Improved statistical prediction of surface currents based on historic HF-radar observations
Paduan, Jeffrey D.
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Accurate short-term prediction of surface currents can improve efficiency of search-and-rescue operations, oil-spill response, and marine operations. We developed a linear statistical model for predicting surface currents (up to 48 hours in the future) based on a short time-history of past HF-radar observations (past 48 hours) and an optional forecast of surface winds. Our model used empirical orthogonal functions (EOFs) to capture spatial correlations in the HF-radar data and used a linear autoregression model to predict the temporal dynamics of the EOF coefficients. We tested the developed statistical model using historical observations of surface currents in Monterey Bay, California. The predicted particle trajectories separated from particles advected with HF-radar data at a rate of 4.4 km/day, which represents an improvement over the existing statistical model (drifter separation of 5.5 km/day). We found that the minimal length of the HF-radar data required to train an accurate statistical model was between one and two years, depending on the accuracy desired.
The 1st ORCA, S3-1
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