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A new method for estimating current-depth profiles from observations of wavenumber-dependent Doppler shifts of the overlying ocean wave field is presented. Consecutive scans of marine X-band backscatter provide wave field measurements in the time–space domain that transform into the directional wavenumber–frequency domain via a 3D fast Fourier transform (FFT). Subtracting the linear dispersion shell yields Doppler shift observations in the form of ( k x , k y , Δ ω ) triplets. A constrained linear regression technique is used to extract the wavenumber-dependent effective velocities, which represent a weighted depth average of the Eulerian currents (Stewart and Joy). This new method estimates these Eulerian currents from the effective velocities via the inversion of the integral relationship, which was first derived by Stewart and Joy. To test the effectiveness of the method, the inverted current profiles are compared to concurrent ADCP measurements. The inversion method is found to successfully predict current behavior, with a depth-average root-mean-square (RMS) error less than 0.1 m s −1 for wind speeds greater than 5 m s −1 and a broad wave spectrum. The ability of the inversion process to capture the vertical structure of the currents is assessed using a time-average RMS error during these favorable conditions. The time-averaged RMS error is found to be less than 0.1 m s −1 for depths shallower than 20 m, approximately twice the depth of existing methods of estimating current shear from wave field measurements.

The influence of wave–current interactions on time series of marine X-band radar backscatter maps at the mouth of the Columbia River (MCR) near Astoria, Oregon, is examined. The energetic wave environment at the MCR, coupled with the strong tidally forced currents, provides a unique test environment to explore the limitations in accurately determining the magnitude and vertical structure of upper-ocean currents from wavefield measurements. Direct observation in time and space of the wave-induced radar backscatter and supporting acoustic Doppler current profiler (ADCP) current measurements provide a rich dataset for investigating how currents shift the observed wave dispersion relationship. First, current extraction techniques that assume a specific current–depth profile are tested against ADCP measurements. These constrained solutions prove to have inaccuracies because the models do not properly account for vertical shear. A forward solution using measured current profiles to predict the wavenumber–Doppler shift relationship for the range of ocean waves sensed by the radar is introduced. This approach confirms the ocean wavefield is affected by underlying vertical current shear. Finally, a new inversion method is developed to extract current profiles from the wavenumber-dependent Doppler shift observations. The success of the inversion model is shown to be sensitive to the range of wavenumbers spanned by observed Doppler shifts, with skill exceeding 0.8 when wavenumbers span more than 0.1 rad m −1 . This agreement when observations successfully capture the broadband wavefield suggests the X-band backscatter is a viable means of remotely estimating current shear.

A skill analysis of the Multiple Signal Characterization (MUSIC) algorithm used in compact-antenna-style HF radar ocean current radial velocity/bearing determination is performed using simulation. The simulation is based upon three collocated antennas (two cross loops and a monopole with ideal gain patterns) in a geometry similar to the 25-MHz SeaSonde HF radar commercially available from Coastal Ocean Dynamics Applications Radar (CODAR) Ocean Sensors, Palo Alto, California. The simulations consider wind wave/current scenarios of varying complexity to provide insight to the accuracy of surface current retrievals and the inherent limitations of the technique, with a focus on the capabilities of the MUSIC algorithm itself. The influence of second-order scatter, interference, and stationary target scatter are not considered. Simple error reduction techniques are explored and their impacts quantified to aid in operational system configuration and encourage areas of further research. Increases in skill between 55% and 100% using spatial averaging, and between 14% and 33% using temporal averaging, are realized, highlighting the utility of these techniques. When these error-flagging and averaging techniques are employed, individual range cell skill metrics are found to be as high as 0.94 for simple currents at a high signal-to-noise ratio (SNR), while more complex currents achieve a maximum skill metric of 0.72 for the same SNR. These simulations, conducted under ideal conditions, provide insight to understanding the variables, which influence the accuracy of surface currents retrieved using MUSIC.

This study takes on the challenge of resolving upper ocean surface currents with a suite of airborne remote sensing methodologies, simultaneously imaging the ocean surface in visible, infrared, and microwave bands. A series of flights were conducted over an air-sea interaction supersite established 63 km offshore by a large multi-platform CASPER-East experiment. The supersite was equipped with a range of in situ instruments resolving air-sea interface and underwater properties, of which a bottom-mounted acoustic Doppler current profiler was used extensively in this paper for the purposes of airborne current retrieval validation and interpretation. A series of water-tracing dye releases took place in coordination with aircraft overpasses, enabling dye plume velocimetry over 100 m to 10 km spatial scales. Similar scales were resolved by a Multichannel Synthetic Aperture Radar, which resolved a swath of instantaneous surface velocities (wave and current) with 10 m resolution and 5 cm/s accuracy. Details of the skin temperature variability imprinted by the upper ocean turbulence were revealed in 1–14,000 m range of spatial scales by a mid-wave infrared camera. Combined, these methodologies provide a unique insight into the complex spatial structure of the upper ocean turbulence on a previously under-resolved range of spatial scales from meters to kilometers. However, much attention in this paper is dedicated to quantifying and understanding uncertainties and ambiguities associated with these remote sensing methodologies, especially regarding the smallest resolvable turbulent scales and reference depths of retrieved currents.

A method based on machine learning and image processing techniques has been developed to track the surface expression of internal waves in near–real time. X-band radar scans are first preprocessed and averaged to suppress surface wave clutter and enhance the signal-to-noise ratio of persistent backscatter features driven by gradients in surface currents. A machine learning algorithm utilizing a support vector machine (SVM) model is then used to classify whether or not the image contains an internal solitary wave (ISW) or internal tide bore (bore). The use of machine learning is found to allow rapid assessment of the large dataset, and provides insight on characterizing optimal environmental conditions to allow for radar illumination and detection of ISWs and bores. Radon transforms and local maxima detections are used to locate these features within images that are determined to contain an ISW or bore. The resulting time series of locations is used to create a map of propagation speed and direction that captures the spatiotemporal variability of the ISW or bore in the coastal environment. This technique is applied to 2 months of data collected near Point Sal, California, and captures ISW and bore propagation speed and direction information that currently cannot be measured with instruments such as moorings and synthetic aperture radar (SAR).

The Coupled Air–Sea Processes and Electromagnetic Ducting Research (CASPER) project aims to better quantify atmospheric effects on the propagation of radar and communication signals in the marine environment. Such effects are associated with vertical gradients of temperature and water vapor in the marine atmospheric surface layer (MASL) and in the capping inversion of the marine atmospheric boundary layer (MABL), as well as the horizontal variations of these vertical gradients. CASPER field measurements emphasized simultaneous characterization of electromagnetic (EM) wave propagation, the propagation environment, and the physical processes that gave rise to the measured refractivity conditions. CASPER modeling efforts utilized state-of-the-art large-eddy simulations (LESs) with a dynamically coupled MASL and phase-resolved ocean surface waves. CASPER-East was the first of two planned field campaigns, conducted in October and November 2015 offshore of Duck, North Carolina. This article highlights the scientific motivations and objectives of CASPER and provides an overview of the CASPER-East field campaign. The CASPER-East sampling strategy enabled us to obtain EM wave propagation loss as well as concurrent environmental refractive conditions along the propagation path. This article highlights the initial results from this sampling strategy showing the range-dependent propagation loss, the atmospheric and upper-oceanic variability along the propagation range, and the MASL thermodynamic profiles measured during CASPER-East.

Knowledge of the nearshore ocean environment is important for naval operations, including military and humanitarian applications. The models used by the US Navy for predicting waves and circulation in the coastal regions are presented here. The wave model of choice for coastal regions is the Simulating WAves Nearshore (SWAN) model, which predicts wave energy as a function of frequency and direction. SWAN is forced by winds as well as waves at the offshore boundaries. For coastal circulation, Delft3D, composed of a number of different modules that can be coupled with each other, is presently used. Most applications for daily operational predictions use only the Delft3D-FLOW module, which predicts currents, mean water levels, temperature, and salinity. Inputs to the model include winds, tides, general ocean circulation, waves, daily river discharges, temperature, and salinity. Delft3D-FLOW is coupled with the Delft3D-WAVE module for areas where wave effects are of importance. A four-dimensional variational assimilation (4DVar) system based on SWAN, the SWANFAR system, is under development for nearshore wave predictions. It will improve wave predictions by using regional wave observations. We present several case studies that illustrate the validation and diverse applications of these models. All operational systems are run at the Naval Oceanographic Office.

This paper proposes a novel algorithm for retrieving the ocean wind vector from marine radar image sequences in real time. It is presented as an alternative to mitigate anemometer problems, such as blockage, shadowing, and turbulence. Since wind modifies the sea surface, the proposed algorithm is based on the dependence of the sea surface backscatter on wind direction and speed. This algorithm retrieves the wind vector using radar measurements in the range of 200–1500 m. Wind directions are retrieved from radar images integrated over time and smoothed (averaged) in space by searching for the maximum radar cross section in azimuth as the radar cross section is largest for upwind directions. Wind speeds are retrieved by an empirical third-order polynomial geophysical model function (GMF), which depends on the range distance in the upwind direction to a preselected intensity level and the intensity level. This GMF is approximated from a dataset of collocated in situ wind speed and radar measurements (~31 000 measurements, ~56 h). The algorithm is validated utilizing wind and radar measurements acquired on the Research Platform (R/P) FLIP (for Floating Instrumentation Platform) during the 13-day Office of Naval Research experiment on High-Resolution Air–Sea Interaction (HiRes) in June 2010. Wind speeds ranged from 4 to 22 m s−1. Once the proposed algorithm is tuned, standard deviations and biases of 14° and −1° for wind directions and of 0.8 and −0.1 m s−1 for wind speeds are observed, respectively. Additional studies of uncertainty and error of the retrieved wind speed are also reported.

Estimates of surface currents over the continental shelf are now regularly made using high-frequency radar (HFR) systems along much of the U.S. coastline. The recently deployed HFR system at the Martha’s Vineyard Coastal Observatory (MVCO) is a unique addition to these systems, focusing on high spatial resolution over a relatively small coastal ocean domain with high accuracy. However, initial results from the system showed sizable errors and biased estimates of M2 tidal currents, prompting an examination of new methods to improve the quality of radar-based velocity data. The analysis described here utilizes the radial metric output of CODAR Ocean Systems’ version 7 release of the SeaSonde Radial Site Software Suite to examine both the characteristics of the received signal and the output of the direction-finding algorithm to provide data quality controls on the estimated radial currents that are independent of the estimated velocity. Additionally, the effect of weighting spatial averages of radials falling within the same range and azimuthal bin is examined to account for differences in signal quality. Applied to two month-long datasets from the MVCO high-resolution system, these new methods are found to improve the rms difference comparisons with in situ current measurements by up to 2 cm s−1, as well as reduce or eliminate observed biases of tidal ellipses estimated using standard methods.