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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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.
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Alexander Barth, Aida Alvera-Azcárate, Matjaz Licer, and Jean-Marie Beckers
Geosci. Model Dev., 13, 1609–1622, https://doi.org/10.5194/gmd-13-1609-2020, https://doi.org/10.5194/gmd-13-1609-2020, 2020
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Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
Nonlin. Processes Geophys., 26, 143–162, https://doi.org/10.5194/npg-26-143-2019, https://doi.org/10.5194/npg-26-143-2019, 2019
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Julien Brajard, Alberto Carrassi, Marc Bocquet, and Laurent Bertino
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-136, https://doi.org/10.5194/gmd-2019-136, 2019
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Luc Vandenbulcke and Alexander Barth
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Athanasia Iona, Athanasios Theodorou, Sarantis Sofianos, Sylvain Watelet, Charles Troupin, and Jean-Marie Beckers
Earth Syst. Sci. Data, 10, 1829–1842, https://doi.org/10.5194/essd-10-1829-2018, https://doi.org/10.5194/essd-10-1829-2018, 2018
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Athanasia Iona, Athanasios Theodorou, Sylvain Watelet, Charles Troupin, Jean-Marie Beckers, and Simona Simoncelli
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Hector Simon Benavides Pinjosovsky, Sylvie Thiria, Catherine Ottlé, Julien Brajard, Fouad Badran, and Pascal Maugis
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Marcos García Sotillo, Emilio Garcia-Ladona, Alejandro Orfila, Pablo Rodríguez-Rubio, José Cristobal Maraver, Daniel Conti, Elena Padorno, José Antonio Jiménez, Este Capó, Fernando Pérez, Juan Manuel Sayol, Francisco Javier de los Santos, Arancha Amo, Ana Rietz, Charles Troupin, Joaquín Tintore, and Enrique Álvarez-Fanjul
Earth Syst. Sci. Data, 8, 141–149, https://doi.org/10.5194/essd-8-141-2016, https://doi.org/10.5194/essd-8-141-2016, 2016
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J.-M. Beckers, A. Barth, I. Tomazic, and A. Alvera-Azcárate
Ocean Sci., 10, 845–862, https://doi.org/10.5194/os-10-845-2014, https://doi.org/10.5194/os-10-845-2014, 2014
J. Marmain, A. Molcard, P. Forget, A. Barth, and Y. Ourmières
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A. Barth, J.-M. Beckers, C. Troupin, A. Alvera-Azcárate, and L. Vandenbulcke
Geosci. Model Dev., 7, 225–241, https://doi.org/10.5194/gmd-7-225-2014, https://doi.org/10.5194/gmd-7-225-2014, 2014
Related subject area
Approach: Remote Sensing | Properties and processes: Coastal and near-shore processes
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This paper presents the results of the first scientific campaign of a new method to remotely sense the small-scale, fast-evolving dynamics that are vital to our understanding of coastal and shelf sea processes. This work represents the first demonstration of the simultaneous measurement of current and wind vectors from this novel method. Comparisons with other current measuring systems and models around the dynamic area of the Iroise Sea are presented and show excellent agreement.
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For the first time, high-resolution surface current data from high-frequency radar have been obtained along the central and southern coasts of Vietnam, and combined with a modelling approach, this is helping scientists to understand coastal processes. The research showed that the surface circulation is not only driven by winds, but also by other factors. This can enrich public knowledge of the coastal dynamics that govern other environmental impacts along the coasts.
Phoebe A. Hudson, Adrien C. H. Martin, Simon A. Josey, Alice Marzocchi, and Athanasios Angeloudis
Ocean Sci., 20, 341–367, https://doi.org/10.5194/os-20-341-2024, https://doi.org/10.5194/os-20-341-2024, 2024
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Satellite salinity data are used for the first time to study variability in Arctic freshwater transport from the Lena River and are shown to be a valuable tool for studying this region. These data confirm east/westerly wind is the main control on fresh water and sea ice transport rather than the volume of river runoff. The strong role of the wind suggests understanding how wind patterns will change is key to predicting future Arctic circulation and sea ice concentration.
Cited articles
Alvera-Azcárate, A., Barth, A., Parard, G., and Beckers, J.-M.: Analysis of SMOS sea surface salinity data using DINEOF, Remote Sens. Environ., 180, 137–145, https://doi.org/10.1016/j.rse.2016.02.044, special Issue: ESA's Soil Moisture and Ocean Salinity Mission – Achievements and Applications, 2016. a
Alvera-Azcárate, A., Van der Zande, D., Barth, A., Troupin, C., Martin, S., and Beckers, J.-M.: Analysis of 23 years of daily cloud-free chlorophyll and suspended particulate matter in the Greater North Sea, Frontiers in Marine Science, 8, 707632, https://doi.org/10.3389/fmars.2021.707632, 2021. a, b
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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...
