Abstract
Abstract. The use of high-frequency radar (HFR) data is increasing worldwide for different applications in the field of operational oceanography and data assimilation, as it provides real-time coastal surface currents at high temporal and spatial resolution. In this work, a Lagrangian-based, empirical, real-time, short-term prediction (L-STP) system is presented in order to provide short-term forecasts of up to 48 h of ocean currents. The method is based on finding historical analogs of Lagrangian trajectories obtained from HFR surface currents. Then, assuming that the present state will follow the same temporal evolution as the historical analog, we perform the forecast. The method is applied to two HFR systems covering two areas with different dynamical characteristics: the southeast Bay of Biscay and the central Red Sea. A comparison of the L-STP methodology with predictions based on persistence and reference fields is performed in order to quantify the error introduced by this approach. Furthermore, a sensitivity analysis has been conducted to determine the limit of applicability of the methodology regarding the temporal horizon of Lagrangian prediction. A real-time skill score has been developed using the results of this analysis, which allows for the identification of periods when the short-term prediction performance is more likely to be low, and persistence can be used as a better predictor for the future currents.
Highlights
The coastal zone is under increasing human pressure
Given the motivation described above, and developed partially within the framework of the JERICO- project, we present a Lagrangian-based short-term prediction (L-STP on) methodology using existing high-frequency radar (HFR) datasets to be applied to surface current real-time observations
It is a visual representation of the (a) target trajectories, (b) the selected analog, (c) truth trajectories during the 48 h from the target period, and (d) the L-STP trajectories provided by the method (48 h from the analog)
Summary
The coastal zone is under increasing human pressure. During recent decades coastal seas have been experiencing intensified activity for recreation, transport, fisheries, and marinerelated energy production, which, in many cases, results in serious damage to coastal marine ecosystems. A better understanding of the dynamical processes responsible for the surface oceanic transport is a prerequisite for the efficient management of the coastal ocean. Coastal processes are responsible for the transport and fate of multi-source pollutants like plastics, nutrients, jellyfish, and harmful algal blooms. Improving the capacity of monitoring and forecasting the coastal area is key for the integrated assessment of the marine ecosystem. This requirement is driving the setup of a growing number of multi-platform operational observatories designed for continuous monitoring of the coastal ocean from international or national (e.g., US IOOS, EU EOOS, Australian IMOS) to local scales. Due to the need for forecasting applications for response to emergency situations such as oil spills or search-and-rescue operations, many of the existing operational observatories are linked with operational ocean forecasting models with or without data assimilation (e.g., MARACOOS, NOAA Global Real-Time Ocean Forecast System, COPERNICUS Marine Environment Monitoring System)
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