Remote Sensing of Ocean Internal Waves: An Overview (original) (raw)
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Satellite Synthetic Aperture Radar Detection of Ocean Internal Waves in the South China Sea
2008
The long-term goal of the project is to meet the goal of ONR DRI NLIW, which is to achieve the basic science understanding that leads to a predictive capability that will be able to tell when and where non-linear internal waves will occur and what effects they will have on the hydrodynamic and acoustic environment. This project focuses on the use of remotely sensed variables, together with models, that can reproduce and predict the generation and structure of these waves, their evolution during propagation, and the processes controlling dissipation. OBJECTIVES 1) To determine the statistical features of ocean internal waves in SCS. Interpreting ten years of satellite synthetic aperture radar (SAR) images, the statistical features of ocean internal waves in SCS will be determined. The statistical items will include the wavelength distribution, distribution of number of waves in a wave packet, characteristic half width distribution, generation location distribution, occurrence seasonal distribution, and propagation direction spectrum on the continental shelf. The statistical analysis includes the ocean environment conditions and its seasonal variability. The results will provide the users an outline of internal wave behavior in SCS, serve as a basis for empirical prediction of internal wave behavior in SCS, and contribute to creation of a predictive system.
Global Atlas of Ocean Internal Waves
1998
Our long-term goal is to set up a database of ocean internal waves observed from US space shuttles. Each case includes photographs, interpretation maps, quantitative data and information extracted from images, and boundary conditions collected by in situ measurements. The database will be publicly accessible. Users, who are interested in ocean internal waves, may search for the information they need through the network.
Ocean internal wave spectra inferred from seismic reflection transects
Geophysical Research Letters, 2005
1] Internal waves affect many important dynamical processes in the ocean, but in situ observations of internal waves are infrequent and spatially sparse. Here we show that remote sensing of internal waves by marine seismic reflection methods can provide quantitative information on internal wave energy and its spatial variability at high lateral resolution and full ocean depth over large volumes of the ocean. Seismic images of the Norwegian Sea water column show reflections that capture snapshots of finestructure displacements due to internal waves. Horizontal wave number spectra derived from digitized reflection horizons in the open ocean compare favorably to the Garrett-Munk tow spectrum of oceanic internal wave displacements. Spectra within 10 km laterally and 200 m vertically of the continental slope show enhanced energy likely associated with internal wave-sloping boundary interactions.
Remote Sensing of Environment, 2004
This paper presents a method for estimating parameters of a two-layer stratified ocean using satellite SAR images. According to weak nonlinearity shallow water theory, internal solitary waves (ISWs) in stratified oceans may be either depression or elevation waves, depending on the sign of the quadratic nonlinearity coefficient in the KdV equation. It has been confirmed that ISWs can convert their polarity when passing through a turning point, where the quadratic nonlinearity coefficient changes sign. For a two-layer stratified ocean, the turning point is located where the upper and lower layer depths are equal. The authors suggest that depression, elevation and broadening ISWs can be discerned according to their different signatures in SAR images. It is also found that a SAR image can record a continuous evolution process from depression to elevation ISWs in its spatial domain, under conditions of a spatially inhomogeneous ocean environment. Therefore, the upper and lower layer depths can be calculated by determining the polarity conversion of ISWs observed in satellite SAR images. Furthermore, the density difference between the upper and lower layers can also be estimated, when the wave speed is known. We extract ocean stratification parameters, including upper layer depth and density difference, from polarity conversion of ISWs observed in a RADARSAT-1 SAR image taken over the northeastern South China Sea. Comparing the estimated results with field measurements, we find that this method can estimate the upper layer depth with considerable success. In estimating the density difference between the upper and lower layers, it also gives a quite reasonable result.