The OceanGliders program started in 2016 to support active coordination and enhancement of global glider activity. OceanGliders contributes to the international efforts of the Global Ocean Observation System (GOOS) for Climate, Ocean Health, and Operational Services. It brings together marine scientists and engineers operating gliders around the world: (1) to observe the long-term physical, biogeochemical, and biological ocean processes and phenomena that are relevant for societal applications; and, (2) to contribute to the GOOS through real-time and delayed mode data dissemination. The OceanGliders program is distributed across national and regional observing systems and significantly contributes to integrated, multi-scale and multi-platform sampling strategies. OceanGliders shares best practices, requirements, and scientific knowledge needed for glider operations, data collection and analysis. It also monitors global glider activity and supports the dissemination of glider data through regional and global databases, in realtime and delayed modes, facilitating data access to the wider community. OceanGliders currently supports national, regional and global initiatives to maintain and expand the capabilities and application of gliders to meet key global challenges such as improved measurement of ocean boundary currents, water transformation and storm forecast.

Since the motion of autonomous underwater vehicles is affected by ambient flow, knowledge of an environmental flow field can be used to improve the navigation of autonomous underwater vehicles. Due to imperfect knowledge of flow, the actual trajectory of an autonomous underwater vehicle deviates from the predicted trajectory. The difference between the actual and predicted trajectories is referred to as the motion-integration error, providing information of flow along the vehicle trajectory. Inspired by computerized tomography, this paper proposes motion tomography, a tomographic method for creating a fine-grid spatial map of flow based on the motion-integration error. While typical computerized tomography is a linear problem, motion tomography is a nonlinear problem because of unknown nonlinear trajectories of autonomous underwater vehicles and the dependency of the trajectories on the flow field. Therefore, motion tomography employs an iterative process consisting of two alternating steps: Trajectory tracing and flow field estimation. Starting from an initial guess of the flow field, in the trajectory tracing step, unknown nonlinear vehicle trajectories are estimated. Then, using the estimated vehicle trajectories, a spatial map of flow is constructed through either the non-parametric or parametric flow field estimation. The error bound for trajectory tracing is computed and the convergence of both the non-parametric and parametric flow field estimation algorithms is proved. Simulation and experimental data are analyzed to evaluate the performance of motion tomography when subject to changing vehicle speed and flow variability.

In recent years, collecting scientific data from ocean environments has been increasingly undertaken by underwater gliders. For better navigation performance, the influence of flow on the navigation of underwater gliders may be significantly reduced by estimating flow velocity. However, methods for estimating flow do not always account for spatial and temporal changes in the flow field, leading to poor navigation in complex ocean environments. To improve navigation accuracy in such environmental conditions, this paper studies an approach for the real-time guidance of underwater gliders assisted by predictive ocean models. This study is motivated by glider deployments conducted from January to April 2012 and in February 2013 in Long Bay, South Carolina, where the ocean currents are characterized by strong tides and a stronger alongshore current, the Gulf Stream. The flow speed here often exceeds the forward speed of the glider. To deal with such a challenge, a computationally efficient method of depth-averaged ocean current modeling was developed. The method adjusts the ocean model based on the most recent ocean observations from gliders as feedback, and flow predictions from the model are incorporated into path planning, which produces waypoints. The entire process of flow prediction, path planning, and waypoint computation is performed off-board the gliders in real time by the glider navigation support system, the Glider-Environment Network Information System (GENIoS). This paper presents the setup and method for the glider navigation strategy applied to the Long Bay deployments. For demonstration, the performance of the method described here is compared to that of the default method implemented in the built-in glider navigation system.

Defining the vertical depth average of measured currents to be barotropic is a widely used method of separating barotropic and baroclinic tidal currents in the ocean. Away from the surface and bottom boundary layers, depth-averaging measured velocity is an excellent estimate of barotropic tidal flow, and internal tidal dynamics can be well represented by the difference between the measured currents and their depth average in the vertical. However, in shallow and/or energetic tidal environments such as the shelf of the South Atlantic Bight (SAB), bottom boundary layers can occupy a significant fraction of the water column, and depth averaging through the bottom boundary layer can overestimate the barotropic current by several tens of centimeters per second near bottom. The depth-averaged current fails to capture the bottom boundary layer structure associated with the barotropic tidal signal, and the resultant estimate of baroclinic tidal currents can mimic a bottom-trapped internal tide.Complex empirical orthogonal function (CEOF) analysis is proposed as a method to retain frictional effects in the estimate of the barotropic tidal currents and allow an improved determination of the baroclinic currents. The method is applied to a midshelf region of the SAB dominated by tides and friction to quantify the effectiveness of CEOF analysis to represent internal structure underlying a strong barotropic signal in shallow water. Using examples of synthesized and measured data, EOF estimates of the barotropic and baroclinic modes of motion are compared to those made using depth averaging. The estimates of barotropic tidal motion using depth-averaging and CEOF methods produce conflicting predictions of the frequencies at which there is meaningful baroclinic variability. The CEOF method preserves the frictional boundary layer as part of the barotropic tidal current structure in the gravest mode, providing a more accurate representation of internal structure in higher modes. The application of CEOF techniques to isolate internal structure co-occurring with highly energetic tidal dynamics in shallow water is a significant test of the method. Successful separation of barotropic and baroclinic modes of motion suggests that, by fully capturing the effects of friction associated with the barotropic tide, CEOF analysis is a viable technique to facilitate examination of the internal tide in similar environments.

The processes through which people learn to live with CFS/ME are poorly understood and have not been rigorously explored within the literature. Semi-structured interviews were conducted with eight women and analysed using interpretative phenomenological analysis. Participants initially described being 'overwhelmed' by CFS/ME. Attempts at seeking help were unsatisfactory and participants described feeling let down and disbelieved. Participants reacted to this by identifying types of 'self-help' and assertively taking more responsibility for their illness and its treatment. Acquiring social support and greater knowledge were key mediating factors in the emergence of control and acceptance. The relevance of the themes to existing research and the implications for clinical practice are considered.

The Processes driving Exchange At Cape Hatteras (PEACH) program seeks to better understand seawater exchanges between the continental shelf and the open ocean near Cape Hatteras, North Carolina. This location is where the Gulf Stream transitions from a boundary-trapped current to a free jet, and where robust along-shelf convergence brings cool, relatively fresh Middle Atlantic Bight and warm, salty South Atlantic Bight shelf waters together, forming an important and dynamic biogeographic boundary. The magnitude of this convergence implies large export of shelf water to the open ocean here. Background on the oceanography of the region provides motivation for the study and gives context for the measurements that were made. Science questions focus on the roles that wind forcing, Gulf Stream forcing, and lateral density gradients play in driving exchange. PEACH observational efforts include a variety of fixed and mobile observing platforms, and PEACH modeling included two different resolutions and data assimilation schemes. Findings to date on mean circulation, the nature of export from the southern Middle Atlantic Bight shelf, Gulf Stream variability, and position variability of the Hatteras Front are summarized, together with a look ahead to forthcoming analyses.

Simultaneous ADCP profile measurements are compared over a 2-month period in late 2003. One set of measurements comes from a National Data Buoy Center (NDBC) buoy-mounted ADCP, the other from a bottom-mounted, upward-looking ADCP moored roughly 500 m from the buoy. The study was undertaken to evaluate the proficiency of an experimental configuration by NDBC; unfortunately, the ADCP was not optimally configured. The higher temporally and vertically resolved bottom-mounted ADCP data are interpolated in time and depth to match the buoy-mounted ADCP measurements. It is found that the two ADCP measurements are significantly different. The buoy-mounted measurements are affected by highfrequency (Ͻ10 h period) noise that is vertically coherent throughout the profiles. This noise results in autospectra that are essentially white, unlike the classic red spectra formed from the bottom-mounted ADCP observations. The spectra imply a practical noise floor of 0.045 m s Ϫ1 for the buoy-mounted system. Contamination by surface waves is the likely cause of this problem. At tidal frequencies the buoy-mounted system underestimates major axis tidal current magnitude by 10%-40%; interference from the buoy chain and/or fish or plankton are considered the most likely cause of the bias. The subtidal velocity field (periods greater than 40 h) is only partially captured; the correlation coefficient for the east-west current is 0.49 and for the north-south current is 0.64.

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