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

Airborne LiDAR Measurements of Sea Surface Properties in the German Bight

Sea surface measurements are mainly gathered using satellite altimeter, buoy, and platform measurements. Satellite measurements typically have a coarse spatial resolution and need recalibration in coastal regions, whereas point measurements of buoys only represent limited areas around the measurement point because of the complex coastal bathymetry. Wave models (WAM) are used to expand the sparse observations in space and time. As a part of the project WIndPArk far-field (WIPAFF), which focused on wakes behind offshore wind farms, extensive airborne light detection and ranging (LiDAR) measurements of ocean waves in the German Bight were performed for more than 90 h. The LiDAR data processed for significant wave height can be used to validate and improve WAM models for complex areas and fill the observation gap between satellite altimeter and point measurements. This creates a detailed picture of the sea surface for coastal engineering and environmental applications. After introducing the measurement techniques and the data situation, intercomparisons between the available airborne measurements, buoy data, and WAM model output are presented to provide an insight into the potential of airborne LiDAR measurements for wave characterization and wave model validation

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

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

Fit-for-Purpose Information for Offshore Wind Farming Applications—Part-I: Identification of Needs and Solutions

The rapid expansion of offshore wind farms (OWFs) in European seas is accompanied by many challenges, including efficient and safe operation and maintenance, environmental protection, and biodiversity conservation. Effective decision-making for industry and environmental agencies relies on timely, multi-disciplinary marine data to assess the current state and predict the future state of the marine system. Due to high connectivity in space (land–estuarial–coastal sea), socioeconomic (multi-sectoral and cross-board), and environmental and ecological processes in sea areas containing OWFs, marine observations should be fit for purpose in relation to multiple OWF applications. This study represents an effort to map the major observation requirements (Part-I), identify observation gaps, and recommend solutions to fill those gaps (Part-II) in order to address multi-dimension challenges for the OWF industry. In Part-I, six targeted areas are selected, including OWF operation and maintenance, protection of submarine cables, wake and lee effects, transport and security, contamination, and ecological impact assessments. For each application area, key information products are identified, and integrated modeling–monitoring solutions for generating the information products are proposed based on current state-of-the-art methods. The observation requirements for these solutions, in terms of variables and spatial and temporal sampling needs, are therefore identified.publishedVersio

Fit-for-Purpose Information for Offshore Wind Farming Applications—Part-II: Gap Analysis and Recommendations

Offshore wind energy installations in coastal areas have grown massively over the last decade. This development comes with a large number of technological, environmental, economic, and scientific challenges, which need to be addressed to make the use of offshore wind energy sustainable. One important component in these optimization activities is suitable information from observations and numerical models. The purpose of this study is to analyze the gaps that exist in the present monitoring systems and their respective integration with models. This paper is the second part of two manuscripts and uses results from the first part about the requirements for different application fields. The present solutions to provide measurements for the required information products are described for several European countries with growing offshore wind operations. The gaps are then identified and discussed in different contexts, like technology evolution, trans-European monitoring and modeling initiatives, legal aspects, and cooperation between industry and science. The monitoring gaps are further quantified in terms of missing observed quantities, spatial coverage, accuracy, and continuity. Strategies to fill the gaps are discussed, and respective recommendations are provided. The study shows that there are significant information deficiencies that need to be addressed to ensure the economical and environmentally friendly growth of the offshore wind farm sector. It was also found that many of these gaps are related to insufficient information about connectivities, e.g., concerning the interactions of wind farms from different countries or the coupling between physical and biological processes.publishedVersio

The role of heat wave events in the occurrence and persistence of thermal stratification in the southern North Sea

Temperature extremes not only directly affect the marine environment and ecosystems but also indirectly influence hydrodynamics and marine life. In this study, the role of heat wave events in the occurrence and persistence of thermal stratification was analysed by simulating the water temperature of the North Sea from 2011 to 2018 using a fully coupled hydrodynamic and wave model within the framework of the Geesthacht Coupled cOAstal model SysTem (GCOAST). The model results were assessed against reprocessed satellite data and in situ observations from field campaigns and fixed Marine Environmental Monitoring Network (MARNET) stations. To quantify the degree of stratification, the potential energy anomaly throughout the water column was calculated. The air temperatures and potential energy anomalies in the North Sea (excluding the Norwegian Trench and the area south of 54∘ N) were linearly correlated. Different from the northern North Sea, where the water column is stratified in the warm season each year, the southern North Sea is seasonally stratified in years when a heat wave occurs. The influences of heat waves on the occurrence of summer stratification 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 heat wave period and a 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 heat wave-induced stratified southern North Sea were ultimately attributed to changes in water depth