Trajectory prediction using HF radar surface currents: Monte Carlo simulations of prediction uncertainties

Abstract

[1] An important aspect of particle trajectory modeling in the ocean is the assessment of the uncertainty in the final particle position. Monte Carlo particle trajectory simulations using surface currents derived from standard-range and long-range CODAR HF radar systems were performed using random-walk and random-flight models of the unresolved velocities. Velocity statistics for these models were derived from the covariance functions of differences between CODAR and drifter estimates of surface currents. Comparison of predicted trajectories and drifter tracks demonstrate that these predictions are superior to assuming the drifters stay at their initial position. Vertical shear between the effective depth of long-range CODAR measurements ($2.4 m) and that of drifters (0.65 m) causes the drifters to move more rapidly downwind than predicted. This bias is absent when standard-range CODAR currents (effective depth $0.5 m) are used, implying that drifter leeway is not the cause of the bias. Particle trajectories were computed using CODAR data and the random-flight model for 24-hour intervals using a Monte Carlo approach to determine the 95% confidence interval of position predictions. Between 80% and 90% of real drifters were located within the predicted confidence interval, in reasonable agreement with the expected 95% success rate. In contrast, predictions using the random-walk approach proved inconsistent with observations unless the diffusion coefficient was increased to approximately the random-flight value. The consistency of the random-flight uncertainty estimates and drifter data supports the use of our methodology for estimating model parameters from drifter-CODAR velocity differences.

Evaluation of two algorithms for a network of coastal HF radars in the Mid-Atlantic Bight

“…The random flight turbulent parameters that we used to determine u′ are shown in Table 1. The parameters are consistent with those calculated by Ullman et al 2006 and used in the present application in SAROPS by the USCG. At each hour of each simulation, we calculated the location of the centroid of the particle cluster and the 95th percentile confidence region (Fig.…”

“…The model used to determine u′ in this study was based on a random flight model (Ullman et al 2006;Griffa 1996). The random flight turbulent parameters that we used to determine u′ are shown in Table 1.…”

“…2). This confidence region was calculated by rank ordering the spatial bins, by frequency, of a two-dimensional frequency histogram of the final simulated particle locations (Ullman et al 2006). In addition to the drifter trajectories, we also calculated the mean separation and 95th percentile separation of the actual drifter position relative to the initial drifter position for each 48-h segment.…”

HF Radar Activity in European Coastal Seas: Next Steps toward a Pan-European HF Radar Network

“…In addition to a lot of scientific work related to the study and characterization of physical ocean processes, there are several other applications of HFR in research and marine management. Some of the examples provided in Paduan and Washburn (2013) include direct applications of HFR data to search and rescue (SAR) (Ullman et al, 2006) or oil-spill mitigation (e.g., Abascal et al, 2009;Frolov et al, 2012), marine traffic information (Breivik and Saetra, 2001), water quality assessment (e.g., Kim et al, 2009) and biological oceanography (e.g., Nishimoto and Washburn, 2002;Brzezinski and Washburn, 2011).…”

The Crossover Time as an Evaluation of Ocean Models Against Persistence

“…For quantifying improvements in ocean current forecasts, particularly the Lagrangian predictability, persistence has been typically taken as either a constant last known position (Barron et al, 2007;Kuang et al, 2012;Schmidt & Gangopadhyay, 2013;Ullman et al, 2006) or constant last known velocity (Paldor et al, 2004;Rixen & Ferreira-Coelho, 2007) of a surface-drifting buoy held constant. Surface-drifting buoys provide unique observations of the ocean currents capturing various scales of motion (Lumpkin et al, 2017).…”