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, VS. 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.

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It has been shown before, and it is intuitively evident, that in a Significant Wave Height (SWH) time series, the longer the sampling interval, the lower is the number of events which are above a given threshold value. As a consequence, the use of data with a low time resolution (such as a 3 h sampling, for instance) causes a considerable undervaluation of the extreme SWH values for a given return time RT. In this paper an example of such a bias is provided, and a method is suggested to estimate it on a regional basis. Results may help to improve the use of historical wave meters data which were often collected with a low time resolution, and may also provide a tool to improve the application of Numerical Meteo-Wave models to the evaluation of extremes.

Calculating the significant wave height (SWH) in a given location as a function of the return time is an essential tool of coastal and ocean engineering; such a calculation can be carried out by making use of the now widely available weather and wave model chains, which often lead to underestimating the results, or by means of in situ experimental data (mostly, wave buoys), which are only available in a limited number of sites. A procedure is hereby tested whereby the curves of extreme SWH as a function of the return time deriving from model data are integrated with the similar curves computed from buoy data. A considerable improvement in accuracy is gained by making use of this integrated procedure in all locations where buoy data series are not available or are not long enough for a correct estimation. A useful and general design tool has therefore been provided to derive the extreme value SWH for any point in a given area.data raises however various issues: apart from the obvious problem of reliability of the model chains of both the atmospheric and the sea wave parts, an important aspect is the way through which ground truth wave data are assimilated into the analysis. Most of the assimilation procedures are carried out with satellite altimeter data, which are scattered in time (at many hours' intervals) and wide apart in space (tens or hundreds of kilometers), so extreme SWH values may often be missed. It is also worth noting that the sampling time of the models, i.e., the time interval at which data are stored and released, is often higher than the standard sampling time of buoys, thus causing a negative bias on the estimated extreme values [1][2][3].In order to overcome these problems, an integrated procedure [4] was been proposed by some of the authors of the present paper whereby the curves of extreme SWH as a function of the return time T R (in the following: SWH(T R )) deriving from synthetic data are compared and calibrated with the similar curves computed from buoy data in different locations. This provides a way of deriving SWH(T R ) curves for sites where no experimental data are available.The present paper presents an extension of the same technique and provides an experimental authentication of the methodology based on a new large set of reliable data along the coasts of the USA.The determination of the probability of extreme SWH is one of the main problems of coastal, offshore, and marine engineering, so that the relevant literature is not only extensive, but also increasing with time as the technology improves and the requirements become more stringent. Therefore, in the following, only contributions which are connected to the aims of this paper will be considered.All the procedures are substantially based on fitting Extreme Value Probability Distributions (in the following: EVPD) to buoy recorded time series of SWH; references on the general problem date back to many years ago [5], however Goda's textbook [6] is still the most common reference for maritime engineering, even though many authors...