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The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m × 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82 % and 84 % for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 ∘ C and 0 . 4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.

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a b s t r a c tOscillating water column (OWC) are devices for wave energy extraction equipped with turbines for energy conversion. The purpose of the present work is to study the thermodynamic of a real gas flow through the turbine and its differences with respect to the ideal gas hypothesis, with the final goal to be applied to OWC systems. The effect of moisture in the air chamber of the OWC entails variations on the atmospheric conditions near the turbine, modifying its performance and efficiency. In this work we study the influence of humid air in the performance of the turbine. Experimental work is carried out and a real gas model is asserted, in order to take a first approach to quantify the extent of influence of the airewater vapour mixture in the turbine performance. The application of a real gas model and the experimental study confirmed the deviations of the turbine performance from the expected values depending on flow rate, moisture and temperature.

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Intent: Explores integrating the use of cloud-based data and how scientists can access large volumes of diverse, current and authoritative data, addresses the problem of locating and using large amounts of scientific data. The Section "Architectures for Real-Time Data Management and Services for Observations" describes streaming data and an architecture for making it easy to gather data. Also see Johanson et al. (2016). Cloud-Based Management of Scientific Data-Storing Data in the Cloud Intent: Explores storing and managing data in the cloud. Addresses the problem of ever increasing data quantities with decreasing budgets for data management. Explores the ways scientific projects can meet data access and dissemination requirements such as the U.S. Public Access to Research Results (PARR) mandate (Holdren, 2013). The section on the NOAA Big Data Project and open data and archiving are examples of this pattern. Also see Meisinger et al. (2009). Computing Infrastructure for Scientific Research Intent: Explores the ways in which cloud computing, in the form of PaaS or IaaS could be used as part of a research program and for teaching. It addresses the need for larger computational capabilities, especially under constrained budgets. The modeling efforts described in later sections are examples of this pattern.