Characterizing Alboran Sea Frontal Dynamics: A Multi-Variable Analysis with GRADHIST in Data Cubes

In this study, we used the DeepESDL framework to exploit multivariate data cubes for the identification and characterization of frontal zones in the Alboran Sea throughout the year 2023. By operating directly within a unified data hypercube architecture, we seamlessly evaluate the spatial coherence and temporal evolution across highly disparate environmental variables. We adapted the Gradient Histogram Method (GRADHIST) algorithm to run efficiently on data cube tracks of sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), and chlorophyll-a concentration (CHL) fields. This multi-dimensional approach enabled a detailed, synchronized description of the spatial distribution and evolution of fronts. The GRADHIST algorithm proved highly adaptable to the data cube format, utilizing a dynamic threshold that optimizes front detection across fields of fundamentally different physical natures and dynamic ranges.