Descriptors for sea surface temperature front regularity characterization

Monitoring sea surface temperature (SST) is of high interest. The dynamics of climatic events such as tropical storms or cyclones are closely related to the sea surface temperature. An important research topic in oceanography is about the understanding of the interactions between the ocean´s surface and it´s deeper layers. In Lapeyre et al. show that in the case of baroclinic unstable flows, the potential vorticity of mesoscale and submesoscale structures in the ocean interior are strongly correlated to the surface density structures. Thus, in frontal regions, knowledge of the ocean surface structures regularity gives insights about the 3D dynamics of the ocean. In this paper we focus on the analysis of the SST images level lines regularity from satellites images in regions in the neighborhood of oceanic fronts. Two regions of interest are considered: the region of Benguela and the region of Malvinas. Our investigations suggest that SST fronts belong to the class of statistical self similar curves. Taking into account self similar curves properties, we propose local curvature based descriptors for SST front region regularity characterization. We experimentally assess the efficiency of our descriptors by measuring their ability to capture SST image regularity seasonal variations.