Detailed observations of the air flow velocity, pressure and reynolds stresses above water waves in a wave flume are presented. The static pressure fluctuations induced by the waves are observed following a new procedure that eliminates acoustical contamination by the wave maker. The measurements are analysed by comparing them with numerical simulations of the air flow over waves. In these numerical simulations the sensitivity to the choice of turbulence closure is studied. We considered both first-order turbulence closure schemes based on the eddy viscosity concept, and a second-order reynolds stress model. the comparison shows that turbulence closure schemes based on the eddy viscosity concept overestimate the modulation of the reynolds stress in a significant part of the vertical domain. When an eddy viscosity closure is used, the overestimated modulation of the reynolds stress gives a significant contribution to the wave growth rate. Our results confirm the conclusions belcher & hunt reached on the basis of the rapid distortion theory. The ratio of the wind speed to the phase speed of the paddle wave in the experiment varies between 3 and 6. The observed amplitudes of the velocity and pressure perturbation are in excellent agreement with the simulations. Comparison of the observed phases of the pressure and velocity perturbations shows that the numerical model underpredicts the downwind phase shift of the undulating flow. The sheltering coefficients for the flow over hills and the growth rates of waves that are slow compared to the wind calculated with the reynolds stress model are in excellent agreement with the analytical model of belcher & hunt. Extending the calculations to fast waves, we find that the energy flux to waves travelling almost as fast as the wind is increased on going from the mixing length turbulence closure to the reynolds stress model

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Abstract. A new wave retrieval method for the ERS synthetic aperture radar(SAR)wave mode is presented. The new algorithm, named semiparametric retrieval algorithm (SPRA), uses the full nonlinear mapping relations as proposed by Hasselmann and Hasselmann [1991]. It differs from previous retrieval algorithms in that it does not require a priori information on the sea state. Instead, it combines the observed SAR spectrum with the collocated wind vector from the ERS scatterometer to make an estimate of the wind sea spectrum. The residual signal in the SAR spectrum is interpreted as swell. The method has been validated by collocating over 5 years of SAR wave mode observations with spectral buoy measurements at 11 locations on the open ocean. For wave components longer than 225 m, the standard deviation between the retrieved spectra and buoy observations is 0.41 m, which corresponds to a relative RMS error of 29%. About 10% of the observed SAR spectra were rejected, in particular in light wind conditions when nonwave features such as those caused by slicks dominated the imagette. The bias and scatter in the results obtained under light wind conditions could be reduced by introducing a wind-dependent tilt modulation. This wind-dependent tilt formulation is derived from the empirical CMOD4 relation between the wind vector, the incidence angle, and the radar backscatter for the ERS scatterometer. BackgroundIn this paper we present a method that extracts spectral wave data from ERS synthetic aperture radar (SAR) observations. The ERS-1 and ERS-2 satellites (launched in 1991 and 1995, respectively) have gathered a few million SAR spectra over the open ocean. The retrieval algorithm described here was developed to exploit this unique data set, and use it to augment the description of the wave climate in remote areas.The way in which an ocean wave spectrum is mapped onto a SAR spectrum is reasonably well understood. The method described in this paper does not require a priori knowledge of the sea state to retrieve an ocean wave spectrum from a SAR observation. Instead, the fact that on the ERS satellites the scatterometer is operated simultaneously with the SAR wave mode is ex- Hasselmann and

The impact of sea waves on sensible heat and momentum fluxes is described. The approach is based on the conservation of heat and momentum in the marine atmospheric surface layer. The experimental fact that the drag coefficient above the sea increases considerably with increasing wind speed, while the exchange coefficient for sensible heat (Stanton number) remains virtually independent of wind speed, is explained by a different balance of the turbulent and the wave-induced parts in the total fluxes of momentum and sensible heat.Organised motions induced by waves support the wave-induced stress which dominates the surface momentum flux. These organised motions do not contribute to the vertical flux of heat. The heat flux above waves is determined, in part, by the influence of waves upon the turbulence diffusivity.The turbulence diffusivity is altered by waves in an indirect way. The wave-induced stress dominates the surface flux and decays rapidly with height. Therefore the turbulent stress above waves is no longer constant with height. That changes the balance of the turbulent kinetic energy and of the dissipation rate and, hence the diffusivity.The dependence of the exchange coefficient for heat on wind speed is usually parameterized in terms of a constant Stanton number. However, an increase of the exchange coefficient with wind speed is not ruled out by field measurements and could be parametrized in terms of a constant temperature roughness length. Because of the large scatter, field data do not allow us to establish the actual dependence. The exchange coefficient for sensible heat, calculated from the model, is virtually independent of wind speed in the range of 3-10 ms-' . For wind speeds above 10 ms-' an increase of 10% is obtained, which is smaller than that following from the 'constant roughness length' parameterization.

Abstract. Wave spectra that are retrieved from ERS-1/2 synthetic aperture radar (SAR) wave mode observations with two different algorithms are validated against 6 years of buoy observations. The Max-Planck Institut fiir Meteorologie (MPIM) algorithm, which runs operationally at the European Centre for Medium-Range Weather Forecasts (ECMWF), is found to deteriorate the quality of the WAM spectrum which is used as a first guess. The Semi-Parametric Retrieval Algorithm (SPRA) does not use a first-guess spectrum. For wavelengths which are observed by the SAR, it has a skill comparable to WAM. Several causes for the poor performance of the MPIM scheme are suggested. First, despite the fact that the SAR generally does not resolve the wind sea peak, the MPIM scheme allows for independent adjustment of its energy and peak frequency. Second, by using the quasi-linear approximation in the inversion, the scheme is inclined to interpret the SAR signal at low wave numbers as swell, whereas often it is generated by waves at higher wave numbers via nonlinearities in the SAR mapping. Third, the MPIM scheme is not able to adjust the spectral width of wave systems. The SPRA scheme retrieves swell information only up to a 180 ø directional ambiguity, and the SPRA retrievals often contain a spectral gap between the shortest waves observed by the SAR and the parameterized wind sea. In conclusion, the retrieval scheme performing best is the SPRA scheme, which has an accuracy comparable to WAM model output for the longer-swell waves.

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This report presents the results of a twelve month study initiated and supported by ESA on SAR imaging of the ocean surface. It was jointly conducted by the Nansen Environmental and Remote Sensing Center, ARGOSS and the Institute of Oceanography of the University of Hamburg. The objective of the study has been to intercompare and improve SAR retrieval algorithms and interaction models for upper ocean and atmospheric boundary layer applications, in particular for coastal oceans. As such the study is expected to enhance the usefulness of marine SAR imagery for research and practical applications. The study consists of two main phases, namely: a) review of existing algorithms and models; and b) intercomparison and assessment of the models using available data set