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

In many coastal areas there is an increasing number and variety of observation data available, which are often very heterogeneous in their temporal and spatial sampling characteristics. With the advent of new systems, like the radar altimeter on board the Sentinel-3A satellite, a lot of questions arise concerning the accuracy and added value of different instruments and numerical models. Quantification of errors is a key factor for applications, like data assimilation and forecast improvement. In the past, the triple collocation method to estimate systematic and stochastic errors of measurements and numerical models was successfully applied to different data sets. 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 lead to larger differences and this assumption can become critical. In this study the triple collocation method is extended in different ways with the specific problems of the coast in mind. In addition to nearest-neighbour approximations considered so far, the presented method allows for use of a large variety of interpolation approaches to take spatial variations in the observed area into account. Observation and numerical model errors can therefore be estimated, even if the distance between the different data sources is too large to assume that they measure the same quantity. If the number of observations is sufficient, the method can also be used to estimate error correlations between certain data source components. As a second novelty, an estimator for the uncertainty in the derived observation errors is derived as a function of the covariance matrices of the input data and the number of available samples. In the first step, the method is assessed using synthetic observations and Monte Carlo simulations. The technique is then applied to a data set of Sentinel-3A altimeter measurements, in situ wave observations, and numerical wave model data with a focus on the North Sea. Stochastic observation errors for the significant wave height, as well as bias and calibration errors, are derived for the model and the altimeter. The analysis indicates a slight overestimation of altimeter wave heights, which become more pronounced at higher sea states. The smallest stochastic errors are found for the in situ measurements. Different observation geometries of in situ data and altimeter tracks are furthermore analysed, considering 1-D and 2-D interpolation approaches. For example, the geometry of an altimeter track passing between two in situ wave instruments is considered with model data being available at the in situ locations. It is shown that for a sufficiently large sample, the errors of all data sources, as well as the error correlations of the model, can be estimated with the new method.</p

Investigation of inhomogeneity parameters of ERS-2 wave mode image

In order to classify different types of globally distributed synthetic aperture radar (SAR) wave mode images, which are acquired over the ocean for wind speed and sea state measurements, we develop a new scheme to differentiate images showing ocean wave, sea ice and surface slicks. A new classification parameter has been developed using 1535 SAR wave mode images to differentiate homogeneous and inhomoge-neous images. The new parameter is applied to two years of images. Comparison of the performance using the new parameter and inhomogeneity parameter (IH) defined in [1] are given. In the Arctic area the results of two parameters are compared to Special Sensor Microwave Imager (SSM/I) ice concentration data. The global distribution of inhomogeneous images is analyzed. Inhomegeneity in ice-free SAR images was found to be mainly due to low wind speed

Statistical analysis of ocean wave and wind parameters retrieved with an empirical SAR algorithum

A global dataset of two years (September 1998 to December 2000) of ERS SAR data was reprocessed to more than one million SAR imagettes. Met ocean Parameters like significant ocean wave height (H s), wind speed (U 10) and mean wave period (T m-10) are derived from the SAR images using a new empirical algorithm CWAVE [1]. The results are compared to collocated ERS altimeter data and in Situ measurements from NOAA buoys and observations taken onboard the vessel Polarstern. It is shown that the SAR derived H s is comparable in quality to altimeter measurements and can thus be used for real time assimilation

Synergy of wind wave model simulations and satellite observations during extreme events

In this study, the quality of wave data provided by the new Sentinel-3A satellite is evaluated and the sensitivity of the wave model to wind forcing is tested. We focus on coastal areas, where altimeter data are of lower quality and wave modelling is more complex than for the open ocean. In the first part of the study, the sensitivity of the wave model to wind forcing is evaluated using data with different temporal and spatial resolution, such as ERA-Interim and ERA5 reanalyses, the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis and short-range forecasts, German Weather Service (DWD) forecasts and regional atmospheric model simulations (coastDat). Numerical simulations show that the wave model forced using the ERA5 reanalyses and that forced using the ECMWF operational analysis/forecast demonstrate the best capability over the whole study period, as well as during extreme events. To further estimate the variance of the significant wave height of ensemble members for different wind forcings, especially during extreme events, an empirical orthogonal function (EOF) analysis is performed. In the second part of the study, the satellite data of Sentinel-3A, Jason-2 and CryoSat-2 are assessed in comparison with in situ measurements and spectral wave model (WAM) simulations. Intercomparisons between remote sensing and in situ observations demonstrate that the overall quality of the former is good over the North Sea and Baltic Sea throughout the study period, although the significant wave heights estimated based on satellite data tend to be greater than the in situ measurements by 7 to 26&thinsp;cm. The quality of all satellite data near the coastal area decreases; however, within 10&thinsp;km off the coast, Sentinel-3A performs better than the other two satellites. Analyses in which data from satellite tracks are separated in terms of onshore and offshore flights have been carried out. No substantial differences are found when comparing the statistics for onshore and offshore flights. Moreover, no substantial differences are found between satellite tracks under various metocean conditions. Furthermore, the satellite data quality does not depend on the wind direction relative to the flight direction. Thus, the quality of the data obtained by the new Sentinel-3A satellite over coastal areas is improved compared to that of older satellites.</p

Linear and Nonlinear Rogue Wave Statistics in the Presence of Random Currents

We review recent progress in modeling the probability distribution of wave heights in the deep ocean as a function of a small number of parameters describing the local sea state. Both linear and nonlinear mechanisms of rogue wave formation are considered. First, we show that when the average wave steepness is small and nonlinear wave effects are subleading, the wave height distribution is well explained by a single "freak index" parameter, which describes the strength of (linear) wave scattering by random currents relative to the angular spread of the incoming random sea. When the average steepness is large, the wave height distribution takes a very similar functional form, but the key variables determining the probability distribution are the steepness, and the angular and frequency spread of the incoming waves. Finally, even greater probability of extreme wave formation is predicted when linear and nonlinear effects are acting together.Comment: 25 pages, 12 figure