Lucy R. Wyatt

Propagating waves on the ocean surface can be represented as a stochastic process whose statistics are characterized by a spectrum. This paper reviews methods for measuring the wave spectrum and related quantities. Observations begin by sensing fluid dynamical properties of the sea surface over space and/or time. Visual observations, collected routinely since the mid‐18th century, comprise the longest‐running wave record. Nearshore measurement methods continue to advance, including traditional pressure and acoustic sensing as well as newer technologies like distributed acoustic sensing and LiDAR. Detailed small‐scale wave physics can now be explored with measurement techniques using light, including stereo‐imaging and polarimetry. Reductions in the size, cost, and power consumption of microelectronics have propagated through ocean wave instrumentation, most notably in wave buoys. Global networks of freely drifting miniature wave buoys offer novel observational capabilities. Remote sensing techniques based on radar and LiDAR continue to evolve and are widely deployed from land, ships, aircraft, autonomous vehicles, and satellites. Spaceborne altimeters form one of the most important records of wave height, and new spaceborne sensors now observe directional spectra globally with sampling akin to traditional altimetry. Aircraft and autonomous systems provide strategic sampling capabilities for detailed process studies and access to extreme storm environments. The quality and quantity of ocean wave measurements have never been greater. This review aims to help make sense of it all.

Propagating waves on the surface of the ocean can be represented as a stochastic process, whose statistics are characterized by a spectrum. Measuring the wave spectrum, and quantities derived from the spectrum, are reviewed here. Observation begins by sensing some property of the sea surface over space and/or time. Visual observations, collected routinely since the mid 18th century, comprise the longerest running wave record. Measurement methods in the nearshore are advancing, including traditional methods using pressure and acoustic sensing, but also newer methods such as distributed acoustic sensing and lidar. Detailed, small scale wave physics can now be explored with measurement techniques using light, including stereo-imaging and polarimatry. The decrease in size, cost, and power consumption of microelectronics has propagated through to ocean wave instrumentation, most notably in wave buoys. Global networks of freely drifting miniature wave buoys offer novel observational power. Remote sensing techniques based on radar and lidar continue to evolve, and are widely deployed from land and on ships, aircraft, autonomous vehicles, and satellites. Spaceborne altimeters form one of the most important records of wave height, and a suite of suite of new spaceborne sensors are observing directional spectra across the globe with sampling akin to traditional altimetry. Aircraft and autonomous systems are providing strategic sampling capabilities, whether for detailed process studies or accessing extreme storm environments. The quality and quantity of ocean wave measurements has never been greater. This review will help you make sense of it all.

HF radar systems are used in many parts of the world as a part of operational coastal ocean observing systems. Their primary product is surface current mapping from the coast to a range determined by radio frequency and environmental conditions. Initiatives to promote their use for wave measurement are now being developed. Obtaining reliable wind measurements has proved more difficult primarily because there is no direct physical relationship between the radar signal and the wave field. In this paper, a machine learning approach, previously demonstrated for radar data at the location of an in situ measurement, has been extended to allow for wind mapping using wind model data for training. Using data from three different radar deployments operating at different frequencies, a single machine learning model has been developed that can be applied to all three locations. A subset of the model data is used in the training and testing of the method, and accuracy is assessed using a mix of these data and data at all model positions within the radar field of view. The results show that the new wind speed measurements are significantly more accurate than those previously available using an inverse wind-wave model. Radar wind maps are consistent with, although show more spatial variability than, model or satellite winds. More validation with offshore wind masts is recommended.

HF radar systems form part of many operational coastal monitoring systems providing near-real-time surface currents for many useful applications. Although wave measurements have been possible with these systems for many years, they have not yet been adopted widely for operational monitoring because they have not been thought to be sufficiently accurate or reliable. However, the value of such data is beginning to be appreciated, and this is motivating more work on wave measurement with HF radar systems with many more papers on accuracy assessment and data availability appearing in the literature. In this paper, the wave measurement capability, limitations, and differences between different radar types are reviewed, and methods to assess accuracy are discussed and applied to phased array HF radar data obtained from the University of Plymouth WERA radars using the Seaview Software inversion method during April and November 2012 compared with directional buoy data. Good accuracy over a range of different wave parameters will be demonstrated. Newly available single-radar inversions are shown to be less accurate than dual-radar inversions, although they still provide useful data, and ways to improve performance are discussed. Swell and wind–sea components in the directional spectra are identified, and qualitative agreement with buoy peak parameters is demonstrated. Recommendations are given on statistical methods for the validation of wave parameters.

Partitioning is a process used to separate wind-sea and swell contributions in an ocean wave directional spectrum to simplify, and hence make more useful, the interpretation of the spectrum for users of wave data. HF radar systems can measure the wave spectrum over regions of the coastal ocean from the coast to over 100 km offshore with good spatial and temporal resolution depending on the operating frequency and bandwidth. Such systems can measure hundreds of directional spectra across the field of view of the radar, so there is very strong motivation to reduce the dimensionality of the data set for practical applications using partitioning. For similar reasons partitioning methods are increasingly being used for wave model and satellite-measured spectra. A partitioning method, which extends the method of Waters J, Wyatt LR, Wolf J, Hines A. [2013. Data assimilation of partitioned HF radar wave data into Wavewatch III. Ocean Model. 72:17–31.] for HF radar data, is described, assessed using buoy data and used to demonstrate the spatial variability of both swell and wind waves in three coastal regions. The results are very encouraging. HF radar systems could therefore provide very useful data for wave model and satellite partitioning validations in coastal waters where model and satellite measurements are most challenged by wave-current interactions, coastal topography and bathymetry.

HF radars are now an important part of operational coastal observing systems where they are used primarily for measuring surface currents. Their use for wave and wind direction measurement has also been demonstrated. These measurements are based on physical models of radar backscatter from the ocean surface described in terms of its ocean wave directional spectrum and the influence thereon of the surface current. Although this spectrum contains information about the local wind that is generating the wind sea part of the spectrum, it also includes spectral components propagating into the local area having been generated by winds away from the area i.e., swell. In addition, the relationship between the local wind sea and wind speed depends on fetch and duration. Thus, finding a physical model to extract wind speed from the radar signal is not straightforward. In this paper, methods that have been proposed to date will be briefly reviewed and an alternative approach is developed using machine learning methods. These have been applied to three different data sets using different radar systems in different locations. The results presented here are encouraging and proposals for further development are outlined.

This paper presents the case for the use of HF radar to provide wave, current and wind measurements and maps during the surveying, resource and impact assessment, construction, maintenance and performance assessment phases of an offshore fixed or floating wind farm or other marine renewable project. The data presented are from the Celtic Sea which is currently the focus of a lot of offshore wind interest. The data are from a dual radar system deployed at the Wave Hub site off the Cornwall coast since 2011, a single radar system in North Devon reactivated in 2017, and data from 2005 when the North Devon radar was operated as a dual radar with a second site in South Wales. The accuracy of the wave and wind data is discussed and examples of the sort of data that could be available are presented.

HF radars are becoming important components of coastal operational monitoring systems particularly for currents and mostly using monostatic radar systems where the transmit and receive antennas are colocated. A bistatic configuration, where the transmit antenna is separated from the receive antennas, offers some advantages and has been used for current measurement. Currents are measured using the Doppler shift from ocean waves which are Bragg-matched to the radio signal. Obtaining a wave measurement is more complicated. In this paper, the theoretical basis for bistatic wave measurement with a phased-array HF radar is reviewed and clarified. Simulations of monostatic and bistatic radar data have been made using wave models and wave spectral data. The Seaview monostatic inversion method for waves, currents and winds has been modified to allow for a bistatic configuration and has been applied to the simulated data for two receive sites. Comparisons of current and wave parameters and of wave spectra are presented. The results are encouraging, although the monostatic results are more accurate. Large bistatic angles seem to reduce the accuracy of the derived oceanographic measurements, although directional spectra match well over most of the frequency range.

For a number of decades, coastal HF radar has been used to remotely measure ocean surface parameters, including waves, at distances exceeding 100 km. The information, which has value in many ocean engineering applications, is obtained using the HF radar cross-section, which relates the directional ocean spectrum to the received radar signal, through a nonlinear integral equation. The equation is impossible to solve analytically, for the ocean spectrum, and a number of numerical methods are currently used. In this study, a neural network is trained to infer the directional ocean spectrum from HF radar Doppler spectra. The neural network is trained and tested on simulated radar data and then validated with data collected off the coast of Cornwall, where there are two HF radars and a wave buoy to provide the sea-truth. Key ocean parameters are derived from the estimated directional spectra and then compared with the values measured by both the wave buoy and an existing inversion method. The results are encouraging; for example, the RMSE of the obtained mean wave direction decreases from 20.6° to 15.7°. The positive results show that neural networks may be a viable solution in certain situations, where existing methods struggle.

The IMOS HF-Radar array in South Australia provides observations of the ocean waters south of Spencer Gulf. In addition to ocean surface currents, the data from this array can be processed to provide near real-time observations of wave statistics and wind direction. The Australian Bureau of Meteorology requires access to these observations for forecast modelling but currently only have a single Waverider buoy operating in South Australian waters at Cape du Couedic, south of Kangaroo Island, which provides no directional information. The HF-Radar array could potentially be used to augment the current operational observation systems used by the Bureau. In this paper, we evaluate the performance of the HF-Radar system against observations from the Waverider buoy and an automatic weather station at Neptune Island and also compare the HF-Radar observations to a wave model based on the eSA-Marine forecast grid. The results suggest that upgrading the HF-Radar to provide near real-time wave and wind data would provide a new, independent source of environmental observations for the Bureau.