Ocean Surface Current Uncertainty Quantification via Drift Modeling and Spaceborne Retrieved Radial Velocities

Ocean surface current is an essential ocean state variable in operational oceanography. They govern the movement of pollutants, microorganisms, heat, and salt across the ocean. Their dynamics shape marine ecosystems and influence climate patterns. Monitoring and accurately predicting ocean surface currents are essential for managing offshore pollution and its potential impacts on the shoreline, safeguarding marine biodiversity and ensuring maritime safety. Ocean and atmospheric models are simplified representations of a highly complex, multi-scale flow system where approximations are unavoidable. Combined with the limited and inexact nature of available observations, predictions inherently present a degree of uncertainty. Ensemble modeling addresses these uncertainties by providing multiple future outcomes rather than a single solution. This approach helps quantify the likelihood of specific events, offering a more probabilistic understanding of the ocean behavior and enhancing decision-making in emergency situations, e.g, oil spills. Although widely employed and investigated by the atmospheric community, operational ensemble prediction systems are still emerging in oceanographic forecast centers. In this thesis, we evaluate how ocean and atmospheric ensemble operational prediction systems address ocean surface current uncertainties through short-term drift modeling and ocean current retrieval using satellite observations. The first part of this work provides background information about ocean dynamics as a multi-scale problem, modeling, remote sensing, and uncertainty. The second part presents the three research papers produced during the Ph.D. project. We introduce novel approaches to analyzing ensemble performance, with metrics specifically tailored for trajectory modeling, encompassing both delineated oil slicks and drifter trajectories. Our research sheds new light on the effects of horizontal resolution and wind forcing on short-term oil slick drift prediction using operational ensemble models. Furthermore, we present the first estimates of how wind field uncertainty affects ocean radial velocities, using remote sensing as the source of ocean current data. By bridging remote sensing, numerical modeling, and quantitative ensemble performance metrics, the main results of the three research articles in this compendium showed that (Paper I) wind forcing has little impact on short-term trajectory prediction spread, and lower resolution ensemble models pro