Fronts and eddies identified with aerial guidance are seeded with drifters to quantify submesoscale flow kinematics. The Lagrangian observations show mean divergence and vorticity values that can exceed 5 times the Coriolis frequency. Values are the largest observed in the field to date and represent an extreme departure from geostrophic dynamics. The study also quantifies errors and biases associated with Lagrangian observations of the underlying velocity strain tensor. The greatest error results from undersampling, even with a large number of drifters. A significant bias comes from inhomogeneous sampling of convergent regions that accumulate drifters within a few hours of deployment. The study demonstrates a Lagrangian sampling paradigm for targeted submesoscale structures over a broad range of scales and presents flow kinematic values associated with vertical velocities O(10) m h−1 that can have profound implications on ocean biogeochemistry.
Empirical orthogonal function (EOF) analysis has been widely used in meteorology and oceanography to extract dominant modes of behavior in scalar and vector datasets. For analysis of two-dimensional vector fields, such as surface winds or currents, use of the complex EOF method has become widespread. In the present paper, this method is compared with a real-vector EOF method that apparently has previously been unused for current or wind fields in oceanography or meteorology. It is shown that these two methods differ primarily with respect to the concept of optimal representation. Further, the real-vector analysis can easily be extended to threedimensional vector fields, whereas the complex method cannot. To illustrate the differences between approaches, both methods are applied to Ocean Surface Current Radar data collected off Cape Hatteras, North Carolina, in June and July 1993. For this dataset, while the complex analysis ''converges'' in fewer modes, the real analysis is better able to isolate flows with wide cross-shelf structures such as tides.
[1] Infrared imaging provides a new way to detect internal waves under conditions where techniques that rely on backscatter from the sea surface may not be effective and it provides a new means to investigate the spatial variability associated with internal waves. This is illustrated with imagery collected in a bay under light winds using an airborne infrared camera. The internal waves appear as groups of dark and bright bands, corresponding to surface temperature fluctuations of about 0.05°C. A signal of this size is shown to be plausible based on straining of the water's surface thermal boundary layer ('cool skin') by internal waves having a strain rate of the order of 10 À2 s À1 . In addition, fine-scale temperature structure was detected that we speculate may indicate instabilities induced by the internal waves.
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