High-frequency Radar Currents Research Articles

Reducing the error in estimates of the Sunda Strait currents by blending HF radar currents with model results

The examination of currents by merging model results and radial velocity High-Frequency (HF) radar data has been undertaken in the Sunda Strait, which links Sumatera Island and Java Island, involving two coastal sites (Anyer and Labuan) and using the Ensemble Transform Kalman Filter (ETKF). Dependent validation involved the data used during model analysis while independent validation utilised observations from a different site. These validations are needed to obtain an optimal representativity error, which has the lowest averaged root mean square (RMS) value over time and is appropriate for all sites. Moreover, the optimal representativity error was evaluated using the relative error reduction and the associated skill score metric. The results show that the model analysis for both independent and dependent validation has better results than the model with no blending. Interestingly, independent validation has a smaller RMS value than the model with no blending, although it is still greater than the dependent validation. The best results were obtained from model analysis of all sites, with the representativity error value being 0.2704 m/s for Anyer and 0.4459 m/s for Labuan. However, they have a pattern in the RMS error over time series. It is necessary to consider the factor such as winds that would modulate the magnitude of radial velocity.

Gaps Filling in HF Radar Sea Surface Current Data Using Complex Empirical Orthogonal Functions

The knowledge of speed and direction of coastal ocean currents are critical for carrying out search and rescue at sea, to respond to the hazardous marine oil spills, to estimate the dispersal pattern of pollutants, to issue warnings against tsunamis and cyclones etc. Such operations need uninterrupted observational data of sea surface currents over wide area with high spatial and temporal resolution. The High frequency (HF) radars installed at the shore, sense the surface of ocean using electromagnetic waves, and provide the speed and direction of ocean currents over a specific spatial domain at high temporal resolution. However, the frequent gaps in the data are a concern for its usage. The gaps arise due to its dependence on the roughness of the sea other electromagnetic interferences from environment sources and due to data acquisition failures. Hence, it is necessary to fill the gaps for successful usage of HF radar data. The present work describes a method to fill the gaps in the HF radar data using complex empirical orthogonal functions (CEOF). Before filling the missing data, HF radar data is assessed with available observations. The CEOFs are calculated iteratively using singular value decomposition. The number of statistically significant CEOFs required to construct the missing HF radar currents data accurately along the Indian coast are obtained through a cross-validation procedure. The proposed method is first validated for various rates of synthetic data gaps created in synthetic data and in model derived cyclonic wind fields and comparisons are illustrated, the reconstructed data and the original data are in good match

Interoperability of Direction-Finding and Beam-Forming High-Frequency Radar Systems: An Example from the Australian High-Frequency Ocean Radar Network

Direction-finding SeaSonde (4.463 MHz; 5.2625 MHz) and phased-array WEllen RAdar WERA (9.33 MHz; 13.5 MHz) High-frequency radar (HFR) systems are routinely operated in Australia for scientific research, operational modeling, coastal monitoring, fisheries, and other applications. Coverage of WERA and SeaSonde HFRs in Western Australia overlap. Comparisons with subsurface currents show that both HFR types agree well with current meter records. Correlation (R), root-mean-squares differences (RMSDs), and mean bias (bias) for hourly-averaged radial currents range between R = (−0.03, 0.78), RMSD = (9.2, 30.3) cm/s, and bias = (−5.2, 5.2) cm/s for WERAs; and R = (0.1, 0.76), RMSD = (17.4, 33.6) cm/s, bias = (0.03, 0.36) cm/s for SeaSonde HFRs. Pointing errors (θ) are in the range θ = (1°, 21°) for SeaSonde HFRs, and θ = (3°, 8°) for WERA HFRs. For WERA HFR current components, comparison metrics are RU = (−0.12, 0.86), RMSDU = (12.3, 15.7) cm/s, biasU = (−5.1, −0.5) cm/s; and, RV = (0.61, 0.86), RMSDV = (15.4, 21.1) cm/s, and biasV = (−0.5, 9.6) cm/s for the zonal (u) and the meridional (v) components. Magnitude and phase angle for the vector correlation are ρ = (0.58, 0.86), φ = (−10°, 28°). Good match was found in a direct comparison of SeaSonde and WERA HFR currents in their overlap (ρ = (0.19, 0.59), φ = (−4°, +54°)). Comparison metrics at the mooring slightly decrease when SeaSonde HFR radials are combined with WERA HFR: scalar (vector) correlations for RU, V, (ρ) are in the range RU = (−0.20, 0.83), RV = (0.39, 0.79), ρ = (0.47, 0.72). When directly compared over the same grid, however, vectors from WERA HFR radials and vectors from merged SeaSonde–WERA show RU (RV) exceeding 0.9 (0.7) within the HFR grid. Despite the intrinsic differences between the two types of radars used here, findings show that different HFR genres can be successfully merged, thus increasing current mapping capability of the existing HFR networks, and minimising operational downtime, however at a likely cost of slightly decreased data quality.

Effect of small scale transport processes on phytoplankton distribution in coastal seas

Coastal ocean ecosystems are major contributors to the global biogeochemical cycles and biological productivity. Physical factors induced by the turbulent flow play a crucial role in regulating marine ecosystems. However, while large-scale open-ocean dynamics is well described by geostrophy, the role of multiscale transport processes in coastal regions is still poorly understood due to the lack of continuous high-resolution observations. Here, the influence of small-scale dynamics (O(3.5–25) km, i.e. spanning upper submesoscale and mesoscale processes) on surface phytoplankton derived from satellite chlorophyll-a (Chl-a) is studied using Lagrangian metrics computed from High-Frequency Radar currents. The combination of complementary Lagrangian diagnostics, including the Lagrangian divergence along fluid trajectories, provides an improved description of the 3D flow geometry which facilitates the interpretation of two non-exclusive physical mechanisms affecting phytoplankton dynamics and patchiness. Attracting small-scale fronts, unveiled by backwards Lagrangian Coherent Structures, are associated to negative divergence where particles and Chl-a standing stocks cluster. Filaments of positive divergence, representing large accumulated upward vertical velocities and suggesting accrued injection of subsurface nutrients, match areas with large Chl-a concentrations. Our findings demonstrate that an accurate characterization of small-scale transport processes is necessary to comprehend bio-physical interactions in coastal seas.

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

AbstractSince January 2009, two long-range high-frequency (HF) radar systems have been collecting hourly high-spatial-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 (2009–12). 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.

Comparison of HF radar measurements with Eulerian and Lagrangian surface currents

High-frequency (HF) radar-derived ocean currents are compared with in situ measurements to conclude if the radar observations include effects of surface waves that are of second order in the wave amplitude. Eulerian current measurements from a high-resolution acoustic Doppler current profiler and Lagrangian measurements from surface drifters are used as references. Directional wave spectra are obtained from a combination of pressure sensor data and a wave model. Our analysis shows that the wave-induced Stokes drift is not included in the HF radar-derived currents, that is, HF radars measure the Eulerian current. A disputed nonlinear correction to the phase velocity of surface gravity waves, which may affect HF radar signals, has a magnitude of about half the Stokes drift at the surface. In our case, this contribution by nonlinear dispersion would be smaller than the accuracy of the HF radar currents, hence no conclusion can be made. Finally, the analysis confirms that the HF radar data represent an exponentially weighted vertical average where the decay scale is proportional to the wavelength of the transmitted signal.

Constraining energetic slope currents through assimilation of high-frequency radar observations

Abstract. Assimilation of high-frequency (HF) radar current observations and CTD hydrography is performed with the 4D-Var analysis scheme implemented in the Regional Ocean Modeling System (ROMS). We consider both an idealized case, with a baroclinic slope current in a periodic channel, and a realistic case for the coast of Vesterålen in northern Norway. In the realistic case, the results of the data assimilation are compared with independent data from acoustic profilers and surface drifters. Best results are obtained when background error correlation scales are small (10 km or less) and when the data assimilation window is short, i.e. about 1 day. Furthermore, we find that the impact of assimilating HF radar currents is generally larger than the impact of CTD hydrography. However, combining the HF radar currents with a few hydrographic profiles gives significantly better results, which demonstrates the importance of complementing surface observations with observations of the vertical structure of the ocean.

Assessing the fidelity of surface currents from a coastal ocean model and HF radar using drifting buoys in the Middle Atlantic Bight

The rapid expansion of urbanization along the world’s coastal areas requires a more comprehensive and accurate understanding of the coastal ocean. Over the past several decades, numerical ocean circulation models have tried to provide such insight, based on our developing understanding of physical ocean processes. The systematic establishment of coastal ocean observation systems adopting cutting-edge technology, such as high frequency (HF) radar, satellite sensing, and gliders, has put such ocean model predictions to the test, by providing comprehensive observational datasets for the validation of numerical model forecasts. The New York Harbor Observing and Prediction System (NYHOPS) is a comprehensive system for understanding coastal ocean processes on the continental shelf waters of New York and New Jersey. To increase confidence in the system’s ocean circulation predictions in that area, a detailed validation exercise was carried out using HF radar and Lagrangian drifter-derived surface currents from three drifters obtained between March and October 2010. During that period, the root mean square (RMS) differences of both the east–west and north–south currents between NYHOPS and HF radar were approximately 15 cm s−1. Harmonic analysis of NYHOPS and HF radar surface currents shows similar tidal ellipse parameters for the dominant M2 tide, with a mean difference of 2.4 cm s−1 in the semi-major axis and 1.4 cm s−1 in the semi-minor axis and 3° in orientation and 10° in phase. Surface currents derived independently from drifters along their trajectories showed that NYHOPS and HF radar yielded similarly accurate results. RMS errors when compared to currents derived along the trajectory of the three drifters were approximately 10 cm s−1. Overall, the analysis suggests that NYHOPS and HF radar had similar skill in estimating the currents over the continental shelf waters of the Middle Atlantic Bight during this time period. An ensemble-based set of particle tracking simulations using one drifter which was tracked for 11 days showed that the ensemble mean separation generally increases with time in a linear fashion. The separation distance is not dominated by high frequency or short spatial scale wavelengths suggesting that both the NYHOPS and HF radar currents are representing tidal and inertial time scales correctly and resolving some of the smaller scale eddies. The growing ensemble mean separation distance is dominated by errors in the mean flow causing the drifters to slowly diverge from their observed positions. The separation distance for both HF radar and NYHOPS stays below 30 km after 5 days, and the two technologies have similar tracking skill at the 95 % level. For comparison, the ensemble mean distance of a drifter from its initial release location (persistence assumption) is estimated to be greater than 70 km in 5 days.

Backtracking drifting objects using surface currents from high-frequency (HF) radar technology

In this work, the benefits of high-frequency (HF) radar ocean observation technology for backtracking drifting objects are analysed. The HF radar performance is evaluated by comparison of trajectories between drifter buoys versus numerical simulations using a Lagrangian trajectory model. High-resolution currents measured by a coastal HF radar network combined with atmospheric fields provided by numerical models are used to backtrack the trajectory of two dataset of surface-drifting buoys: group I (with drogue) and group II (without drogue). A methodology based on optimization methods is applied to estimate the uncertainty in the trajectory simulations and to optimize the search area of the backtracked positions. The results show that, to backtrack the trajectory of the buoys in group II, both currents and wind fields were required. However, wind fields could be practically discarded when simulating the trajectories of group I. In this case, the optimal backtracked trajectories were obtained using only HF radar currents as forcing. Based on the radar availability data, two periods ranging between 8 and 10 h were selected to backtrack the buoy trajectories. The root mean squared error (RMSE) was found to be 1.01 km for group I and 0.82 km for group II. Taking into account these values, a search area was calculated using circles of RMSE radii, obtaining 3.2 and 2.11 km2 for groups I and II, respectively. These results show the positive contribution of HF radar currents for backtracking drifting objects and demonstrate that these data combined with atmospheric models are of value to perform backtracking analysis of drifting objects.

Dynamics of surface currents over Qingdao coastal waters in August 2008

[1] Surface currents measured by High Frequency (HF) radar are used to investigate the dynamics in the coastal waters of Qingdao, China, on the western coast of the Yellow Sea. Different factors affecting the surface currents are revealed by dynamical analysis. Harmonic tidal analysis shows that the coastal tidal currents are barotropic and their temporal evolution is mainly influenced by the semidiurnal tidal constitutes, topography and geometry. It is also found that their horizontal distribution represents the effect of local topography. At sub-tidal frequencies, the high correlation between winds and the sub-tidal surface currents indicates a crucial role of wind-forcing in driving the currents. Varying wind direction coupled with small-scale features of the coastal geometry results in complicated sub-mesoscale circulation. In addition, the monthly coastal circulation is characterized by an eddy structure and is primarily determined by the outflow which is closely associated with the sea level slope along the coast. The variability of the residual currents is studied by analyzing the cross-shore momentum equation with wind stress, sea level records, and HF radar currents. It is shown that both the barotropic pressure gradient and the nonlinear tidal stress contribute to the variations of the residual currents near the bay mouth and further govern the coastal monthly circulation in August, 2008.

Application of HF radar currents to oil spill modelling

In this work, the benefits of high-frequency (HF) radar currents for oil spill modeling and trajectory analysis of floating objects are analyzed. The HF radar performance is evaluated by means of comparison between a drifter buoy trajectory and the one simulated using a Lagrangian trajectory model. A methodology to optimize the transport model performance and to calculate the search area of the predicted positions is proposed. This method is applied to data collected during the Galicia HF Radar Experience. This experiment was carried out to explore the capabilities of this technology for operational monitoring along the Spanish coast. Two long-range HF radar stations were installed and operated between November 2005 and February 2006 on the Galician coast. In addition, a drifter buoy was released inside the coverage area of the radar. The HF radar currents, as well as numerical wind data were used to simulate the buoy trajectory using the TESEO oil spill transport model. In order to evaluate the contribution of HF radar currents to trajectory analysis, two simulation alternatives were carried out. In the first one, wind data were used to simulate the motion of the buoy. In the second alternative, surface currents from the HF radar were also taken into account. For each alternative, the model was calibrated by means of the global optimization algorithm SCEM-UA (Shuffled Complex Evolution Metropolis) in order to obtain the probability density function of the model parameters. The buoy trajectory was computed for 24 h intervals using a Monte Carlo approach based on the results provided in the calibration process. A bivariate kernel estimator was applied to determine the 95% confidence areas. The analysis performed showed that simulated trajectories integrating HF radar currents are more accurate than those obtained considering only wind numerical data. After a 24 h period, the error in the final simulated position improves using HF radar currents. Averaging the information from all the simulated daily periods, the mean search and rescue area calculated using HF radar currents, is reduced by approximately a 62% in comparison with the search area calculated without these data. These results show the positive contribution of HF radar currents for trajectory analysis, and demonstrate that these data combined with atmospheric forecast models, are of value for trajectory analysis of oil spills or floating objects.