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In-Situ Wave Observations in the High Resolution Air-Sea Interaction DRI

Ocean wave prediction models, based on a spectral energy balance, are widely used to obtain wind-wave forecasts and hindcasts on global and regional scales (e.g., Komen et al., 1994). However, these inherently stochastic models assume a Gaussian and homogeneous sea state and thus do not describe the...

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Bibliographic Details
Main Authors: Herbers, Thomas H, Janssen, Tim T
Format: Report
Language:English
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Summary:Ocean wave prediction models, based on a spectral energy balance, are widely used to obtain wind-wave forecasts and hindcasts on global and regional scales (e.g., Komen et al., 1994). However, these inherently stochastic models assume a Gaussian and homogeneous sea state and thus do not describe the nonlinear instability processes that can dramatically alter the structure of wave groups and produce anomalously large waves, also known as freak or rogue waves. Fully deterministic modeling capabilities are now becoming available that incorporate these nonlinear effects and provide the detailed phase-resolved sea surface predictions needed in many applications. Concurrent with the development of new models, advances in radar remote sensing techniques are enabling the detailed observation of the sea surface on the scales of wave groups and individual waves. The long-term goal of this research is to test these emerging new models and measurement technologies in realistic sea states and use them to better understand and predict the wave group structure and occurrence of extreme waves in the ocean. OBJECTIVES: observe the nonlinear evolution of wave groups in realistic broadband sea states; provide ground-truth data for testing the capabilities of shipboard wave radar systems; develop and test a prototype wave-resolving GPS drifter; develop models for nonlinear wave evolution in a varying medium; develop a statistical model for the evolution of inhomogeneous wave fields; and provide in-situ wave data for the verification of phase-resolving wave prediction models.