Title:
Space-time Measurements of Oceanic Sea States

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Fedele, Francesco
Benetazzo, Alvise
Gallego, Guillermo
Shih, Ping-Chang
Yezzi, Anthony
Barbariol, Francesco
Ardhuin,Fabrice
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Abstract
Stereo video techniques are effective for estimating the space-time wave dynamics over an area of the ocean. Indeed, a stereo camera view allows retrieval of both spatial and temporal data whose statistical content is richer than that of time series data retrieved from point wave probes.We present an application of the Wave Acquisition Stereo System (WASS) for the analysis of offshore video measurements of gravity waves in the Northern Adriatic Sea and near the southern seashore of the Crimean peninsula, in the Black Sea. We use classical epipolartechniques to reconstruct the sea surface from the stereo pairs sequentially in time, viz. asequence of spatial snapshots. We also present a variational approach that exploits the entire data image set providing a global space-time imaging of the sea surface, viz. simultaneous reconstruction of several spatial snapshots of the surface in order to guarantee continuity of the sea surface both in space and time. Analysis of the WASS measurements show that the sea surface can be accurately estimated in space and time together, yielding associated directionalspectra and wave statistics at a point in time that agrees well with probabilistic models. In particular, WASS stereo imaging is able to capture typical features of the wave surface,especially the crest-to-trough asymmetry due to second order nonlinearities, and the observedshape of large waves are fairly described by theoretical models based on the theory of quasi-determinism (Boccotti, 2000). Further, we investigate the space-time extremes of the observed stationary sea states, viz. the largest surface wave heights expected over a given area during thesea state duration. The WASS analysis provides the first experimental proof that a space-time extreme is generally larger than that observed in time via point measurements, in agreement withthe predictions based on stochastic theories for global maxima of Gaussian fields.
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2013-10
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