Articles | Volume 20, issue 6
https://doi.org/10.5194/os-20-1567-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-20-1567-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Dec 2024
Research article |
| 02 Dec 2024
| 02 Dec 2024
Ensemble reconstruction of missing satellite data using a denoising diffusion model: application to chlorophyll a concentration in the Black Sea
GeoHydrodynamics and Environment Research (GHER), FOCUS, University of Liège, Liège, Belgium
Julien Brajard
Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen 5007, Norway
Aida Alvera-Azcárate
GeoHydrodynamics and Environment Research (GHER), FOCUS, University of Liège, Liège, Belgium
Bayoumy Mohamed
GeoHydrodynamics and Environment Research (GHER), FOCUS, University of Liège, Liège, Belgium
Charles Troupin
GeoHydrodynamics and Environment Research (GHER), FOCUS, University of Liège, Liège, Belgium
Jean-Marie Beckers
GeoHydrodynamics and Environment Research (GHER), FOCUS, University of Liège, Liège, Belgium
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Francesca Doglioni, Robert Ricker, Benjamin Rabe, Alexander Barth, Charles Troupin, and Torsten Kanzow
Earth Syst. Sci. Data, 15, 225–263, https://doi.org/10.5194/essd-15-225-2023, https://doi.org/10.5194/essd-15-225-2023, 2023
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Georges Baaklini, Roy El Hourany, Milad Fakhri, Julien Brajard, Leila Issa, Gina Fifani, and Laurent Mortier
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Alexander Barth, Aida Alvera-Azcárate, Charles Troupin, and Jean-Marie Beckers
Geosci. Model Dev., 15, 2183–2196, https://doi.org/10.5194/gmd-15-2183-2022, https://doi.org/10.5194/gmd-15-2183-2022, 2022
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Earth-observing satellites provide routine measurement of several ocean parameters. However, these datasets have a significant amount of missing data due to the presence of clouds or other limitations of the employed sensors. This paper describes a method to infer the value of the missing satellite data based on a convolutional autoencoder (a specific type of neural network architecture). The technique also provides a reliable error estimate of the interpolated value.
Malek Belgacem, Katrin Schroeder, Alexander Barth, Charles Troupin, Bruno Pavoni, Patrick Raimbault, Nicole Garcia, Mireno Borghini, and Jacopo Chiggiato
Earth Syst. Sci. Data, 13, 5915–5949, https://doi.org/10.5194/essd-13-5915-2021, https://doi.org/10.5194/essd-13-5915-2021, 2021
Short summary
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The Mediterranean Sea exhibits an anti-estuarine circulation, responsible for its low productivity. Understanding this peculiar character is still a challenge since there is no exact quantification of nutrient sinks and sources. Because nutrient in situ observations are generally infrequent and scattered in space and time, climatological mapping is often applied to sparse data in order to understand the biogeochemical state of the ocean. The dataset presented here partly addresses these issues.
Estrella Olmedo, Verónica González-Gambau, Antonio Turiel, Cristina González-Haro, Aina García-Espriu, Marilaure Gregoire, Aida Álvera-Azcárate, Luminita Buga, and Marie-Hélène Rio
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-364, https://doi.org/10.5194/essd-2021-364, 2021
Revised manuscript not accepted
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We present the first dedicated satellite salinity product in the Black Sea. We use the measurements provided by the European Soil Moisture and Ocean Salinity mission. We introduce enhanced algorithms for dealing with the contamination produced by the Radio Frequency Interferences that strongly affect this basin. We also provide a complete quality assessment of the new product and give an estimated accuracy of it.
Sylvain Watelet, Øystein Skagseth, Vidar S. Lien, Helge Sagen, Øivind Østensen, Viktor Ivshin, and Jean-Marie Beckers
Earth Syst. Sci. Data, 12, 2447–2457, https://doi.org/10.5194/essd-12-2447-2020, https://doi.org/10.5194/essd-12-2447-2020, 2020
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We present here a seasonal atlas of the Barents Sea including both temperature and salinity for the period 1965–2016. This atlas is curated using several in situ data sources interpolated thanks to the tool DIVA minimizing the expected errors. The results show a recent "Atlantification" of the Barents Sea, i.e., a general increase in both temperature and salinity, while its density remains stable. The atlas is made freely accessible (https://doi.org/10.21335/NMDC-2058021735).
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Short summary
Most satellite observations have gaps, for example, due to clouds. This paper presents a method to reconstruct missing data in satellite observations 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.
Most satellite observations have gaps, for example, due to clouds. This paper presents a method...
