Modeling the SAR Signature of Nonlinear Internal Waves

Abstract

OBJECTIVES AND IMPORTANCENonlinear Internal Waves are pervasive globally, particularly in coastal waters. The currents and displacements associated with internal waves influence acoustic propagation and underwater navigation, as well as ocean transport and mixing. Synthetic Aperture Radar (SAR) imagery can reveal the surface manifestations of internal waves (IWs) in satellite imagery and so is routinely used to locate and characterize these features. Though some of the mechanisms that link the SAR signatures, surface processes, and the underlying internal structures have been understood for decades, a complete characterization has yet to emerge, making SAR imagery useful only as a qualitative tool. The objective of this research is to develop and validate a forward model to predict the SAR signature of NLIWs that explicitly includes relevant mechanisms that impact the sea surface roughness and corresponding backscattering cross section, such as wind speed and direction, compound modulation (i.e. modulation of intermediate-scale waves by IWs, which in turn modulate smaller waves), microscale breaking and breaking waves. SIGNIFICANT RESULTS AND LESSONS LEARNED BackgroundFor the first part of this effort we developed a computationally efficient, web-based implementation of the Lyzenga and Bennett (1988) (LB) model, which uses the action balance equation to model the interactions between surface waves and currents, such as those generated by the passage of an 1W. The action balance equation can be expressed, in general as:where N(k) is the wave action spectrum (see Phillips, 1980) at a given wavenumber, k; Q(k) is the action source term that includes contributions from wind input, wave interactions and dissipative processes; x indexes spatial location along the current profile attributable to an IW, o is the radian frequency, cgi is the group velocity and ui is the current due to the internal wave in the direction indexed by x.