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. In many coastal areas there is an increasing number and varietyof observation data available, which are often very heterogeneous in their temporal andspatial sampling characteristics. With the advent of new systems, like the radaraltimeter on board the Sentinel-3A satellite, a lot of questions arise concerning theaccuracy and added value of different instruments and numerical models. Quantification oferrors is a key factor for applications, like data assimilation and forecast improvement.In the past, the triple collocation method to estimate systematic and stochasticerrors of measurements and numerical models was successfully applied to different datasets.This method relies on the assumption that three independent data sets provide estimates of the same quantity.In coastal areas with strong gradients even small distances between measurements can leadto larger differences and this assumption can become critical. In this study thetriple collocation method is extended in different ways with the specificproblems of the coast in mind. In addition to nearest-neighbour approximations consideredso far, the presented method allows for use of a large variety of interpolationapproaches to take spatial variations in the observed area into account. Observation andnumerical model errors can therefore be estimated, even if the distance between thedifferent data sources is too large to assume that they measure the same quantity. If thenumber of observations is sufficient, the method can also be used to estimate errorcorrelations between certain data source components. As a second novelty, an estimatorfor the uncertainty in the derived observation errors is derived as a function of thecovariance matrices of the input data and the number of available samples. In the first step, the method is assessed using synthetic observations and Monte Carlosimulations. The technique is then applied to a data set of Sentinel-3A altimetermeasurements, in situ wave observations, and numerical wave model data with a focus onthe North Sea. Stochastic observation errors for the significant wave height, as well asbias and calibration errors, are derived for the model and the altimeter. The analysisindicates a slight overestimation of altimeter wave heights, which become more pronouncedat higher seastates. The smallest stochastic errors are found for the in situ measurements. Different observation geometries of in situ data and altimeter tracks are furthermoreanalysed, considering 1-D and 2-D interpolation approaches. For example, the geometry ofan altimeter track passing between two in situ wave instruments is considered with modeldata being available at the in situ locations. It is shown that for a sufficiently largesample, the errors of all data sources, as well as the error correlations of the model,can be estimated with the new method.

A multi-collocation method for coastal zone observations with applications to Sentinel-3A altimeter wave height data