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, caused by the specular reflections of direct solar radiance from the sea surface, have a significant impact on remotely sensed ocean color, reflectance, and surface temperature observations and need to be correctly accounted for (e.g., Gordon and Wang 1992 , 1994 ). The seminal work of Cox and Munk (1954) was the first attempt to derive an ocean surface slope distribution model, based on a limited number of sun-glitter photographs collected from a U.S. Air Force Boeing B-17G aircraft off the
, caused by the specular reflections of direct solar radiance from the sea surface, have a significant impact on remotely sensed ocean color, reflectance, and surface temperature observations and need to be correctly accounted for (e.g., Gordon and Wang 1992 , 1994 ). The seminal work of Cox and Munk (1954) was the first attempt to derive an ocean surface slope distribution model, based on a limited number of sun-glitter photographs collected from a U.S. Air Force Boeing B-17G aircraft off the
1. Introduction The characterization of surface gravity waves is important for air–sea interaction processes, such as the exchange of energy, momentum, mass, and heat between the ocean and the atmosphere, as well as for risk assessment for offshore structures and related engineering applications. This study is concerned with the characterization of spatial statistics of the sea surface elevation using airborne lidar observations. Several field investigations have reported on the statistics of
1. Introduction The characterization of surface gravity waves is important for air–sea interaction processes, such as the exchange of energy, momentum, mass, and heat between the ocean and the atmosphere, as well as for risk assessment for offshore structures and related engineering applications. This study is concerned with the characterization of spatial statistics of the sea surface elevation using airborne lidar observations. Several field investigations have reported on the statistics of
(NOAA station 8779750). (c) Significant wave height H s , wave age c p / U 10 , and peak period T p measured by the lidar. The corresponding offshore buoy wave observations of H s and T p are shown with solid blue and red lines, respectively. (d) Mixed layer depth (MLD; red line), initial mean depth of dye patches (blue line), and optical depth at the emission band (1/ K em ) (green line). (e) Turbulent Langmuir number (La t ) calculated from the lidar and wind data and mean near surface
(NOAA station 8779750). (c) Significant wave height H s , wave age c p / U 10 , and peak period T p measured by the lidar. The corresponding offshore buoy wave observations of H s and T p are shown with solid blue and red lines, respectively. (d) Mixed layer depth (MLD; red line), initial mean depth of dye patches (blue line), and optical depth at the emission band (1/ K em ) (green line). (e) Turbulent Langmuir number (La t ) calculated from the lidar and wind data and mean near surface
observations ( Komen et al. 1984 ; Banner and Young 1994 ; Alves and Banner 2003 , hereafter AB03 ). Numerical prediction models that run on a daily basis at global and regional scales, commonly referred to as operational models, are limited to coarse spectral and spatial grids and with parameterizations or approximations of the nonlinear energy fluxes resulting from wave–wave interactions constrained by the computational time available. Thus, present operational wind-wave models can only predict the
observations ( Komen et al. 1984 ; Banner and Young 1994 ; Alves and Banner 2003 , hereafter AB03 ). Numerical prediction models that run on a daily basis at global and regional scales, commonly referred to as operational models, are limited to coarse spectral and spatial grids and with parameterizations or approximations of the nonlinear energy fluxes resulting from wave–wave interactions constrained by the computational time available. Thus, present operational wind-wave models can only predict the
................................................. WILLIAM BLUMEN 1984--1993Mars Atmosphere Pressure Periodicities from Viking Observations ....................................................... R. D. SHARMAN AND J. A. RYAN 1994--2001Time Spectral Analysis of Midlatitude Disturbances in the Martian Atmosphere...................................................... JEFFREY R. BARNES 2002--2015Response of Deep Tropical Cumulus Clouds to Mesoscale Processes
................................................. WILLIAM BLUMEN 1984--1993Mars Atmosphere Pressure Periodicities from Viking Observations ....................................................... R. D. SHARMAN AND J. A. RYAN 1994--2001Time Spectral Analysis of Midlatitude Disturbances in the Martian Atmosphere...................................................... JEFFREY R. BARNES 2002--2015Response of Deep Tropical Cumulus Clouds to Mesoscale Processes
moving (pitch–roll) buoy or measurement platform. The directional and frequency response of these systems is limited and not capable of the resolution required to fully test modern theories of directional surface wave spectra. Additionally, Doppler shift induced by longer dominant waves can distort the high-frequency portion of wave frequency spectra ( Kitaigordskii et al. 1975 ; Banner 1990 ). It is only in the last two decades that observations of bimodal directional spectra at wavenumbers and
moving (pitch–roll) buoy or measurement platform. The directional and frequency response of these systems is limited and not capable of the resolution required to fully test modern theories of directional surface wave spectra. Additionally, Doppler shift induced by longer dominant waves can distort the high-frequency portion of wave frequency spectra ( Kitaigordskii et al. 1975 ; Banner 1990 ). It is only in the last two decades that observations of bimodal directional spectra at wavenumbers and
after the field deployment at the CIRPAS calibration facility, accurately characterizing the lower and upper bounds of each size-range channel for all four sensors. b. Wave and surface kinematics The sea surface elevation was measured with a scanning lidar instrument: the National Aeronautics and Space Administration (NASA)/EG&G Airborne Topographic Mapper (ATM III). Although this system is primarily used to characterize ice sheet thickness in polar regions as part of the NASA Ice Bridge project
after the field deployment at the CIRPAS calibration facility, accurately characterizing the lower and upper bounds of each size-range channel for all four sensors. b. Wave and surface kinematics The sea surface elevation was measured with a scanning lidar instrument: the National Aeronautics and Space Administration (NASA)/EG&G Airborne Topographic Mapper (ATM III). Although this system is primarily used to characterize ice sheet thickness in polar regions as part of the NASA Ice Bridge project
characterized the modulation of the wave field by tidal ( Vincent 1979 ; Masson 1996 ; Pearman et al. 2014 ) and large-scale currents ( Kudryavtsev et al. 1995 , Wang et al. 1994 ; Haus 2007 ). However, more measurements are needed to improve numerical wave models and wave breaking parameterizations in conditions with strong wave–current interaction (e.g., Romero and Melville 2010a ; Banner and Morison 2010 ; Ardhuin et al. 2010 , 2012 ). This study presents novel wave observations from two
characterized the modulation of the wave field by tidal ( Vincent 1979 ; Masson 1996 ; Pearman et al. 2014 ) and large-scale currents ( Kudryavtsev et al. 1995 , Wang et al. 1994 ; Haus 2007 ). However, more measurements are needed to improve numerical wave models and wave breaking parameterizations in conditions with strong wave–current interaction (e.g., Romero and Melville 2010a ; Banner and Morison 2010 ; Ardhuin et al. 2010 , 2012 ). This study presents novel wave observations from two
temperature fields, as well as their modulation by other physical processes, research into ocean surface signatures requires in situ measurements of the lower marine atmospheric boundary layer and the surface waters before the full fidelity of these techniques can be established. Unlike the scattering methods discussed above, airborne lidar directly measures the short surface waves. This includes the modulation of the surface gravity waves by internal waves (hereinafter denoted IW) as well as the IW
temperature fields, as well as their modulation by other physical processes, research into ocean surface signatures requires in situ measurements of the lower marine atmospheric boundary layer and the surface waters before the full fidelity of these techniques can be established. Unlike the scattering methods discussed above, airborne lidar directly measures the short surface waves. This includes the modulation of the surface gravity waves by internal waves (hereinafter denoted IW) as well as the IW
during the SOCAL2013 experiment using observations collected from an airborne topographic scanning lidar ( Lenain and Melville 2017 ). Note the presence of both equilibrium and saturation ranges, showing both −5/2 and −3 spectral slopes over the three-decade bandwidth of the data. (b) Transition wavenumber k n plotted against g / u * 2 for three experiments (SOCAL2013, LCDRI2017, and ISDRI2017). The Phillips (1985) parameter r = k n u * 2 / g is best fit to the data (red dashed line) and is
during the SOCAL2013 experiment using observations collected from an airborne topographic scanning lidar ( Lenain and Melville 2017 ). Note the presence of both equilibrium and saturation ranges, showing both −5/2 and −3 spectral slopes over the three-decade bandwidth of the data. (b) Transition wavenumber k n plotted against g / u * 2 for three experiments (SOCAL2013, LCDRI2017, and ISDRI2017). The Phillips (1985) parameter r = k n u * 2 / g is best fit to the data (red dashed line) and is
