Author

A. Charantonis

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#machine learning Preprint Sep 2026

Drift Field Net: Learning Ocean Lagrangian advection fields from in-situ and satellite observations

The North Pacific Subtropical Gyre (NPSG) is a major accumulation zone for floating plastic debris, resulting from basin-scale convergent ocean circulation. Effective cleanup strategies in this region rely on accurate forecasts of Lagrangian particle drift. Here, we introduce Drift Field Net (DFN), a deep neural networ...

T. Archambault, Pierre Garcia, Mattia Romero et al. · 0 citations

Jul 2026

BG4Sea: Biogeochemical Seasonal Forecastability via Progressive Information Scaling

BG4Sea is introduced, which to this knowledge is the first global, data-driven system to produce multivariate seasonal forecasts of the marine biogeochemical state, and predictability attribution to each component is discussed, alongside the model's structural limitations.

Gabriela Martinez Balbontin, A. Charantonis, Dominique Béréziat et al. · arXiv.org · 0 citations

#machine learning Preprint May 2026

Emulating the Forced Response of Climate Models with Generative Machine Learning

This research demonstrates that the model, ArchesClimate -- SSP, does not simply imitate scenarios seen during training, but is actually capable of modeling the response of a climate state to diverse forcings, an important step towards reliable and rapid climate model scenario generation.

Graham Clyne, Julia Kaltenborn, Peer Nowack et al. · 0 citations