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M. Gilman, A. Soloviev, and H. Graber

Abstract

A large dataset of high-resolution photographic images of far wakes of a volunteer observing ship (Royal Caribbean’s Explorer of the Seas) has been acquired under various meteorological conditions and ship operation modes. This work presents the description of instrumentation, methodology, and the results of the experiment. Environmental and ship operation factors that affect appearance and geometric properties of ship wakes in photographic and satellite-based radar images have been analyzed. The photo imagery reveals an asymmetry of the wake depending on wind direction relative to the ship course. In addition, a good agreement between the averaged shape of the wakes measured from the photographic images and a few available satellite-based radar images of the wake of the same ship has been found.

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A. Soloviev, R. Lukas, P. Hacker, H. Schoeberlein, M. Baker, and A. Arjannikov

Abstract

New techniques developed for near-surface turbulence measurements during the Tropical Ocean Global Atmosphere (TOGA) Coupled Ocean–Atmosphere Response Experiment (COARE) employ a difference in spatial scales of turbulence and surface waves. According to this approach, high relative speed of the measurements provides separation of the turbulence and surface wave signals. During the TOGA COARE field studies, high-resolution probes of pressure, temperature, conductivity, fluctuation velocity, and acceleration were mounted on the bow of the vessel at a 1.7-m depth in an undisturbed region ahead of the moving vessel. The localization in narrow frequency bands of the vibrations of the bow sensors allows accurate calculation of the dissipation rate. A coherent noise reduction algorithm effectively removes vibration contamination of the velocity dataset. Due to the presence of surface waves and the associated pitching of the vessel, the bow probes “scanned” the near-surface layer of the ocean. Contour plots calculated using the bow signals provide a spatial context for the analysis of near-surface turbulence. A fast-moving free-rising profiler equipped by similar probes sampled the near-surface turbulence during stations. Theory of the three-component electromagnetic velocity sensor and examples of data obtained by bow sensors and free-rising profiler are also presented in this paper.

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J. Boutin, Y. Chao, W. E. Asher, T. Delcroix, R. Drucker, K. Drushka, N. Kolodziejczyk, T. Lee, N. Reul, G. Reverdin, J. Schanze, A. Soloviev, L. Yu, J. Anderson, L. Brucker, E. Dinnat, A. Santos-Garcia, W. L. Jones, C. Maes, T. Meissner, W. Tang, N. Vinogradova, and B. Ward

Abstract

Remote sensing of salinity using satellite-mounted microwave radiometers provides new perspectives for studying ocean dynamics and the global hydrological cycle. Calibration and validation of these measurements is challenging because satellite and in situ methods measure salinity differently. Microwave radiometers measure the salinity in the top few centimeters of the ocean, whereas most in situ observations are reported below a depth of a few meters. Additionally, satellites measure salinity as a spatial average over an area of about 100 × 100 km2. In contrast, in situ sensors provide pointwise measurements at the location of the sensor. Thus, the presence of vertical gradients in, and horizontal variability of, sea surface salinity complicates comparison of satellite and in situ measurements. This paper synthesizes present knowledge of the magnitude and the processes that contribute to the formation and evolution of vertical and horizontal variability in near-surface salinity. Rainfall, freshwater plumes, and evaporation can generate vertical gradients of salinity, and in some cases these gradients can be large enough to affect validation of satellite measurements. Similarly, mesoscale to submesoscale processes can lead to horizontal variability that can also affect comparisons of satellite data to in situ data. Comparisons between satellite and in situ salinity measurements must take into account both vertical stratification and horizontal variability.

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