5194/tc-18-2161-2024, https://doi.org/10.5194/tc-18-2161-2024, 2024
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Alexander Barth, Julien Brajard, Aida Alvera-Azcárate, Bayoumy Mohamed, Charles Troupin, and Jean-Marie Beckers
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Most satellite observations have gaps, for example, due to clouds. This paper presents a method to reconstruct missing data in satellite observation of the chlorophyll a concentration in the Black Sea. Rather than giving a single possible reconstructed field, the discussed method provides an ensemble of possible reconstructions using a generative neural network. The resulting ensemble is validated using techniques from numerical weather prediction and ocean modelling.
Roy El Hourany, Juan Pierella Karlusich, Lucie Zinger, Hubert Loisel, Marina Levy, and Chris Bowler
Ocean Sci., 20, 217–239, https://doi.org/10.5194/os-20-217-2024, https://doi.org/10.5194/os-20-217-2024, 2024
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Georges Baaklini, Roy El Hourany, Milad Fakhri, Julien Brajard, Leila Issa, Gina Fifani, and Laurent Mortier
Ocean Sci., 18, 1491–1505, https://doi.org/10.5194/os-18-1491-2022, https://doi.org/10.5194/os-18-1491-2022, 2022
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We use machine learning to analyze the long-term variation of the surface currents in the Levantine Sea, located in the eastern Mediterranean Sea. We decompose the circulation into groups based on their physical characteristics and analyze their spatial and temporal variability. We show that most structures of the Levantine Sea are becoming more energetic over time, despite those of the western area remaining the most dominant due to their complex bathymetry and strong currents.
Juliette Mignot, Carlos Mejia, Charles Sorror, Adama Sylla, Michel Crépon, and Sylvie Thiria
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Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
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Revised manuscript not accepted
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We explore the possibility of combining data assimilation with machine learning. We introduce a new hybrid method for a two-fold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting its future states. Numerical experiments have been carried out using the chaotic Lorenz 96 model, proving both the convergence of the hybrid method and its statistical skills including short-term forecasting and emulation of the long-term dynamics.
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Approach: Remote Sensing | Depth range: Surface | Geographical range: All Geographic Regions | Phenomena: Biological Processes
Ocean colour opportunities from Meteosat Second and Third Generation geostationary platforms
Carbon-based phytoplankton size classes retrieved via ocean color estimates of the particle size distribution
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MERIS-based ocean colour classification with the discrete Forel–Ule scale
Improvement to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data
Comparison of global ocean colour data records
Ewa J. Kwiatkowska, Kevin Ruddick, Didier Ramon, Quinten Vanhellemont, Carsten Brockmann, Carole Lebreton, and Hans G. Bonekamp
Ocean Sci., 12, 703–713, https://doi.org/10.5194/os-12-703-2016, https://doi.org/10.5194/os-12-703-2016, 2016
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Copernicus operational services include ocean colour applications from medium-resolution polar-orbiting satellite sensors. The goal is to satisfy EU reporting on the quality of marine, coastal and inland waters, as well as to support climate, fisheries, environmental monitoring, and sediment transport applications. Ocean colour data from polar platforms, however, suffer from fractional coverage. This effort is in developing water turbidity services from Meteosat geostationary instruments.
Tihomir S. Kostadinov, Svetlana Milutinović, Irina Marinov, and Anna Cabré
Ocean Sci., 12, 561–575, https://doi.org/10.5194/os-12-561-2016, https://doi.org/10.5194/os-12-561-2016, 2016
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Recent advances in ocean color remote sensing have allowed the quantification of the particle size distribution (and thus volume) of suspended particles in surface waters, using their backscattering signature. Here, we leverage these developments and use volume-to-carbon allometric relationships to quantify phytoplankton carbon globally using SeaWiFS ocean color data. Phytoplankton carbon concentrations are partitioned among three size classes: picoplankton, nanoplankton and microplankton.
G. Zibordi, F. Mélin, J.-F. Berthon, and E. Canuti
Ocean Sci., 9, 521–533, https://doi.org/10.5194/os-9-521-2013, https://doi.org/10.5194/os-9-521-2013, 2013
M. R. Wernand, A. Hommersom, and H. J. van der Woerd
Ocean Sci., 9, 477–487, https://doi.org/10.5194/os-9-477-2013, https://doi.org/10.5194/os-9-477-2013, 2013
A. Sadeghi, T. Dinter, M. Vountas, B. B. Taylor, M. Altenburg-Soppa, I. Peeken, and A. Bracher
Ocean Sci., 8, 1055–1070, https://doi.org/10.5194/os-8-1055-2012, https://doi.org/10.5194/os-8-1055-2012, 2012
S. Djavidnia, F. Mélin, and N. Hoepffner
Ocean Sci., 6, 61–76, https://doi.org/10.5194/os-6-61-2010, https://doi.org/10.5194/os-6-61-2010, 2010
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Short summary
The paper is a contribution to the study of phytoplankton pigment climatology from satellite ocean-color observations in the Senegalo–Mauritanian upwelling, which is a very productive region where in situ observations are lacking. We processed the satellite data with an efficient new neural network classifier. We were able to provide the climatological cycle of diatoms. This study may have an economic impact on fisheries thanks to better knowledge of phytoplankton dynamics.
The paper is a contribution to the study of phytoplankton pigment climatology from satellite...