Gap-Filling Sentinel-1 Offshore Wind Speed Image Time Series Using Multiple-Point Geostatistical Simulation and Reanalysis Data

Offshore wind is expected to play a key role in future energy systems. Wind resource studies often call for long-term and spatially consistent datasets assess the potential. Despite vast amount of available data sources, no current means can provide relevant sub-daily information at fine spatial scale (~1 km). Synthetic aperture radar (SAR) delivers field estimates over ocean resolution but suffers from partial coverage irregular revisit times. Physical model outputs, which are basis reanalysis products, be queried any time step lack fine-scale variability. To combine advantages both, we use framework multiple-point geostatistics realistically reconstruct speed patterns instances satellite absent. fine-resolution images generated conditioned coregistered regional coarser scale. Available simultaneous sources used as training generate synthetic image series. The latter then evaluated via cross validation statistical comparison against reference data. Multiple realizations also uncertainty associated with simulation outputs. Results show that proposed methodology reproduce spatiotemporal variability while honoring coarse thus filling gaps space time.