Skill Assessment of HF Radar–Derived Products for Lagrangian Simulations in the Bay of Biscay

Abstract

Since January 2009, two long-range high-frequency (HF) radar systems have been collecting hourly highspatial-resolution surface current data in the southeastern corner of the Bay of Biscay. The temporal resolution of the HF radar surface currents permits simulating drifter trajectories with the same time step as that of real drifters deployed in the region in 2009. The main goal of this work is to compare real drifter trajectories with trajectories computed from HF radar currents obtained using different methods, including forecast currents. Open-boundary modal analysis (OMA) is applied to the radar radial velocities and then a linear autoregressive model on the empirical orthogonal function (EOF) decomposition of an historical data series is used to forecast OMA currents. Additionally, the accuracy of the forecast method in terms of the spatial and temporal distribution of the Lagrangian distances between observations and forecasts is investigated for a 4-yr period . The skills of the different HF radar products are evaluated within a 48-h window. The mean distances between real trajectories and their radar-derived counterparts range from 4 to 5 km for real-time and forecast currents after 12 hours of simulations. The forecast model improves persistence (i.e., the simulations obtained by using the last available OMA fields as a constant variable) after 6 hours of simulation and improves the estimation of trajectories up to 28% after 48 hours. The performance of the forecast is observed to be variable in space and time, related to the different ocean processes governing the local ocean circulation.

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

“…Since the applications related to the management of the marine environment and emergencies at sea require accurate prediction of Lagrangian trajectories, several studies have assessed the effectiveness of trajectory predictions using currents derived from HFR (Menna et al, 2007;Uttieri et al, 2011;Solabarrieta et al, 2016). An example of calculation of Lagrangian trajectories using HFR data is shown in Figure 5.…”

“…Orfila et al (2015) used a Genetic Algorithm to identify mathematical expressions that best forecast the time series of statistically significant EOFs. Solabarrieta et al (2016) applied the linear autoregressive models described in Frolov et al (2012) to perform an analysis of their spatio-temporal performances in a multi-year experiment in the Southeastern Bay of Biscay. A neural network based approach to obtain short term forecasts for currents and water levels from HFR data was presented in Wahle and Stanev (2011).…”

“…In Europe, the number of systems is growing with over 50 HFRs currently deployed and a number in the planning stage. Nowadays, these systems are integrated in many European coastal observatories with proven potential for monitoring (e.g., Wyatt et al, 2006;Molcard et al, 2009;Berta et al, 2014b) and even providing short-term prediction of coastal currents (e.g., Orfila et al, 2015;Solabarrieta et al, 2016;Vilibić et al, 2016), and inputs for data assimilation and the validation and calibration of numerical ocean forecasting models, especially near the coast (e.g., Barth et al, 2008Barth et al, , 2011Marmain et al, 2014;Stanev et al, 2015;Iermano et al, 2016). The growing number of HFRs, the optimization of HFR operation against technical hitches and the need for complex data processing and analysis, highlight the urgent requirement to increase the coordination in the HFR community.…”

Short-Term Forecasting of Coastal Surface Currents Using High Frequency Radar Data and Artificial Neural Networks

“…Spatial coverage of surface currents measured by radars can reach around 200 km, depending on transmission frequencies. Coastal radar data have applications in many areas such marine renewable energy, oil-spill monitoring [43,44], data assimilation [3,[45][46][47], trajectory forecasts [48] and search and rescue [49].…”

The Crossover Time as an Evaluation of Ocean Models Against Persistence

“…Persistence has been extensively used in meteorology ( Van den Dool, 2007) but less in oceanography (Farrara et al, 2013;Oey et al, 2005;Rowe et al, 2016;Shriver et al, 2007;Solabarrietaa et al, 2016;Vandenbulcke et al, 2009) as a performance benchmark. 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.…”