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It is well known that look cross spectra processed from synthetic aperture radar (SAR) contain valuable information on ocean waves. With the launch of the European satellite ENVISAT, SAR look cross spectra (SLCS) have become available on an operational basis. Activities therefore exist at different European weather centres to use the data for assimilation into numerical wave models. Furthermore there is scientific interest in SLCS, e.g., concerning the estimation of the phase speed of ocean waves. For the estimation of ocean wave parameters, it is important to have information about the accuracy of SLCS. In this paper, errors of estimated SLCS due to SAR image speckle, spectral estimation errors, and image pattern decorrelation associated with ocean wave motion are analyzed. A probability model is proposed for the estimated SLCS based on the respective cross-spectrum coherence. The model is used to calculate signal-to-noise ratios and confidence limits for the SLCS phase and magnitude, as well as the real and imaginary part. The coherence is factored into a component describing look decorrelation due to SAR image speckle and a second factor describing the effect of sea surface motion. It is shown that the ocean-wave-dependent decorrelation can be simulated using existing nonlinear integral transforms for the look variance spectrum and the SLCS. The decorrelation effect associated with speckle noise is related to SAR system parameters, e.g., the spatial SAR resolution. The probability model is used to investigate the optimal choice of look processing parameters like the look separation time. A statistical analysis based on a global dataset of a reprocessed dataset of European Remote Sensing 2 satellite SLCS is presented confirming the applicability of the probability model. The implications of the results for the retrieval of two-dimensional wave spectra from SLCS are summarized. Possible future applications of the model like, for example, the investigation of the turbulent air flow over waves, are discussed. Index Terms--Coherence, cross spectrum, ocean waves, synthetic aperture radar (SAR).

A method is presented to derive two-dimensional sea surface elevation fields from complex synthetic aperture radar (SAR) data. Applied to spaceborne SAR data as acquired by European Remote Sensing 2 (ERS-2) or the Environmental Satellite (ENVISAT), the method allows to analyze the structure of ocean wave fields, e.g., wave grouping or individual wave heights on a global scale. The technique, thus, provides wave parameters not obtained with common SAR wave retrieval schemes, which are designed to estimate the 2-D wave spectrum, i.e., second-order statistical moments of the wave field. Estimates of sea surface elevation fields are obtained based on the existing theory of SAR ocean wave imaging, i.e., the modulation of the SAR image intensity due real aperture radar and motion-related effects. A power series expansion is derived for SAR intensity images that enables the analysis of nonlinear effects as well as to derive a quasi-linear approximation of the SAR imaging model in the spatial domain. A statistical analysis is performed based on a global dataset of 2-D wave spectra provided by the European Centre for Medium-Range Weather Forecast. Distributions are given for the relative error of the quasi-linear approximation in the spatial domain. It is shown that the error can be reduced by smoothing the SAR image in the azimuthal direction at the cost of lower resolution. Smoothed elevation fields are retrieved by the minimization of a cost function defined in the Fourier domain based on the quasi-linear approximation of the imaging process. A multilook technique is applied to infer the information on wave propagation directions, which is required because the SAR transfer function is non-Hermitian, i.e., the SAR image is not determined by the 'frozen' sea surface, but wave motion has a significant impact. The method is applied to simulated SAR images as well as to data acquired by ERS-2. The errors of the retrieved wave field due to image noise, uncertainties in the SAR imaging model, and bandwidth limitations are analyzed. In particular, the fact that the estimated elevation field is smoothed due to the finite system resolution and smearing effects associated with wave motion is discussed. A statistical test is proposed to check the homogeneity of the SAR image. The method makes sure that atmospheric effects are not misinterpreted as being caused by ocean waves. Index Terms--Individual waves, ocean waves, sea surface elevation, synthetic aperture radar (SAR).

The global availability of synthetic aperture radar (SAR) wave mode data from the European Remote Sensing (ERS) satellites ERS-1 and ERS-2, as well as ENVISAT, allows for the investigation of the wind field over the ocean on a global and continuous basis. For this purpose, 27 days of ERS-2 SAR wave mode data were processed, representing a total of 34310 imagettes of size 10 km x 5 km, available every 200 km along the satellite track. In this paper, two methods for retrieving wind speeds from SAR imagettes are presented and validated, showing the applicability of ENVISAT alike SAR wave mode data for global ocean wind retrieval. The first method is based on the well-tested empirical C-baud scatterometer (SCAT) models, which describe the dependency of the normalized radar cross section (NRCS) on wind speed and direction. To apply C-band models to SAR data, the NRCS needs to be accurately calibrated. This is performed by a new efficient method utilizing a subset of colocated measurements from ERS-2 SCAT and model winds from the European Centre for Medium-Range Weather Forecast (ECMWF). SAR wind speeds are computed from the calibrated imagettes and compared to the entire set of colocated ERS-2 SCAT and ECMWF model data. Comparison to ERS-2 SCAT winds result in a correlation of 0.95 with a bias of -0.0l [ms.sup.-1] and an rms error of 1.0 [ms.sup.-1] The second approach is based on neural networks (NNs), which allow the retrieval of wind speeds from uncalibrated SAR imagettes. NNs are trained using the mean intensity of ERS-2 SAR imagettes and colocated wind data from the ERS-2 SCAT and ECMWF model data. Validation of the NN-retrieved SAR wind speeds to ERS-2 SCAT and ECMWF model wind data result in a correlation of 0.96 with a bias of -0.04 [ms.sup.-1] and an rms error of 0.93 [ms.sup.-1]. Index Terms--Calibration, neural network, scatterometer, synthetic aperture radar (SAR), wind speed.

Extremes in temperatures not only directly affect the marine environment and ecosystems but also have indirect impacts on hydrodynamics and marine life. The role of heat wave events responsible for the occurrence and persistence of thermal stratification was analysed using a fully coupled hydrodynamic and wave model within the framework of the Geesthacht Coupled cOAstal model SysTem (GCOAST) for the North Sea. The model results were assessed against satellite reprocessed data and in situ observations from field campaigns and fixed MARNET stations. To quantify the degree of stratification, a potential energy anomaly over the water column was calculated. A linear correlation existed between the air temperatures and the potential energy anomaly in the North Sea excluding the Norwegian Trench and the area south of 54° N latitude. Contrary to the northern part of the North Sea, where the water column is stratified in the warming season each year, the southern North Sea is seasonally stratified in years when a heatwave occurs. The influences of heatwaves on the occurrence of summer stratifications in the southern North Sea are mainly in the form of two aspects, i.e., a rapid rise in sea surface temperature at the early stage of the heatwave period and a relatively higher water temperature during summer than the multiyear mean. Another factor that enhances the thermal stratification in summer is the memory of the water column to cold spells earlier in the year. Differences between the seasonally stratified northern North Sea and the heatwave-induced stratified southern North Sea were attributed to changes in water depth. [ABSTRACT FROM AUTHOR]

The recovery of offshore wind farm wakes in the German Bight was analyzed by a unique in situ data set, measured on‐board the research aircraft Dornier Do‐128 during the WIPAFF project in 2016 and 2017. These observations were used to validate a simple analytical wake recovery model in five case studies. The observed recovery rates were compared with the results of the mesoscale Weather Research and Forecasting (WRF) model. The airborne data show that the wake recovery can be described by an exponential function as expected by the analytical model and strengthens the hypothesis that the vertical downward moment flux has an important influence on the wake recovery. However, the predicted wake recovery rates (by the analytical model) do not always fully agree with the observations. Although, as a first‐order approximation, the model seems to perform well, further optimization has to be implemented to account for wind park layout, turbine induced turbulence, and horizontal momentum flux. WRF simulations reveal an exponential recovery, although the mesoscale model does not reproduce the correct atmospheric conditions for most of the cases. Therefore, the wake recovery rates estimated by WRF disagree with the measured data in most of the studied cases. [ABSTRACT FROM AUTHOR]