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Mario Brito, Gwyn Griffiths, James Ferguson, David Hopkin, Richard Mills, Richard Pederson, and Erin MacNeil

risk management is informed by assessments provided by a group of experts, where the final assessment is one that represents the group judgment. Individual expert judgments can be aggregated mathematically or behaviorally to reach this group judgment. Previously, expert judgments have been mathematically aggregated using the linear opinion pool, where experts have been kept separate during the elicitation ( Clemen and Winkler 1999 ; Griffiths et al. 2009 ; Brito et al. 2010 ). There are different

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Christopher A. Roseman and Brian M. Argrow

the safety management system manual ( Bristol 2019 ). The manual describes the five steps of the “DIAAT” process: describing the system, identifying hazards, then analyzing, assessing, and treating the risk. Describing the system involves outlining the scope of the risk analysis being performed. After the system is described, hazards within the risk assessment scope must be identified. A hazard is a condition that could foreseeably cause or contribute to an accident. Analyzing hazard risk is

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Mario P. Brito and Gwyn Griffiths

of failure or its likelihood of occurrence ( Subramanian et al. 1996 ). In this paper failure mitigation (risk mitigation) is achieved by reducing the likelihood of failure occurrence. While both Brito et al. (2014) and Rudnick et al. (2016) give emphasis to the role of mitigation through failure prevention and correction, neither presents an analytical framework for updating the risk profile based on a structured assessment of the understanding and elimination of failure modes and the

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Mario Brito, David Smeed, and Gwyn Griffiths

underwater vehicle operations in extreme environments . Risk Anal. , 30 , 1771 – 1788 , doi: 10.1111/j.1539-6924.2010.01476.x . Brito, M. P. , Griffiths G. , Ferguson J. , Hopkin D. , Mills R. , Pederson R. , and MacNeil E. , 2012 : A behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments . J. Atmos. Oceanic Technol. , 29 , 1689 – 1703 , doi: 10.1175/JTECH-D-12-00005.1 . Collett, D. , 2003 : Modelling Survival Data in Medical Data

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Eric Gilleland

average only once every 100 years, it is important to utilize the correct statistical analyses in order to accurately portray the risks of these types of events; as well as their uncertainty information. In what follows, it is helpful to denote a random sample of variables X 1 , …, X n , to represent a physical phenomena of interest such as 24-h accumulated rainfall, daily maximum temperature, and streamflow. Theoretical, asymptotic arguments give justification for modeling maxima taken over very

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Scott A. Stephens, Robert G. Bell, Douglas Ramsay, and Nigel Goodhue

1. Introduction Coastal inundation events damage infrastructure and property, cause economic loss and disruption, and put lives at risk. Forecasts of the timing and magnitude of coastal hazards allow coastal or emergency managers to design appropriate responses in advance that can significantly reduce risk. Here we discuss a method for the development of high-water calendars to provide alerts for dates of very high tides compounded by high background mean sea level anomaly (MSLA), as a

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Lohitzune Solabarrieta, Sergey Frolov, Mike Cook, Jeff Paduan, Anna Rubio, Manuel González, Julien Mader, and Guillaume Charria


Since January 2009, two long-range high-frequency (HF) radar systems have been collecting hourly high-spatial-resolution surface current data in the southeastern corner of the Bay of Biscay. The temporal resolution of the HF radar surface currents permits simulating drifter trajectories with the same time step as that of real drifters deployed in the region in 2009. The main goal of this work is to compare real drifter trajectories with trajectories computed from HF radar currents obtained using different methods, including forecast currents. Open-boundary modal analysis (OMA) is applied to the radar radial velocities and then a linear autoregressive model on the empirical orthogonal function (EOF) decomposition of an historical data series is used to forecast OMA currents. Additionally, the accuracy of the forecast method in terms of the spatial and temporal distribution of the Lagrangian distances between observations and forecasts is investigated for a 4-yr period (2009–12). The skills of the different HF radar products are evaluated within a 48-h window. The mean distances between real trajectories and their radar-derived counterparts range from 4 to 5 km for real-time and forecast currents after 12 hours of simulations. The forecast model improves persistence (i.e., the simulations obtained by using the last available OMA fields as a constant variable) after 6 hours of simulation and improves the estimation of trajectories up to 28% after 48 hours. The performance of the forecast is observed to be variable in space and time, related to the different ocean processes governing the local ocean circulation.

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Duick T. Young, Lee Chapman, Catherine L. Muller, Xiao-Ming Cai, and C. S. B. Grimmond

) ; Whiteman et al. 2000 ). Data communication uses standard Institute of Electrical and Electronics Engineers (IEEE) 802.11 b/g 2.4-GHz Wi-Fi at bit rates of up to 11 Mbps. When located on existing Wi-Fi hotspots/networks, the sensor utilizes limited bandwidth (user datagram protocol packets ≈2 kB) and poses minimal risk to security, as sensor to Internet data packets are secured using the latest encryption and all communications are sensor initiated. Data packets are transmitted periodically (user

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Sandy Dance, Elizabeth Ebert, and David Scurrah

systems by producing automated objective probabilistic threat areas based on the error characteristics of the estimated storm motion from one or more sources. This probabilistic threat area can be combined with probabilistic products from other automated systems. Additionally, forecasters can manipulate storm motion, size, and other properties to issue better warnings to provide more effective risk assessment and decision making. Such an approach is being pursued by the U.S. National Weather Service

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Matthias Lankhorst


It is becoming increasingly recognized that the eddy field plays an important—possibly dominating—role for oceanic motions in many aspects (e.g., transport of properties and risk assessment in the case of extreme events). This motivates the study of individual eddy events. In the Lagrangian coordinate system, vorticity possibly associated with eddies appears in two forms: as shear vorticity between neighboring particles, and as curvature of the trajectory of a single particle. Typical field experiments in physical oceanography using surface drifters or subsurface floats do not reach data densities high enough to produce enough encounters of drifters to calculate shear vorticity between them. However, curvature in individual tracks is easily observed. This study presents a methodology that extracts segments from within a trajectory that are “looping,” which will be interpreted as a drifter being caught in an eddy. The method makes use of autoregressive processes, a simple type of stochastic processes, which easily enables a fit to the nonperfectly shaped trajectory data usually expected from field experiments. These processes also deliver frequency and persistence of the detected eddies by a very simple calculation, which makes the methodology highly suited for automatized scanning of larger datasets.

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