Articles | Volume 20, issue 6
https://doi.org/10.5194/os-20-1567-2024
https://doi.org/10.5194/os-20-1567-2024
Research article
 | 
02 Dec 2024
Research article |  | 02 Dec 2024

Ensemble reconstruction of missing satellite data using a denoising diffusion model: application to chlorophyll a concentration in the Black Sea

Alexander Barth, Julien Brajard, Aida Alvera-Azcárate, Bayoumy Mohamed, Charles Troupin, and Jean-Marie Beckers

Data sets

gher-uliege/DINDiff.jl: 0.1.0 Alexander Barth https://doi.org/10.5281/zenodo.13165363

Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997–ongoing) European Union-Copernicus Marine Service https://doi.org/10.48670/moi-00303

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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.