HF Radar Data Research Articles

Sea Surface Circulation Structures in the Malta-Sicily Channel from Remote Sensing Data

The Malta-Sicily Channel is part of the Sicily Channel system where water and thermohaline properties between the Eastern and Western Mediterranean basins take place. Several mesoscales features are detached from the main circulation due to wind and bathymetric forcing. In this paper, surface circulation structures are studied using different remotely sensed datasets: satellite data (absolute dynamic topography, Cross-Calibrated Multi-Platform wind vector analysis, satellite chlorophyll and sea surface temperature) and high frequency radar data. We identified high frequency motions (at short time scales—hours to days), as well as mesoscale structures fundamental for the understanding of the Malta-Sicily Channel circulation dynamics. One of those is the Malta-Sicily Gyre; an anticyclonic structure trapped between the Sicilian and Maltese coasts, which is poorly studied in the literature and often confused with the Malta Channel Crest and the Ionian Shelf Break Vortex. In order to characterize this gyre, we calculated its kinetic properties taking advantage of the fine-scale temporal and spatial resolution of the high frequency radar data, and thus confirming its presence with an updated version of the surface circulation patterns in the area.

Coastal currents from Jason-2 and SARAL/AltiKa in the Indian region

ABSTRACT In this study, data from space-based altimeters (Jason-2 and Satellite for ARgos and AltiKa [SARAL/AltiKa]) have been used to compute alongshore geostrophic currents in the coastal regions of the Indian mainland. These derived currents are compared with high-frequency (HF) radar observations. Beyond 30–40 km away from the coastline, altimeter-derived currents match fairly well with the HF radar data. Root mean square error (RMSE) of Jason-2-derived currents ranges between 0.3 and 0.6 m s−1 while the same in the case of SARAL/AltiKa lies between 0.3 and 0.7 m s−1. Satellite-derived across-track geostrophic current components (alongshore current) were also used to study the spatiotemporal variations of the east India coastal current (EICC). The coastal trapping of the EICC, its annual and intra-seasonal peaks are clearly observed in the power spectrum of time-series of Jason-2 and SARAL/AltiKa derived currents.

Validation of HF radar sea surface currents in the Malta-Sicily Channel

A network of High-Frequency radar (HFR) stations runs operationally in the Malta-Sicily Channel (MSC), Central Mediterranean Sea, providing sea surface current maps with high temporal (1 h) and spatial (3 × 3 km) resolutions since August 2012. Comparisons with surface drifter data and near-surface Acoustic Doppler Current Profiler (ADCP) observations, as well as radar site-to-site baseline analyses, provide quantitative assessments of HFR velocities accuracy. Twenty-two drifters were deployed within the HFR domain of coverage between December 2012 and October 2013. Additionally, six ADCP vertical current profiles were collected at selected positions during a dedicated field survey. External radio-frequency interferences lead to significant gaps in the HFR coverage, which were minimized by an interpolation technique applied to the surface current fields. The validation of HFR velocities was first performed for each individual station in the network, using data generated with both the ideal and the measured antenna patterns. The validation was then repeated with pairs of HFR stations using the site-to-site baseline error analysis. The analysis confirms that the baseline error estimations and the full validation metrics for this HFR frequency band are consistent, and in some aspects superior, when compared to results from similar studies in other regions. The validation with drifter tracks suggests that the adopted interpolation procedure does not in general impact on the comparison metrics, but may introduce biases when used to extrapolate currents in regions where radar geometrical constraints are suboptimal. The analysis of the percentage of contribution of individual HFR stations shown that the quality of HFR data is influenced by the network geometry.

Uncertainty Estimates for SeaSonde HF Radar Ocean Current Observations

Abstract HF radars typically produce maps of surface current velocities without estimates of the measurement uncertainties. Many users of HF radar data, including spill response and search and rescue operations, incorporate these observations into models and would thus benefit from quantified uncertainties. Using both simulations and coincident observations from the baseline between two operational SeaSonde HF radars, we demonstrate the utility of expressions for estimating the uncertainty in the direction obtained with the Multiple Signal Classification (MUSIC) algorithm. Simulations of radar backscatter using surface currents from the Regional Ocean Modeling System show a close correspondence between direction of arrival (DOA) errors and estimated uncertainties, with mean values of 15° at 10 dB, falling to less than 3° at 30 dB. Observations from two operational SeaSondes have average DOA uncertainties of 2.7° and 3.8°, with a fraction of the observations (10.5% and 7.1%, respectively) having uncertainties of >10°. Using DOA uncertainties for data quality control improves time series comparison statistics between the two radars, with r2=0.6 increasing to r2=0.75 and RMS difference decreasing from 15 to 12 cm s−1. The analysis illustrates the major sources of error in oceanographic HF radars and suggests that the DOA uncertainties are suitable for assimilation into numerical models.

Applying the Method of EOF Interpolation and 2dVar to Complete the Ocean Surface Current Data Obtained from HF Radar System

Abstract: There are now over 350 high frequency radar (HF radar) stations operating on the coast of 37 countries around the world that allow the mapping of ocean surface current. However, observation from HF radars are often interrupted (loss of data) in both space and time due to signal inference, backscatters, ocean state.Therefore, in this study, we will present a method to improve the surface current data collected from HF radar system. Firstly, the radial surface current data will be filtered intermittently, then the result is interpolated over time and space by the orthogonal experimental EOF and the 2dVar bi-directional variable interpolation. In addition, the authors have initially applied 2dVar interpolation method to the HF radar data in Vietnam and received initial positive results. The methods used in this paper promise to be effective when applied to improve surface flow data obtained from HF radar stations in Vietnam in the future.
 
 Keywords:EOF interpolation, 2dVar, Iroise sea, HF radar, ocean surface current.

HF radar for wind waves measurements in the Malta-Sicily Channel

The CALYPSO HF radar network is a permanent and fully operational observing system currently composed of four CODAR SeaSonde stations. The system is providing real-time hourly maps of sea surface currents and waves data in the Malta-Sicily Channel. The present work aims to compare significant wave height measurements by HF Radar to wave data from numerical models and satellite altimeter. This is the first time that this set of wave data are analysed since the four HF radars were installed between 2012 and 2015. Results suggest that CODAR HF Radar wave data are a reliable source of wave information even in the case of extreme events, providing an avenue to improve and complete the offer of services deriving from the CALYPSO system. Comparisons of HF radar data with both numerical sea wave model and satellite altimeter data confirm agreement, in particular for radar measurements in the annular sectors within the central range-cells which are also characterized by a more reliable and homogeneous temporal behaviour.

On extracting high-frequency tidal variability from HF radar data in the northwestern Bay of Bengal

ABSTRACTFirst results from a systematic harmonic analysis of HF radar (HFR) derived ocean surface current observations in the northwestern Bay of Bengal (BoB) during 2010 is presented. The daily-averaged HFR currents compared reasonably well to composite daily surface currents from multiple satellites with correlation coefficient of 0.90 (0.69) for zonal (meridional) component. A set of sequential daily currents demonstrated sustained northward (southward) alongshore flow during February–April (October–December) with peak magnitude of about 1.8 (1.2) m/s. On tidal scales, harmonic analyses of zonal and meridional components at nearshore and offshore locations indicated that among semi-diurnal tidal components, M2 dominates over S2 and N2; time-scales of which were verified from available coastal tide gauges nearby. Amplitudes of semi-major axis for M2 and K1 tidal ellipses are 7.16 (6.13) and 4.02 (3.30) cm/s in nearshore (offshore) location indicating relatively stronger tidal currents in nearshore location. Finally, significant shallow water constituents S4, MS4 and M3 (M4, 2SM6 and M6) at nearshore (offshore) location are identified, which are due to non-linear interaction of tidal currents with bathymetry. Both semi-diurnal and shallow water tidal currents show dominance along isobaths in offshore region, which turn progressively across-isobath as they move nearshore.

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

Accurate and timely information of surface currents is crucial for various operations such as search and rescue, marine renewable energy extraction and oil spill treatment. Conventional approaches to study coastal surface currents are numerical models and observation platforms such as radars and satellites. However, both have limits. To efficiently obtain high accuracy short-term forecasting states of oceanic parameters of interest, a robust soft computing approach—Artificial Neural Networks (ANN)—was applied to predict surface currents in a tide- and wind-dominated coastal area. Hourly observed surface currents from a Coastal Ocean Dynamic Application Radar (CODAR) system, and tide and wind data from forecasting models were used to establish ANN models for Galway Bay area. One of the fastest algorithms, resilient back propagation, was used to adapt all weights and biases. This study focused on investigating the sensitivity of an ANN model to a series of different input datasets. Results indicate that correlation between ANN forecasts and observation was greater than 0.9 for both surface velocity components with one-hour lead time. Strong correlation ( ≥ 0.75) was obtained between predicted results and radar data for both surface velocity components with three-hour lead time at best. However, forecasting accuracy deteriorated rapidly with longer lead time. By comparison with previous data assimilation models, in this research, best performance was achieved from ANN model’s peak times of the tidally dominant surface velocity component. The forecasts presented in this research show clear improvements over previous attempts at short-term forecasting of wind- and tide-dominated currents using ANN.

A Support Vector Regression-Based Method for Target Direction of Arrival Estimation From HF Radar Data

High-frequency (HF) radars have great potential for maritime surveillance, and the multiple signal classification (MUSIC) algorithm is usually used to estimate the direction of arrival (DOA) of targets for a wide-beam radar. However, the performance of the MUSIC algorithm relies on the precision of the antenna pattern, which could be contaminated by nearby electromagnetic interference. Therefore, the actual antenna pattern must be measured and used. In order to remove the requirement of antenna pattern measurement, a new method for target DOA estimation from wide-beam HF radar data using support vector regression (SVR) is proposed in this letter. A system model that relates target bearing and radar data feature is obtained through the SVR-based machine learning using the automatic identification system data and data associated with the vessels successfully detected by the HF radar. Then, such a model is used to determine the DOAs of targets from new data. The field experimental results at two sites demonstrate that the performance of the SVR method is better than that of the MUSIC algorithm.

Comparative Study on Assimilating Remote Sensing High Frequency Radar Surface Currents at an Atlantic Marine Renewable Energy Test Site

A variety of data assimilation approaches have been applied to enhance modelling capability and accuracy using observations from different sources. The algorithms have varying degrees of complexity of implementation, and they improve model results with varying degrees of success. Very little work has been carried out on comparing the implementation of different data assimilation algorithms using High Frequency radar (HFR) data into models of complex inshore waters strongly influenced by both tides and wind dynamics, such as Galway Bay. This research entailed implementing four different data assimilation algorithms: Direct Insertion (DI), Optimal Interpolation (OI), Nudging and indirect data assimilation via correcting model forcing into a three-dimensional hydrodynamic model and carrying out detailed comparisons of model performances. This work will allow researchers to directly compare four of the most common data assimilation algorithms being used in operational coastal hydrodynamics. The suitability of practical data assimilation algorithms for hindcasting and forecasting in shallow coastal waters subjected to alternate wetting and drying using data collected from radars was assessed. Results indicated that a forecasting system of surface currents based on the three-dimensional model EFDC (Environmental Fluid Dynamics Code) and the HFR data using a Nudging or DI algorithm was considered the most appropriate for Galway Bay. The largest averaged Data Assimilation Skill Score (DASS) over the ≥6 h forecasting period from the best model NDA attained 26% and 31% for east–west and north–south surface velocity components respectively. Because of its ease of implementation and its accuracy, this data assimilation system can provide timely and useful information for various practical coastal hindcast and forecast operations.

Ocean Wind and Current Retrievals Based on Satellite SAR Measurements in Conjunction with Buoy and HF Radar Data

A total of 168 fully polarimetric synthetic-aperture radar (SAR) images are selected together with the buoy measurements of ocean surface wind fields and high-frequency radar measurements of ocean surface currents. Our objective is to investigate the effect of the ocean currents on the retrieved SAR ocean surface wind fields. The results show that, compared to SAR wind fields that are retrieved without taking into account the ocean currents, the accuracy of the winds obtained when ocean currents are taken into account is increased by 0.2–0.3 m/s; the accuracy of the wind direction is improved by 3 – 4 ° . Based on these results, a semi-empirical formula for the errors in the winds and the ocean currents is derived. Verification is achieved by analysis of 52 SAR images, buoy measurements of the corresponding ocean surface winds, and high-frequency radar measurements of ocean currents. Results of the comparisons between data obtained by the semi-empirical formula and data measured by the high-frequency radar show that the root-mean-square error in the ocean current speed is 12.32 cm/s and the error in the current direction is 6.32°.

A probabilistic method for the estimation of ocean surface currents from short time series of HF radar data

We present a new method for inverting ocean surface currents from beam-forming HF radar data. In contrast with the classical method, which inverts radial currents based on shifts of the main Bragg line in the radar Doppler spectrum, the method works in the temporal domain and inverts currents from the amplitude modulation of the I and Q radar time series. Based on this principle, we propose a Maximum Likelihood approach, which can be combined with a Bayesian inference method assuming a prior current distribution, to infer values of the radial surface currents. We assess the method performance by using synthetic radar signal as well as field data, and systematically comparing results with those of the Doppler method. The new method is found advantageous for its robustness to noise at long range, its ability to accommodate shorter time series, and the possibility to use a priori information to improve the estimates. Limitations are related to current sign errors at far-ranges and biased estimates for small current values and very short samples. We apply the new technique to a data set from a typical 13.5 MHz WERA radar, acquired off of Vancouver Island, BC, and show that it can potentially improve standard synoptic current mapping.

Sea Surface Wind Correction Using HF Ocean Radar and Its Impact on Coastal Wave Prediction

AbstractBoth wind speeds and wind directions are important for predicting wave heights near complex coastal areas, such as small islands, because the fetch is sensitive to the wind direction. High-frequency (HF) radar can be used to estimate sea surface wind directions from first-order scattering. A simple method is proposed to correct sea surface wind vectors from reanalysis data using the wind directions estimated from HF radar. The constraints for wind speed corrections are that the corrections are small and that the corrections of horizontal divergences are small. A simple algorithm for solving the solution that minimizes the weighted sum of the constraints is developed. Another simple method is proposed to correct sea surface wind vectors. The constraints of the method are that corrections of wind vectors and horizontal divergences from the reanalysis wind vectors are small and that the projection of the corrected wind vectors to the direction orthogonal to the HF radar–estimated wind direction is small. The impact of wind correction on wave parameter prediction is large in the area in which the fetch is sensitive to wind direction. The accuracy of the wave prediction is improved by correcting the wind in that area, where correction of wind direction is more important than correction of wind speeds for the improvement. This method could be used for near-real-time wave monitoring by correcting forecast winds using HF radar data.

Observations of the sub-inertial, near-surface East India Coastal Current

We present surface current measurements made using two pairs of HF (high-frequency) radars deployed on the east coast of India. The radar data, used in conjunction with data from acoustic Doppler current profiler (ADCP) measurements on the shelf and slope off the Indian east coast, confirm that the East India Coastal Current (EICC) flows poleward as a deep current during February–March. During the summer monsoon, when the EICC flows poleward, and October–December, when the EICC flows equatorward, the current is shallow (<40m deep), except towards the northern end of the coast. Data from Argo floats confirm a shallow mixed layer that leads to a strong vertical shear off southeast India during October–December. A consequence of the strong stratification is that the upward propagation of phase evident in the ADCP data does not always extend to the surface. Even within the seasons, however, the poleward and equatorward flows show variability at periods of the order of 20–45 days, implying that the EICC direction is the same over the top ∼100m for short durations. The high spatial resolution of the HF radar data brings out features at scales shorter than those resolved by the altimeter and the high temporal resolution captures short bursts that are not captured in satellite-derived estimates of surface currents. The radar data show that the EICC, which is a boundary current, leaves a strong imprint on the current at the coast. Since the EICC is known to be affected significantly by remote forcing, this correlation between the boundary and nearshore current implies the need to use large-domain models even for simulating the nearshore current. Comparison with a simulation by a state-of-the-art Ocean General Circulation Model, run at a resolution of 0.1°×0.1°, shows that the model is able to simulate only the low-frequency variability.

Tsunami Detection by High Frequency Radar Beyond the Continental Shelf: II. Extension of Time Correlation Algorithm and Validation on Realistic Case Studies

In past work, tsunami detection algorithms (TDAs) have been proposed, and successfully applied to offline tsunami detection, based on analyzing tsunami currents inverted from high-frequency (HF) radar Doppler spectra. With this method, however, the detection of small and short-lived tsunami currents in the most distant radar ranges is challenging due to conflicting requirements on the Doppler spectra integration time and resolution. To circumvent this issue, in Part I of this work, we proposed an alternative TDA, referred to as time correlation (TC) TDA, that does not require inverting currents, but instead detects changes in patterns of correlations of radar signal time series measured in pairs of cells located along the main directions of tsunami propagation (predicted by geometric optics theory); such correlations can be maximized when one signal is time-shifted by the pre-computed long wave propagation time. We initially validated the TC-TDA based on numerical simulations of idealized tsunamis in a simplified geometry. Here, we further develop, extend, and apply the TC algorithm to more realistic tsunami case studies. These are performed in the area West of Vancouver Island, BC, where Ocean Networks Canada recently deployed a HF radar (in Tofino, BC), to detect tsunamis from far- and near-field sources, up to a 110 km range. Two case studies are considered, both simulated using long wave models (1) a far-field seismic, and (2) a near-field landslide, tsunami. Pending the availability of radar data, a radar signal simulator is parameterized for the Tofino HF radar characteristics, in particular its signal-to-noise ratio with range, and combined with the simulated tsunami currents to produce realistic time series of backscattered radar signal from a dense grid of cells. Numerical experiments show that the arrival of a tsunami causes a clear change in radar signal correlation patterns, even at the most distant ranges beyond the continental shelf, thus making an early tsunami detection possible with the TC-TDA. Based on these results, we discuss how the new algorithm could be combined with standard methods proposed earlier, based on a Doppler analysis, to develop a new tsunami detection system based on HF radar data, that could increase warning time. This will be the object of future work, which will be based on actual, rather than simulated, radar data.

Towards wide-swath high-resolution mapping of total ocean surface current vectors from space: Airborne proof-of-concept and validation

Two-dimensional high-resolution maps of total surface current vectors obtained for the first time with an airborne demonstrator of the innovative Wavemill instrument concept are validated against HF radar data and compared with output from the POLCOMS high-resolution coastal ocean circulation model. Wavemill is a squinted along-track interferometric SAR system optimized for ocean surface current vector retrieval that operates at moderate incidence angles (∼30°) and is compatible with spaceborne implementation. This paper represents the first comprehensive validation of the current retrieval capabilities of squinted along-track SAR interferometry in support of its development as a future European Space Agency Earth Explorer mission.Wavemill airborne data were acquired in October 2011 in Liverpool Bay off the west coast of Great Britain in light southerly wind (5.5m/s) and maximum tidal ebbing flow (0.7m/s) conditions. Contributions to the measured SAR interferometric phase by surface gravity waves, known as the Wind-wave induced Artefact Surface Velocity (WASV), were removed using our best estimate of wind conditions and the (Mouche et al., 2012) empirical correction derived from Envisat ASAR. Validation of the 1.5km resolution Wavemill current vectors against independent current measurements from HF radar gives very encouraging results, with Wavemill biases and precisions typically better than 0.05m/s and 0.1m/s for surface current speed, and better than 10° and 7° for current direction.The sensitivity of the current retrieval to the wind vector used to compute the WASV is estimated. A ± 1m/s error (bias) in wind speed has minimal impact on the quality of the retrieved currents. In contrast, the choice of wind direction is critical: a bias of ± 15° in the direction of the wind vector degrades the accuracy of the airborne current speed against the HF radar by about ± 0.2m/s. This highlights the need for future instruments to provide calibrated SAR Normalised Radar Cross Section data to support retrieval of wind and current vectors simultaneously.Comparisons of POLCOMS surface currents with HF radar data indicate that the model reproduces well the overall temporal evolution of the tidal current (correlation of spatial fields against HF radar over two tidal cycles of 0.9) but that the model features a systematic 1-h delay in the timing of the maximum ebbing flow in eastern parts of the domain near the Mersey Bar Light buoy. At the maximum ebb flow, the model underestimates the current speed (bias of −0.2m/s) with respect to the HF radar and Wavemill data at the time of the flights. Both the HF radar and Wavemill data reflect much greater snapshot spatial variability of the ocean surface current field than is present in the model, resulting in poor correlation of instantaneous spatial fields (< 0.5) between POLCOMS and the HF radar data. The Wavemill data reveal high spatial variability of ocean surface currents at fine scales, which are not visible in the 4km resolution HF radar data. Wavemill detects several strong (1–1.5m/s) localized current jets associated with deeper bathymetry channels in shallow waters (< 10 m) that are too narrow or too close to land to be observed by the HF radar. The study confirms the value of synoptic wide-swath maps of high-resolution ocean surface current vectors for coastal applications and to validate and develop high-resolution ocean circulation models.

Operational oil spill trajectory modelling using HF radar currents: A northwest European continental shelf case study

This paper presents a novel operational oil spill modelling system based on HF radar currents, implemented in a northwest European shelf sea. The system integrates Open Modal Analysis (OMA), Short Term Prediction algorithms (STPS) and an oil spill model to simulate oil spill trajectories. A set of 18 buoys was used to assess the accuracy of the system for trajectory forecast and to evaluate the benefits of HF radar data compared to the use of currents from a hydrodynamic model (HDM). The results showed that simulated trajectories using OMA currents were more accurate than those obtained using a HDM. After 48h the mean error was reduced by 40%. The forecast skill of the STPS method was valid up to 6h ahead. The analysis performed shows the benefits of HF radar data for operational oil spill modelling, which could be easily implemented in other regions with HF radar coverage.

Comparison of retracked coastal altimetry sea levels against high frequency radar on the continental shelf of the Great Barrier Reef, Australia

Comparison of Jason-1 altimetry retracked sea levels and high frequency (HF) radar velocity is examined within the region of the Great Barrier Reef, Australia. The comparison between both datasets is not direct because the altimetry derives only the geostrophic component, while the HF radar velocity includes information on both geostrophic and ageostrophic components, such as tides and winds. The comparison of altimetry and HF radar data is performed based on the parameter of surface velocity inferred from both datasets. The results show that 48% (10 out of 21 cases) of data have high (≥0.5) spatial correlation. The mean of spatial correlation for all 21 cases is 0.43. This value is within the range (0.42 to 0.5) observed by other studies. Low correlation is observed due to disagreement in the trend of velocity signals in which sometimes they have contradictions in the signal direction and the position of the peak is shifted. In terms of standard deviation of difference and root mean square error, both datasets show reasonable agreement with ≤2.5 cm s−1.

Surface current dynamics under sea breeze conditions observed by simultaneous HF radar, ADCP and drifter measurements

Ocean surface boundary layer dynamics off the southern coast of France in the NW Mediterranean is investigated by using velocity observations by high-frequency (HF) radars, surface drifting buoys and a downward-looking drifting acoustic Doppler current profiler (ADCP). The analysis confirms that velocities measured by HF radars correspond to those observed by an ADCP at the effective depth z f = k −1, where k is wavenumber of the radio wave emitted by the radar. The radials provided by the radars were in a very good agreement with in situ measurements, with the relative errors of 1 and 9 % and root mean square (RMS) differences of 0.02 and 0.04 m/s for monostatic and bistatic radar, respectively. The total radar-based velocities appeared to be slightly underestimated in magnitude and somewhat biased in direction. At the end of the survey period, the difference in the surface current direction, based on HF radar and ADCP data, attained 10°. It was demonstrated that the surface boundary layer dynamics cannot be reconstructed successfully without taking into the account velocity variation with depth. A significant misalignment of ∼30° caused by the sea breeze was documented between the HF radar (HFR-derived) surface current and the background current. It was also found that the ocean response to a moderate wind forcing was confined to the 4-m-thick upper layer. The respective Ekman current attained the maximum value of 0.15 m/s, and the current rotation was found to be lagging the wind by approximately 40 min, with the current vector direction being 15–20° to the left of the wind. The range of velocity variability due to wind forcing was found comparable with the magnitude of the background current variability.

Short-Term Variability Analysis of the Hf-Radar Data and Its Classification Scheme

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