Coupled sea surface‐atmosphere model: 2. Spectrum of short wind waves

Kudryavtsev, V. N.; Makin, V. K.; Chapron, Bertrand

doi:10.1029/1999jc900005

Cited by 152 publications

(154 citation statements)

References 35 publications

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“…This model was selected for the following reasons: it was derived based on physical principles and measurements and has been used in many studies in radar remote sensing research. Also, at low wavenumbers, the DP model is identical to spectral model of Kudryavtsev et al( 1999), which is used in a subsequent part of this effort. The DP spectrum employs the same source terms and action modulation framework for computing SW-IW interactions as the LB model, but is more physically realistic.…”

Section: Imlementationmentioning

confidence: 99%

“…This approach uses the innovative spectral model of Kudryavtsev et al (1999) and includes an action source term associated with the generation of parasitic capillary waves produced from the IW-SW interaction. An illustration of this spectral model is provided in Figure 5, for the same conditions (wind speed and direction) used to illustrate the DP model (Figure 4).…”

Section: Imlementationmentioning

confidence: 99%

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Modeling the SAR Signature of Nonlinear Internal Waves

Lettvin¹

2008

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OBJECTIVES AND IMPORTANCENonlinear Internal Waves are pervasive globally, particularly in coastal waters. The currents and displacements associated with internal waves influence acoustic propagation and underwater navigation, as well as ocean transport and mixing. Synthetic Aperture Radar (SAR) imagery can reveal the surface manifestations of internal waves (IWs) in satellite imagery and so is routinely used to locate and characterize these features. Though some of the mechanisms that link the SAR signatures, surface processes, and the underlying internal structures have been understood for decades, a complete characterization has yet to emerge, making SAR imagery useful only as a qualitative tool. The objective of this research is to develop and validate a forward model to predict the SAR signature of NLIWs that explicitly includes relevant mechanisms that impact the sea surface roughness and corresponding backscattering cross section, such as wind speed and direction, compound modulation (i.e. modulation of intermediate-scale waves by IWs, which in turn modulate smaller waves), microscale breaking and breaking waves. SIGNIFICANT RESULTS AND LESSONS LEARNED BackgroundFor the first part of this effort we developed a computationally efficient, web-based implementation of the Lyzenga and Bennett (1988) (LB) model, which uses the action balance equation to model the interactions between surface waves and currents, such as those generated by the passage of an 1W. The action balance equation can be expressed, in general as:where N(k) is the wave action spectrum (see Phillips, 1980) at a given wavenumber, k; Q(k) is the action source term that includes contributions from wind input, wave interactions and dissipative processes; x indexes spatial location along the current profile attributable to an IW, o is the radian frequency, cgi is the group velocity and ui is the current due to the internal wave in the direction indexed by x.

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Section: Imlementationmentioning

confidence: 99%

Section: Imlementationmentioning

confidence: 99%

Modeling the SAR Signature of Nonlinear Internal Waves

Lettvin¹

2008

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“…To be consistent with the in situ observations of gravity-capillary waves, Apel [23] and Elfouhaily et al [21] proposed a full wavenumber spectra based on the developed spectra. Kudryavtsev et al derived a physical model of the short wind wave spectrum by analyzing the input and dissipation of sea wave energy based on the propagation theory of the spectral energy density [29]. Including scattering theory and radar data, the precise retrieval of short-wave spectral properties was implemented with active microwave observations [22,25,26,30].…”

Section: An Improved Directional Spectrummentioning

confidence: 99%

An Improved Spectrum Model for Sea Surface Radar Backscattering at L-Band

Yang

Chen

et al. 2017

Remote Sensing

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L-band active microwave remote sensing is one of the most important technical methods of ocean environmental monitoring and dynamic parameter retrieval. Recently, a unique negative upwind-crosswind (NUC) asymmetry of L-band ocean backscatter over a low wind speed range was observed. To study the directional features of L-band ocean surface backscattering, a new directional spectrum model is proposed and built into the advanced integral equation method (AIEM). This spectrum combines Apel's omnidirectional spectrum and an improved empirical angular spreading function (ASF). The coefficients in the ASF were determined by the fitting of radar observations so that it provides a better description of wave directionality, especially over wavenumber ranges from short-gravity waves to capillary waves. Based on the improved spectrum and the AIEM scattering model, L-band NUC asymmetry at low wind speeds and positive upwind-crosswind (PUC) asymmetry at higher wind speeds are simulated successfully. The model outputs are validated against Aquarius/SAC-D observations under different incidence angles, azimuth angles and wind speed conditions.

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“…This effect was subsequently used to calculate the "back-effect" of a wave field with a continuous Fourier spectrum, for which there is thus a continuous range of critical levels, on the mean flow field itself (Janssen, 1989;Jenkins, 1993;Makin and Kudryavtsev, 1999;Kudryavtsev et al, 1999;Kudryavtsev et al, 2001). …”

Section: Momentum Balance Wave Generation and Dissipationmentioning

confidence: 99%

Interaction of waves, surface currents, and turbulence: the application of surface-following coordinate systems

Jenkins

2007

J Ocean Univ. China

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Surface waves comprise an important aspect of the interaction of the atmosphere and the ocean, so a dynamically consistent framework for modelling atmosphere-ocean interaction must take account of surface waves, either implicitly or explicitly. In order to calculate the effect of wind forcing on waves and currents, and vice versa, it is necessary to employ a consistent formulation of the energy and momentum balance within the airflow, wave field, and water column. It is very advantageous to apply surface-following coordinate systems, whereby the steep gradients in mean flow properties near the air-water interface in the cross-interface direction may be resolved over distances which are much smaller than the height of the waves themselves. We may account for the waves explicitly by employing a numerical spectral wave model, and applying a suitable theory of wave-mean flow interaction. If the mean flow is small compared with the wave phase speed, perturbation expansions of the hydrodynamic equations in a Lagrangian or generalized Lagrangian mean framework are useful: for stronger flows, such as for wind blowing over waves, the presence of critical levels where the mean flow velocity is equal to the wave phase speed necessitates the application of more general types of surface-following coordinate system. The interaction of the flow of air and water and associated differences in temperature and the concentration of various substances (such as gas species) gives rise to a complex boundary-layer structure at a wide range of vertical scales, from the sub-millimetre scales of gaseous diffusion, to several tens of metres for the turbulent Ekman layer. The balance of momentum, heat, and mass is also affected significantly by breaking waves, which act to increase the effective area of the surface for mass transfer, and increase turbulent diffusive fluxes via the conversion of wave energy to turbulent kinetic energy.

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Coupled sea surface‐atmosphere model: 2. Spectrum of short wind waves

Cited by 152 publications

References 35 publications

Modeling the SAR Signature of Nonlinear Internal Waves

Modeling the SAR Signature of Nonlinear Internal Waves

An Improved Spectrum Model for Sea Surface Radar Backscattering at L-Band

Interaction of waves, surface currents, and turbulence: the application of surface-following coordinate systems

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