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Maria Paola Clarizia and Christopher S. Ruf

cm or greater ( Chen-Zhang et al. 2016 ). As a result, there is greater sensitivity of GNSS-R measurements to ocean swell or other, longer, wavelengths that are not directly forced by the local winds and that are often characterized through the significant wave height (SWH) ( Germain et al. 2004 ; Clarizia et al. 2009 ; Marchan-Hernandez et al. 2010 ; Zavorotny et al. 2014 ). This sensitivity can be problematic for the retrieval of wind speed, since a component of the variance in the

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Dion Häfner, Johannes Gemmrich, and Markus Jochum

1. Introduction During the last 25 years, the study of extreme ocean waves (also known as “rogue waves” or “freak waves”) has experienced a renaissance, triggered by the observation of the 25.6-m-high New Year wave at the Draupner oil rig in 1995 ( Haver 2004 ). By now, there are several known mechanisms to generate much higher waves than predicted by linear theory ( Adcock and Taylor 2014 ; Kharif and Pelinovsky 2003 ; Slunyaev et al. 2011 ; Dysthe et al. 2008 ), most of which rely on

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Ganesh K. Subramanian and Andreas Muschinski

of natural and manmade sources and phenomena, including avalanches, meteors, ocean waves, severe weather systems, tornadoes, turbulence on the ground and at higher levels, earthquakes, volcanoes, ground and air traffic, mine blasts, and nuclear explosions. An important subcategory of atmospheric infrasound are “microbaroms.” Microbaroms were discovered by Benioff and Gutenberg (1939) by means of “electromagnetic microbarographs” at the Seismological Laboratory in Pasadena, California. In their

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Laur Ferris, Donglai Gong, Sophia Merrifield, and Louis St. Laurent

. Laurent et al. 2012 ; Sheen et al. 2015 ). While details of downscale energy transfer are active areas of research, it is accepted that forward energy cascade in the interior ocean is principally accomplished through internal wave–wave interactions. Triads of internal waves with summatively resonant wavenumbers and frequencies (Hasselmann’s theorem) interact with each other [presumably in the universal manner of the Garrett–Munk (GM) spectrum] until they break and release their energy as turbulent

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Rodolfo Bolaños, Laurent O. Amoudry, and Ken Doyle

1. Introduction Instrumented bottom frames have been used since the 1960s to investigate bottom boundary layer processes and sediment dynamics. Data from these frames have led to a better understanding of near-bottom wave and current flows in the coastal ocean, and have been very important for the development of numerical models. These data have been used for calculations of bottom stress, bottom roughness, sediment flux, sediment resuspension, and near-bed and water column processes (e

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I. A. Houghton, P. B. Smit, D. Clark, C. Dunning, A. Fisher, N. J. Nidzieko, P. Chamberlain, and T. T. Janssen

, distributed sensing paradigms are not yet widely deployed in the oceans, and as a result, data density in open-ocean regions remains exceedingly low. Specifically, open-ocean weather data are sparse, and model skill is limited as a result. Marine weather is important for many industries, such as shipping operations and maritime safety, and for coastal inundation on exposed coastlines. Further, waves on the surface of the ocean are critical to describing many environmental processes due to their effect on

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Eric D’Asaro

1. Introduction Measurements at the air–sea interface are crucial for monitoring, parameterizing, and understanding the behavior of both the ocean and atmosphere. Maintaining sensors here, however, is often difficult: vibration and impact from the constant wave motion is destructive, intermittent saltwater immersion is corrosive, biological fouling is difficult to prevent, and vandalism is common. Measurements of air–sea properties using sensors away from the interface overcome many of these

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L. R. Centurioni

internal waves (NLIWs) in the northern South China Sea (nSCS). Within this experiment we tested a novel methodology, which employs an array of drifting instrumented chains, the Autonomous Drifting Ocean Stations (ADOS) with acoustic current profilers (ADOS-A hereafter), to measure the thermal structure and the three-dimensional velocity field of the upper ocean and of the NLIWs. Earlier examples of the use of drifting thermistor chains go back to the 1980s and 1990s ( Large et al. 1986 ; McPhaden et

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L. C. Bender III, N. L. Guinasso Jr., J. N. Walpert, and S. D. Howden

attenuation be adjusted to remove only the electronic and digitization noise. Furthermore, using an autocovariance estimate to determine the acceleration spectra allows one to eliminate frequency bins at very low frequencies, where no real wave energy is expected to exist. b. Wave model validation Significant wave heights determined from pitch and roll buoys are regularly used to validate numerical ocean wave model results. For example, Forristall (2007) compared hindcasts using Oceanweather’s standard

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John Lillibridge, Remko Scharroo, Saleh Abdalla, and Doug Vandemark

.g., Jackson et al. 1992 ; Liu et al. 2000 ; Vandemark et al. 2004 ) predict that differences between Ka and Ku bands are expected. Using optical techniques, Cox and Munk (1954) were able to establish a relationship between wind speed and ocean surface mean square slope (MSS). Assuming a quasi-specular reflection backscatter is inversely proportional to MSS, which can be divided into two components: one due to gravity waves and the other due to centimeter-scale gravity–capillary waves. For a one

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