The Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) is a NASA Earth Ventures Suborbital Investigation designed to test the hypothesis that oceanic frontogenesis and the kilometer-scale ("submesoscale") instabilities that accompany it make important contributions to vertical exchange of climate and biological variables in the upper ocean. These processes have been difficult to resolve in observations and models. A necessary step toward testing the hypothesis was to make accurate measurements of upper-ocean velocity fields over a broad range of scales and to relate them to the observed variability of vertical transport and surface forcing. To achieve that, we used aircraft-based remote sensing, satellite remote sensing, ships, drifter deployments, and a fleet of autonomous vehicles. This paper will provide a brief overview of the S-MODE measurements, with a special focus on surface current measurements.

The Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) is a NASA Earth Ventures Suborbital Investigation designed to test the hypothesis that kilometer-scale (“submesoscale”) ocean eddies make important contributions to vertical exchange of climate and biological variables in the upper ocean. To test this hypothesis, S-MODE will employ a combination of aircraft-based remote sensing measurements of the ocean surface, measurements from ships, measurements from a variety of autonomous oceanographic platforms, and numerical modeling. The field campaign will consist of two month-long intensive operating periods (IOPs) that will be preceded by a smaller-scale pilot experiment to test and improve operational readiness and to compare measurements made from different platforms. The pilot experiment was delayed because of the 2020 coronavirus pandemic, and it is currently planned for October-November 2020.

Cyclonic (anticyclonic) oceanic eddies drive local upwelling (downwelling), leaving footprints in the sea surface temperature (SST) field as local extremes. Satellite-measured SST images can therefore be used to obtain information of the characteristics of oceanic eddies. Remotely sensed measurements represent very large data sets, both spatially and temporally. Manual eddy detection and analysis are thus practically impossible. In this letter, an automated scheme for eddy detection from remote sensing SST data is presented. The method is based on the analysis of velocity fields derived from SST measurements (thermal-wind velocity field). Using the geometric features of the velocity field, we can identify positions of eddy centers and derive eddy size, intensity, path, and lifetime. The scheme is applied to a realistic remotely sensed SST data set in a strong eddy activity region: Kuroshio Extension region.