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17 USC 105 interim-entered record; under review. The article of record as published may be found at http://dx.doi.org/10.1175/BAMS-D-19-0281.1 The inner shelf, the transition zone between the surfzone and the midshelf, is a dynamically complex region with the evolution of circulation and stratification driven by multiple physical processes. Cross-shelf exchange through the inner shelf has important implications for coastal water quality, ecological connectivity, and lateral movement of sediment and heat. The Inner-Shelf Dynamics Experiment (ISDE) was an intensive, coordinated, multi-institution field experiment from September–October 2017, conducted from the midshelf, through the inner shelf, and into the surfzone near Point Sal, California. Satellite, airborne, shore- and ship-based remote sensing, in-water moorings and ship-based sampling, and numerical ocean circulation models forced by winds, waves, and tides were used to investigate the dynamics governing the circulation and transport in the inner shelf and the role of coastline variability on regional circulation dynamics. Here, the following physical processes are highlighted: internal wave dynamics from the midshelf to the inner shelf; flow separation and eddy shedding off Point Sal; offshore ejection of surfzone waters from rip currents; and wind-driven subtidal circulation dynamics. The extensive dataset from ISDE allows for unprecedented investigations into the role of physical processes in creating spatial heterogeneity, and nonlinear interactions between various inner-shelf physical processes. Overall, the highly spatially and temporally resolved oceanographic measurements and numerical simulations of ISDE provide a central framework for studies exploring this complex and fascinating region of the ocean. U.S. Office of Naval Research (ONR) ONR Departmental Research Initiative (DRI) Inner-Shelf Dynamics Experiment (ISDE)

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).

Accurate surface meteorological (MET) observations reported reliably and in near–real time remain a critical component of on-scene environmental observation systems. Presented is a system developed by Scripps Institution of Oceanography that allows for rapid, global deployment of ground-based weather observations to support both timely decision-making and collection of high-quality weather time series for science or military applications in austere environments. Named the Expeditionary Meteorological (XMET), these weather stations have been deployed in extreme conditions devoid of infrastructure ranging from tropical, polar, maritime, and desert environments where near continuous observations were reported. To date, over 1 million weather observations have been collected during 225 deployments around the world with a data report success rate of 99.5%. XMET had its genesis during Operation Iraqi Freedom (OIF), when the U.S. Marine Corps 3rd Marine Aircraft Wing identified an immediate capability gap in environmental monitoring of their operation area due to high spatiotemporal variability of dust storms in the region. To address the sensing gap, XMET was developed to be a portable, expendable, ruggedized, self-contained, bidirectional, weather observation station that can be quickly deployed anywhere in the world to autonomously sample and report aviation weather observations. This paper provides an overview of the XMETs design, reliability in different environments, and examples of unique meteorological events that highlight both the unit’s reliability and ability to provide quality time series. The overview shows expeditionary MET sensing systems, such as the XMET, are able to provide long-term continuous observational records in remote and austere locations essential for regional spatiotemporal MET characterization.

Opportunities exist for autonomous underwater vehicles to optimize acoustic communications with knowledge of the sound channel, but implementation has traditionally been hindered by computationally expensive propagation models. This work addresses computational complexity by enabling machine learning to interpret environmental inputs to predict transmission loss (TL) outputs from a physics-based ray tracing model (BELLHOP). Feature representations to reduce bathymetric and sound speed profile (SSP) information through wavelet decomposition and empirical orthogonal analysis are explored, respectively. A decision tree machine learning algorithm is trained to learn the paths (depths and ranges) of acceptable transmission loss for a pair of acoustic modems within a given environment. Our test case is off the coast of Southern California, and SSPs are collected from a 3 year record sampled from an ocean mooring. Bathymetry data is based on high resolution multibeam data. TL field realizations are calculated as training and testing data for environments of varying bathymetry with frequency of 25kHz and range of 1km through the BELLHOP model. Results are shown with stationary and non-stationary emitters to demonstrate the effectiveness of this approach.

The development of a synthetic baseline navigation technique that self-localizes an autonomous underwater vehicle (AUV) using intermittent acoustic communications signals received by a single transducer is described, along with field results from in-ocean tests. The method uses the phase measurement at the output of a second-order phase-locked loop to create fine-scale pseudorange estimates in addition to, or in the absence of, a one-way travel time measurement based on the arrival time of the acoustic data packet. These range measurements are incorporated by an adaptive particle filter. This technique allows the vehicle navigation system to take advantage of multiple phase-derived range measurements made over the duration of a communication packet. These measurements, when incorporated with an appropriate filter and vehicle kinematic model, improve vehicle navigation at no additional cost in navigation-specific acoustic transmissions. This approach was demonstrated and evaluated with data collected at-sea using a REMUS 100 AUV (Hydroid, Inc., Pocasset, MA).

Waves overtop berms and seawalls along the shoreline of Imperial Beach (IB), CA when energetic winter swell and high tide coincide. These intermittent, few-hour long events flood low-lying areas and pose a growing inundation risk as sea levels rise. To support city flood response and management, an IB flood warning system was developed. Total water level (TWL) forecasts combine predictions of tides and sea-level anomalies with wave runup estimates based on incident wave forecasts and the nonlinear wave model SWASH. In contrast to widely used empirical runup formulas that rely on significant wave height and peak period, and use only a foreshore slope for bathymetry, the SWASH model incorporates spectral incident wave forcing and uses the cross-shore depth profile. TWL forecasts using a SWASH emulator demonstrate skill several days in advance. Observations set TWL thresholds for minor and moderate flooding. The specific wave and water level conditions that lead to flooding, and key contributors to TWL uncertainty, are identified. TWL forecast skill is reduced by errors in the incident wave forecast and the one-dimensional runup model, and lack of information of variable beach morphology (e.g., protective sand berms can erode during storms). Model errors are largest for the most extreme events. Without mitigation, projected sea-level rise will substantially increase the duration and severity of street flooding. Application of the warning system approach to other locations requires incident wave hindcasts and forecasts, numerical simulation of the runup associated with local storms and beach morphology, and model calibration with flood observations.

Fisheries management faces numerous monitoring and enforcement challenges that are becoming more complex as fish stocks are depleted; and illegal, unregulated, and unreported fishing becomes more sophisticated. For remote island nations, the challenges are compounded by a loosely understood association of pelagic stocks to the ocean environment, and the tyranny of distance in monitoring and surveilling large exclusive economic zones (EEZ). An approach to ocean conservation is establishing protected areas, with the Pacific island nation of Palau as a leader with the recently established National Marine Sanctuary, which closes 80% of their EEZ to commercial fishing in 2020. Here we present an EEZ-wide analysis of Palau commercial fishing over a 6-year period (2011–2016), and develop a system for predicting fishing activity accounting for oceanic variables, climate indices, and vessel flag. Linking pelagic habitat to fishing activity provides high-resolution decision aids for management, highlighting the need for EEZ-specific analyses in addressing fisheries.

Sea ice inhibits the development of wind‐generated surface gravity waves which are the dominant factor in upper ocean mixing and air‐sea fluxes. In turn, sea ice properties are modified by wave action. Understanding the interaction of ice and waves is important for characterizing both air‐sea interactions and sea ice dynamics. Current leading theory attributes wave attenuation primarily to scattering by ice floes. Here we use new in situ wave measurements to show that attenuation is dominated by dissipation with negligible effect by scattering. Time series of wave height in ice exhibit an ``on/off" behavior that is consistent with switching between two states of sea ice; a relatively unbroken state associated with strong damping (off), possibly caused by ice flexure, and very weak attenuation (on) across sea ice that has been broken up by wave action. Plain Language Summary Waves created by wind at the ocean surface are strongly attenuated when they travel across ice‐covered regions. Until now, this effect was thought to be the result of waves reflection off pieces of ice. Using new measurements of wave directions, we show that waves do not come for a broad range of directions, and scattering must be weak. Instead we find that attenuation is highly variable and related to the size of ice floes. We hypothesize that attenuation may be caused by cyclic deformation of the ice. When the waves are large enough to break the ice up, this deformation stops and the attenuation is much less. This finding is important for forecasting waves in ice‐infested waters as well as predicting seasonal sea ice extent.

Temperature and velocity measurements from 42 moorings were used to investigate the alongshore variability of nonlinear internal bores as they propagated across the central California inner shelf. Moorings were deployed September–October 2017 offshore of the Point Sal headland. Regional coverage was ~30 km alongshore and ~15 km across shore, spanning 9–100-m water depths. In addition to subtidal processes modulating regional stratification, internal bores generated complex spatiotemporal patterns of stratification variability. Internal bores were alongshore continuous on the order of tens of kilometers at the 50-m isobath, but the length scales of frontal continuity decreased to O(1 km) at the 25-m isobath. The depth-averaged, bandpass-filtered (from 3 min to 16 h) internal bore kinetic energy was found to be nonuniform along a bore front, even in the case of an alongshore-continuous bore. The pattern of along-bore variability varied for each bore, but a 2-week average indicated that was generally strongest around Point Sal. The stratification ahead of a bore influenced both the bore’s amplitude and cross-shore evolution. The data suggest that alongshore stratification gradients can cause a bore to evolve differently at various alongshore locations. Three potential bore fates were observed: 1) bores transiting intact to the 9-m isobath, 2) bores being overrun by faster, subsequent bores, leading to bore-merging events, and 3) bores disappearing when the upstream pycnocline was near or below middepth. Maps of hourly stratification at each mooring and the estimated position of sequential bores demonstrated that an individual internal bore can significantly impact the waveguide of the subsequent bore.

An autonomy system that allows a Boeing-Liquid Robotics Wave Glider, an unmanned surface vehicle (USV), to optimally track a Teledyne-Webb Research Slocum Glider, a buoyancy-driven, unmanned underwater vehicle (UUV), by exploiting environmental information is described, along with operational results from a 2-month experiment offshore Iceland. To conduct collocated oceanographic observations with this heterogeneous mix of unmanned assets, autonomy routines were developed to predict Slocum Glider surfacing locations and plan a Wave Glider path that minimized geographic separation between the two platforms. Prediction and planning engines are tuned to the unique performance characteristics of each platform. To ensure survivability of the Wave Glider in the extreme seas present at our study site, autonomy was developed to switch the Wave Glider control between mission objectives and platform survivability. The best estimate of a Slocum Glider's path during a flight segment is based on commanded and measured information from the Slocum Glider just before diving: average speed over ground (SOG), desired heading, depth averaged current (DAC), and the duration of the dive profile. For the Slocum, the DAC is computed from the offset between the expected and actual surfacing location during the previous dive, divided by the segment duration. This time-invariant treatment of the ocean current structure is found to be inadequate to accurately predict vehicle surfacing locations in regions where tidally-driven or other rapidly time-evolving flow patterns dominate. An improved model for predicting Slocum Glider trajectories in the Iceland Basin that incorporates real-time current measurements from a Wave Glider-mounted acoustic doppler current profiler (ADCP) and a short-term predictive tidal model is used to improve the estimate of depth average current. This capability is unique in that no additional information is explicitly exchanged between the vehicles (say from an acoustic modem). The improved path plan is uploaded to the USV in real-time via satcomms, allowing the surface asset to remain close to the UUV for coordinated scientific sampling.

The cross-shore evolution of nonlinear internal waves (NLIWs) from 8-m depth to shore was observed by a dense thermistor array and ADCP. Isotherm oscillations spanned much of the water column at a variety of periods. At times, NLIWs propagated into the surfzone, decreasing temperature by ≈1°C in 5 min. When stratification was strong, temperature variability was strong and coherent from 18- to 6-m depth at semidiurnal and harmonic periods. When stratification weakened, temperature variability decreased and was incoherent between 18- and 6-m depth at all frequencies. At 8-m depth, onshore coherently propagating NLIW events had associated rapid temperature drops (ΔT) up to 1.7°C, front velocity between 1.4 and 7.4 cm s−1, and incidence angles between −5° and 23°. Front position, ΔT, and two-layer equivalent height zIW of four events were tracked upslope until propagation terminated. Front position was quadratic in time, and normalized ΔT and zIW both decreased, collapsing as a linearly decaying function of normalized cross-shore distance. Front speed and deceleration are consistent with two-layer upslope gravity current scalings. During NLIW rundown, near-surface cooling and near-bottom warming at 8-m depth coincide with a critical gradient Richardson number, indicating shear-driven mixing.

Animals often select for habitats that increase their chance of survival by balancing the need to acquire food, reproduce and avoid predation. Perennial blooms of golden jellyfish (Mastigias papua etpisoni) are present in Jellyfish Lake, Palau, a popular tourist destination. Based on the species’ economic importance and unusual behavioural complexity, increased understanding of jellyfish habitat selection is necessary. We used a novel approach, a REMUS autonomous underwater vehicle, to quantify jellyfish distribution, abundance and habitat, and compared these findings to traditional methods. Midday acoustic surveys showed jellyfish distribution was patchy and the population resided mainly on the eastern side of the lake, as it is known that jellyfish migrate eastward towards the sun. Highest vertical densities of jellyfish were at 6–7 m, potentially to mitigate UV damage or photoinhibition of their photosymbionts, suggesting a coupling exists between their vertical distribution and water properties. Abundance estimates of jellyfish were ~2.75 and ~7.1 million (~2 million excluding bell diameters

Landfalling tropical cyclones (TC) generate extreme waves, introducing significant property, personal, and financial risks and damage. Accurate simulations of the sea state during these storms are used to support risk and damage assessments and the design of coastal structures. However, the TCs generate a complex surface gravity wave field as a result of the inherently strong temporal and spatial gradients of the wind forcing. This complexity is a significant challenge to model. To advance our understanding of the performance of these models on the eastern seaboard of the United States, we conduct an assessment of four hindcast products, three based on WAVEWATCH-III and the other using the Wave Modeling project, for six major landfall TCs between 2011–2019. Unique to our assessment was a comprehensive analysis of these hindcast products against an array of fixed wave buoys that generate high quality data. The analysis reveals a general tendency for the wave models to underestimate significant wave height (Hs) around the peak of the TC. However, when viewed on an individual TC basis, distinct Hs error patterns are evident. Case studies of hurricanes Sandy and Florence illustrate complex Hs bias patterns, likely resulting from various mechanisms including insufficient resolution, improper wind input and source term parameterization (e.g., drag coefficient), and omission of wave–current interactions. Despite the added challenges of simulating complex wave fields in shallow coastal waters, the higher resolution Wave Information Study and National Centers for Environmental Prediction (ST4 parameterization only) hindcasts perform relatively well. Results from this study illustrate the challenge of simulating the spatial and temporal variability of TC generated wave fields and demonstrate the value of in-situ validation data such as the north Atlantic buoy array.

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 (kx, ky, Δω) 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.

Observations of hydrodynamics along with optical and acoustical water characteristics in a tropical fringing reef environment reveal a distinct signature associated with flow characteristics and tidal conditions. Flow conditions are dominated by tidal forcing with an offshore component from the reef flat during ebb. Measurements span variable wave conditions enabling identification of wave effects on optical and acoustical water properties. High-frequency acoustic backscatter (6 MHz) is strongly correlated with tidal forcing increasing with offshore directed flow and modulated by wave height, indicating dominant hydrodynamic influence. Backscatter at 300 and 1200 kHz is predominantly diurnal suggesting a biological component. Optical backscatter is closely correlated with high-frequency acoustic backscatter across the range of study conditions. Acoustic backscatter frequency dependence is used along with changes in optical properties to interpret particle-size variations. Changes across wave heights suggest shifts in particle-size distributions with increases in relative concentrations of smaller particles for larger wave conditions. Establishing a connection between the physical processes of a fringing tropical reef and the resulting acoustical and optical signals allows for interpretation and forecasting of the remote sensing response of these phenomena over larger scales.

Passive acoustics is a tool to monitor behavior, distributions, and biomass of marine invertebrates, fish, and mammals. Typically, fixed passive acoustic monitoring platforms are deployed, using a priori knowledge of the location of the target vocal species. Here, we demonstrate the ability to conduct coastal surveys of fish choruses, spatially mapping their distributions with an autonomous surface vehicle. For this study, we used an autonomous Liquid Robotics Wave Glider SV3 equipped with a Remora-ST underwater acoustic recorder and hydrophone. The exploratory 15-day deployment transited through three marine reserves, resulting in approx. 200 hours of passive acoustic recordings, and revealed five distinct fish choruses from La Jolla to Capistrano Beach, CA (approx. 80 km separation), each with unique acoustic signatures. Choruses occurred in the evening hours, typically in the 40 to 1000 Hz band. There was a lack of both temporal and frequency partitioning amongst the choruses, but some choruses exhibited distinct spatial niches by latitude and water temperature. These results suggest that the mobility of the Wave Glider allows for persistent surveys and studies that otherwise may be too challenging or costly for stationary or ship-based sensors; a critical consideration for documenting biological activity over large spatiotemporal scales, or sampling of nearshore marine reserves.

The article of record as published may be found at https://doi.org/10.1029/2019GL083374 The heterogeneity in surface roughness caused by transient, nonlinear internal ocean waves is readily observed in coastal waters. However, the quantifiable impact this heterogeneity has on the marine atmospheric surface layer has not been documented. A comprehensive data set collected from a unique ocean platform provided a novel opportunity to investigate the interaction between this internal ocean process and the atmosphere. Relative to the background atmospheric flow, the presence of internal waves drove wind velocity and stress variance. Furthermore, it is shown that the wind gradient adjusts across individual wave fronts, setting up localized shear that enhanced the air-sea momentum flux over the internal wave packet. This process was largely mechanical, though secondary impacts on the bulk humidity variance and gradient were observed. This study provides the first quantitative analysis of this phenomenon and provides insights into submesoscale air-sea interactions over a transient, internal ocean feature. Plain Language Summary The ocean surface appears rough because the wind applies a tangential force to the water, which deforms the surface, generating short and steep waves. These small waves, in turn, increase the friction felt by the wind as it blows across the ocean surface, thereby setting up a feedback mechanism that physically links, or couples, the lower atmosphere to the upper ocean. However, our understanding of this interaction in the case of a heterogeneously rough ocean surface is limited. Using a unique ocean platform, we have collected a novel and complete data set demonstrating the impact internal ocean waves have on the near-surface atmospheric variability, through their modulation of the ocean surface roughness. The surface currents associated with internal waves generate bands of smooth and rough water that travel coherently with the internal wave packet. Our analysis shows that these transient surface features have a distinct and profound impact on the physical characteristics and structure of the near-surface atmosphere. In particular, internal waves enhance the wind forcing over the ocean and individual wave fronts significantly alter the vertical wind gradient. Our results provide the first documentation of the impact internal waves have on the atmosphere and suggest that these dynamics should be accounted for when studying fine-scale atmosphere-ocean interactions and the impact internal waves have on the marine environment. This research was funded by the Office of Naval Research (ONR) Grant N0001418WX01087 under its Multidisciplinary University Research Initiative (MURI). This research was funded by the Office of Naval Research (ONR) Grant N0001418WX01087 under its Multidisciplinary University Research Initiative (MURI).

Academic, government, and private organizations from around the globe have established High Frequency radar (hereinafter, HFR) networks at regional or national levels. Partnerships have been established to coordinate and collaborate on a single global HFR network (http://global-hfradar.org/ ). These partnerships were established in 2012 as part of the Group on Earth Observations (GEO) to promote HFR technology and increase data sharing among operators and users. The main product of HFR networks are continuous maps of ocean surface currents within 200 km of the coast at high spatial (1-6 km) and temporal resolution (hourly or higher). Cutting-edge remote sensing technologies are becoming a standard component for ocean observing systems, contributing to the paradigm shift towards ocean monitoring. In 2017 the Global HFR Network was recognized by the Joint Technical WMO-IOC Commission for Oceanography and Marine Meteorology (JCOMM) as an observing network of the Global Ocean Observing System (GOOS). In this paper we will discuss the development of the network as well as establishing goals for the future. The U.S. High Frequency Radar Network (HFRNet) has been in operation for over thirteen years, with radar data being ingested from 31 organizations including measurements from Canada and Mexico. HFRNet currently holds a collection from over 150 radar installations totaling millions of records of surface ocean velocity measurements. During the past 10 years in Europe, HFR networks have been showing steady growth with over 60 stations currently deployed and many in the planning stage. In Asia and Oceania countries, more than 110 radar stations are in operation. HFR technology can be found in a wide range of applications: for marine safety, oil spill response, tsunami warning, pollution assessment, coastal zone management, tracking environmental change, numerical model simulation of 3-dimensional circulation, and research to generate new understanding of coastal ocean dynamics, depending mainly on each country's coastal sea characteristics. These radar networks are examples of national inter-agency and inter-institutional partnerships for improving oceanographic research and operations. As global partnerships grow, these collaborations and improved data sharing enhances our ability to respond to regional, national, and global environmental and management issues.

The Coastal Data Information Program (CDIP) is an operational wave monitoring and prediction program based at Scripps Institution of Oceanography, part of the University of California, San Diego. CDIP maintains an array of Datawell™ Waverider directional wave buoys in US and territorial waters. Primary funding is provided by the US Army Corps of Engineers, with additional funding from the State of California, the U.S. Navy, various federal agencies, and the private sector. In recent years, collaborations with NOAA's regional Integrated Ocean Observing System (IOOS) programs have further expanded the CDIP array, which presently includes stations along the Atlantic and Pacific mainland coasts, plus Alaska, Hawaii, the South Pacific, Lake Superior, the Gulf Coast of Florida, and Puerto Rico. Collecting reliable wave buoy data begins with using high quality instruments and maintaining them properly. Working closely with the buoy manufacturer, CDIP has been expanding and improving pre-and post-deployment sensor validation capabilities. Comprehensive and well-documented quality control measures are then applied to the real-time data. Advanced diagnostics and automated messaging have been developed to provide watch keepers with real time notice of any environmental conditions, vessel interference, or technical malfunctions that invalidate the data. Newly available sensors, providing surface current and air temperature data in addition to the waves and sea surface temperature, have been beta-tested and incorporated. The current data have been applied to better understand mooring response to strong currents and effects on wave data. Lessons have been learned and applied regarding the effects of biofouling and anti-fouling measures. As a result, reliable data continue to be collected that characterize long-term wave and sea surface temperature climate, as well as wave conditions generated by major storm events such as Hurricane Florence in 2018. The growing buoy array and evolution of computing resources prompted CDIP to overhaul its data processing, metadata handling, and web hosting infrastructure in order to improve on usability, scalability, reliability, portability and security. Cloud based services are now utilized, and a variety of technologies such as the THREDDS data server, Django™ Web Framework, Django™ Rest Framework, and Docker® containers have been adopted. These forward-looking upgrades will ensure the robustness of CDIP's data management capabilities, wave model output availability, and web presence for years to come.