Articles | Volume 21, issue 4 
            
                
                    
            
            
            https://doi.org/10.5194/os-21-1677-2025
                    © Author(s) 2025. 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-21-1677-2025
                    © Author(s) 2025. This work is distributed under 
the Creative Commons Attribution 4.0 License.
                the Creative Commons Attribution 4.0 License.
Combining BioGeoChemical-Argo (BGC-Argo) floats and satellite observations for water column estimations of the particulate backscattering coefficient
Jorge García-Jiménez
CORRESPONDING AUTHOR
                                            
                                    
                                            Image Processing Laboratory, Universitat de València, Valencia, Spain
                                        
                                    Ana B. Ruescas
                                            Image Processing Laboratory, Universitat de València, Valencia, Spain
                                        
                                    Julia Amorós-López
                                            Image Processing Laboratory, Universitat de València, Valencia, Spain
                                        
                                    Raphaëlle Sauzède
                                            Institut de la Mer de Villefranche, FR3761, CNRS, Sorbonne Université, Paris, France
                                        
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Quentin Hyvernat, Alexandre Mignot, Elodie Gutknecht, Giovanni Ruggiero, Coralie Perruche, Guillaume Samson, Raphaëlle Sauzède, Olivier Aumont, Hervé Claustre, and Fabrizio D'Ortenzio
                                        EGUsphere, https://doi.org/10.5194/egusphere-2025-4369, https://doi.org/10.5194/egusphere-2025-4369, 2025
                                    This preprint is open for discussion and under review for Biogeosciences (BG). 
                                    Short summary
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                                                We introduce an iterative Importance Sampling (iIS) framework to optimize the PISCES model using BGC-Argo data. Using these data, 20 metrics are applied to better constrain parameter values. Three parameter selection strategies are compared: 29 main effects parameters, 66 parameters including interaction effects, and all 95 parameters. All yield statistically indistinguishable but significant skill gains, reducing NRMSE by 54–56% in median across assimilated metrics in the productive layer.
                                            
                                            
                                        Alexandre Mignot, Hervé Claustre, Gianpiero Cossarini, Fabrizio D'Ortenzio, Elodie Gutknecht, Julien Lamouroux, Paolo Lazzari, Coralie Perruche, Stefano Salon, Raphaëlle Sauzède, Vincent Taillandier, and Anna Teruzzi
                                    Biogeosciences, 20, 1405–1422, https://doi.org/10.5194/bg-20-1405-2023, https://doi.org/10.5194/bg-20-1405-2023, 2023
                                    Short summary
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                                                Numerical models of ocean biogeochemistry are becoming a major tool to detect and predict the impact of climate change on marine resources and monitor ocean health.  Here, we demonstrate the use of the global array of BGC-Argo floats for the assessment of biogeochemical models. We first detail the handling of the BGC-Argo data set for model assessment purposes. We then present 23 assessment metrics to quantify the consistency of BGC model simulations with respect to BGC-Argo data.
                                            
                                            
                                        A. B. Ruescas, M. Pereira-Sandoval, and A. Perez-Suay
                                    Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-1-W1-2021, 65–68, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-65-2022, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-65-2022, 2022
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                Short summary
            Estimation of particulate organic carbon (POC) relies on proxies like the particulate backscattering coefficient (bbp) derived from BioGeoChemical-Argo (BGC-Argo) floats and satellite data. BGC-Argo floats provide global insights into vertical bio-optical dynamics. This study integrates Sentinel-3 OLCI (Ocean and Land Colour Instrument) data and machine learning approaches to improve bbp estimates in the top 250 m of the water column. The results differ based on the dynamics of the study areas.
            Estimation of particulate organic carbon (POC) relies on proxies like the particulate...