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.
Forecasting of Surface Currents via Correcting Wind Stress with Assimilation of High-Frequency Radar Data in a Three-Dimensional Model