Marine X-band radar based systems are well tested to provide information about sea state and bathymetry. It is also well known that complex geometries and non-uniform bathymetries provide a much bigger challenge than offshore scenarios. In order to tackle this issue a retrieval method is proposed, based on spatial partitioning of the data and the application of the Normalized Scalar Product (NSP), which is an innovative procedure for the joint estimation of bathymetry and surface currents. The strategy is then applied to radar data acquired around a harbour entrance, and results show that the reconstructed bathymetry compares well with ground truth data obtained by an echo-sounder campaign, thus proving the reliability of the whole procedure. The spectrum thus retrieved is then analysed to show the evidence of reflected waves from the harbour jetties, as confirmed by chain of hydrodynamic models of the sea wave field. The possibility of using a land based radar to reveal sea wave reflection is entirely new and may open up new operational applications of the system.

Estimating the extreme values of significant wave height (H_S), generally described by the H_S return period T_R function H_S(T_R) and by its confidence intervals, is a necessity in many branches of coastal science and engineering. The availability of indirect wave data generated by global and regional wind and wave model chains have brought radical changes to the estimation procedures of such probability distribution—weather and wave modeling systems are routinely run all over the world, and H_S time series for each grid point are produced and published after assimilation (analysis) of the ground truth. However, while the sources of such indirect data are numerous, and generally of good quality, many aspects of their procedures are hidden to the users, who cannot evaluate the reliability and the limits of the H_S(T_R) deriving from such data. In order to provide a simple engineering tool to evaluate the probability of extreme sea-states as well as the quality of such estimates, we propose here a procedure based on integrating H_S time series generated by model chains with those recorded by wave buoys in the same area.

The orbital velocity of sea wave particles affects the value of sea surface parameters as measured by radar Doppler altimeters (also known as delay Doppler altimeter (DDA)). In DDA systems, the along-track resolution is attained by algorithms that take into account the Doppler shift induced by the component along the Earth/antenna direction of the satellite velocity, V_S. Since the vertical component of the wave particle orbital velocity also induces an additional Doppler effect (in the following R-effect), an error arises on the positioning of the target on the sea surface. A numerical investigation shows that when the wavelength of sea waves is of the same order of magnitude of the altimeter resolution, the shape of the waveform might be significantly influenced by the R-effect. The phenomenon can be particularly important for the monitoring of long swells, such as those that often take place in the oceans.

The determination of wave height by active satellite remote sensing, be it Synthetic Aperture Radar (SAR) or altimeter, has been a common practice for many years and is now imbedded on many meteorological and oceanographic forecasting systems. Despite their differences, all active sensors are based on the measurement of the Normalized Radar Cross Section (NRCS) of the sea surface, i.e., of its backscattering properties, which in turn depend on the wind velocity. At small and moderate wind speeds, the main mechanism is the formation of ripples (small scale waves); at higher speeds, whitecaps appear, and foam starts playing an essential role in determining NRCS. In the past few years much research effort has gone into clarifying these effects, thus improving the general quality of the measurements. Little work, however, has been devoted so far to consider the vertical spatial variation of backscattering properties, and in particular of the floating foam, over the sea surface. As it is shown in the present paper, the shape of the backscattered electromagnetic impulse in radar altimeters depends on the spatial distribution of foam over the water height in the sea waves and therefore the performance of these instruments in determining Significant Wave Height (H_s) and Sea Surface Level (SSL) is strongly affected by this effect. This work tackles these problems by making use of specially implemented numerical algorithms to simulate both sea surface processes and radar altimeter techniques. Results show that some causes of errors can be better understood and eventually corrected: in particular, the paper deals with the reconstruction of the electromagnetic Sea State Bias (SSB), the well known altimeter ranging error due to the presence of ocean waves on the sea surface.

This paper reports on an investigation on the influence of waves on the sea surface height error, ${\mathsf{\sigma }}_{\mathrm{h}}$, as measured by Delay Doppler satellite altimetry (DDA). CryoSat-2 altimeter sea surface height (SSH) data in the North Sea, processed in both DDA and pseudo low resolution mode (PLRM), are correlated with European Centre for Medium-Range Weather Forecasts (ECMWF) co-located sea state data. We find a small, but consistent correlation between the 1 Hz standard deviation, ${\mathsf{\sigma }}_{\mathrm{h}}$, of the 20 Hz altimeter SSH and the ECMWF total significant wave height, SWH_t. The same analysis carried out between ${\mathsf{\sigma }}_{\mathrm{h}}$ and the swell component of the wave spectrum shows a smaller correlation. In contrast, the correlation between the PLRM ${\mathsf{\sigma }}_{\mathrm{h}}$ and any component of the SWH spectrum has not been found to be significant. To provide an explanation of these results, the aliasing effect caused by the interaction between the sea wavelength and the altimeter resolution has been considered; a simple model has, therefore, been produced to simulate the dependence of the aliasing-derived, ${\mathsf{\sigma }}_{\mathrm{A}}$, on the sea wavelength. The alias/wavelength curve obtained helps to explain why—at least for the relatively low wavelength sea data considered—the wave direction and its wavelength have little or no influence on ${\mathsf{\sigma }}_{\mathrm{h}}$.