Wind Speed Prediction Using Sentinel-1 OCN Products

Abstract

Assessment of wind speed is inevitable for new offshore wind farm development. The location identification of offshore wind energy plant requires careful planning and analysis of various factors, such as the distance from the shore, wind speed, weather condition, and the water depth (bathymetry). In situ measurements often pose limitations in determining these factors. Alternatively, remote sensing technologies may be employed in setting up offshore wind farms. This paper presents a satellite based methodology for retrieval and forecasting of wind speed in the Kakinada coast of Andhra Pradesh. For this, the level-2 Ocean (OCN) product is used from the Copernicus satellite (Sentinel-1) that contains wind retrieval information. Based on the Global Wind Atlas (GWA) map, several hotspots are identified and the wind-speed time series data (2017–2021) is subsequently obtained for the study site. In total, 126 datasets are retrieved for the purpose of wind-speed prediction using the time-series ARIMA model. The results highlight the usefulness of the proposed technique.