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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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Julie L. Demuth, Rebecca E. Morss, Leysia Palen, Kenneth M. Anderson, Jennings Anderson, Marina Kogan, Kevin Stowe, Melissa Bica, Heather Lazrus, Olga Wilhelmi, and Jen Henderson

1. Introduction The risks posed by many natural hazards are dynamic in that the threat and information available about it evolve. When a hurricane threatens a coastline, for example, its position and intensity changes, and forecast and preparedness information is refined as the storm approaches. People’s assessments of and responses to natural hazard risks are also dynamic, as individuals process information and interact with each other to communicate about, interpret, and respond to the

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Kelley M. Murphy, Eric C. Bruning, Christopher J. Schultz, and Jennifer K. Vanos

assessment in the International Electrotechnical Commission Standard for Lightning Protection, Part II (IEC 62305-2:2010; referred to herein as IEC62305) was used ( International Electrotechnical Commission 2010 ). The risk assessment framework of IEC62305 is intended to produce an annual value of lightning risk for structures. This risk value helps to determine whether or not protection measures are required in order to reduce losses due to lightning. For this research, the risk assessment was adapted

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John A. Hart and Ariel E. Cohen

Risk Assessment Model (SSCRAM). The comparisons of meteorological parameters (e.g., measures of buoyancy and vertical shear) to past severe weather occurrences provide a background for anticipating severe storm risk based on initial environmental information (e.g., Rasmussen and Blanchard 1998 ; Thompson et al. 2003 , 2007 ; Craven and Brooks 2004 ). These studies provide background for forecasters to anticipate future severe weather occurrences based on past ones using meteorological parameter

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Timothy M. Hall and Stephen Jewson

system that allows direct comparison between the models. We find that bias in the track model is more than compensated for on most regional-scale coast segments by the reduction of sampling error compared to the local model. This is the first time, to our knowledge, that the use of basinwide statistical track models has been rigorously justified for use in TC landfall risk assessment. 2. Local model The local model makes predictions of future TC landfall rates on a segment of coastline using only

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Kelly Mahoney, Chesley McColl, Douglas M. Hultstrand, William D. Kappel, Bill McCormick, and Gilbert P. Compo

Accurate assessment of flood risk is critical to protecting lives and property worldwide. The design and safe operation of dams, levees, culverts, bridges, storm drainage infrastructure, and many nuclear facilities are informed by estimates of an “upper bound” of possible precipitation. In particular, dams and nuclear facilities in populated areas are often referred to as “critical” or “high hazard” due to the risk to life and property a failure presents. These structures must be built to

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Stéphane Hallegatte

–237 . Emanuel , K. , C. DesAutels , C. Holloway , and R. Korty , 2004 : Environmental control of tropical cyclone intensity. J. Atmos. Sci. , 61 , 843 – 858 . Emanuel , K. , S. Ravela , E. Vivant , and C. Risi , 2006 : A statistical deterministic approach to hurricane risk assessment. Bull. Amer. Meteor. Soc. , 87 , 299 – 314 . Fankhauser , S. , 1995 : Valuing Climate Change: The Economics of the Greenhouse . Earthscan Publications, 180 pp . Hallegatte , S. , 2006 : A

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Paul A. T. Higgins and Jonah V. Steinbuck

1. Introduction Understanding the potential consequences of climate change to society is extremely challenging because climate impacts will depend on a multitude of contributing factors that interact in complicated ways and that are characterized by varying degrees of uncertainty ( Moss 2011 ). For example, the risk assessment process must synthesize information from numerous disciplines that span the physical sciences (e.g., how much and how fast climate changes), natural sciences (e.g., how

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Stephen M. Strader, Alex M. Haberlie, and Alexandra G. Loitz

, and vulnerability? How does the combination of risk, exposure, and vulnerability influence potential tornado impacts and severity within CWAs? Last, how might this information be used by NWS forecasters, Integrated Warning Teams (IWT), and the Warning Decision Training Division (WDTD) to improve forecaster knowledge, mitigation strategies, and community resilience-building efforts? a. Climatological tornado risk, exposure, and vulnerability assessments Several previous studies have examined

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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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