Acoustic spectrometry of bubbles in an estuarine front: Sound speed dispersion, void fraction, and bubble density.

Estuaries constitute a unique waveguide for acoustic propagation. The spatiotemporally varying three-dimensional front between the seawater and the outflowing freshwater during both flood and ebb constitutes an interfacial sound speed gradient capable of supporting significant vertical and horizontal acoustic refraction. The collision of these two water masses often produces breaking waves, injecting air bubbles into the water column; the negative vertical velocities of the denser saltwater often subduct bubbles to the bottom of these shallow waveguides, filling the water column with a bubbly mixture possessing a significantly lower effective sound speed. A field experiment was carried out in the mouth of Mobile Bay, Alabama in June 2021 to characterize estuarine bubble clouds in terms of their depth-dependent plume structure, frequency-dependent sound speed and attenuation, bubble size distribution, bubble number density, and void fraction. Results demonstrate that sound speed in the bubbly liquid consistently falls below the intrinsic sound speed of bubble-free water; specifically, the bubbly liquid 1.3 m below the surface in a front in this environment possesses effective sound speeds, void fractions, and bubble number densities of approximately 750 m/s, 0.001%, and 2 × 106 bubbles/m3, respectively.

Remote sensing of the nearshore.

The shallow waters of the nearshore ocean are popular, dynamic, and often hostile. Prediction in this domain is usually limited less by our understanding of the physics or by the power of our models than by the availability of input data, such as bathymetry and wave conditions. It is a challenge for traditional in situ instruments to provide these inputs with the appropriate temporal or spatial density or at reasonable logistical or financial costs. Remote sensing provides an attractive alternative. We discuss the range of different sensors that are available and the differing physical manifestations of their interactions with the ocean surface. We then present existing algorithms by which the most important geophysical variables can be estimated from remote sensing measurements. Future directions and opportunities will depend on expected developments in sensors and platforms and on improving processing algorithms, including data assimilation formalisms.