Speaker
Description
Mesoscale eddies modulate vertical particulate organic carbon (POC) flux, yet linking eddy dynamics to in-situ biological observations at scale remains a major data-engineering bottleneck. Satellite altimetry (e.g., SWOT, AVISO Eddy Atlas) and in-situ particle imaging (e.g., Underwater Vision Profiler, UVP) differ substantially in format, resolution, and archival structure.
We present the UVP extension of the Eddy Hunter System, a data-fusion framework under development at GEOMAR. Built on a PostGIS-indexed pipeline, the framework spatiotemporally co-locates UVP profiles with eddy trajectories drawn from a multi-mission altimetry eddy atlas. Profiles are retrievable by profile ID, single eddy observation, or complete eddy-track lifetime.
Each UVP cast provides depth-resolved particle-size distributions across 30 Equivalent Spherical Diameter (ESD) classes (40 µm to >26 mm). We standardize pressure onto a unified depth grid and stack profiles into fixed-shape tensors, yielding machine-learning-ready arrays.
Building on this pipeline, we propose encoding each profile into a low-dimensional embedding via a compact neural network and finally identify particle-spectrum clusters.
We will evaluate cluster membership against eddy polarity, distance to eddy center, and eddy age using permutation and mixed-effects models, testing whether mesoscale eddies imprint a detectable signature on particle size distributions, and how that signature varies from eddy core to periphery.