Space–time regression and interpolation of metocean measurements: A focus on satellite data for the offshore energy sector

Ocean phenomena are characterised by a degree of complexity that prevents their effective modelling and simulation, making it necessary to rely on measuring their properties for monitoring and assessments. For these phenomena, obtaining a complete and exhaustive set of measurements is not trivial. Localised information on wind and wave conditions is of paramount importance for the proper design of offshore structures and offshore renewable energy plants. In such cases, a possible way to artificially extend the availability of information is by using regression and interpolation techniques. This review paper formalises the regression and interpolation problems with a focus on the offshore energy sector. It then presents the most common in-situ metocean measuring devices adopted in the offshore applications, followed by a description of the usage of satellites for performing measurements in this field. Subsequently, a brief description of the most adopted regression and interpolation techniques in the sector is provided. We review the state-of-the-art adoption of regression and interpolation techniques for extending measurements of such complex systems, focusing on the application of satellite measurements in the offshore renewable energy sector. The studies are categorised based on the family of regression and interpolation techniques applied, with each category further analysed, compared, and critically assessed. The main findings are reported, highlighting the adoption of machine learning techniques and the combinations of different regression and interpolation methods adopted to tackle the problems related to offshore energy sector resource assessment.