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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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Amanda M. Walker, David W. Titley, Michael E. Mann, Raymond G. Najjar, and Sonya K. Miller

. Applications of statistics and neural networks led to the development of the Hurricane Impact Level model ( Pilkington and Mahmoud 2016 ), which uses a storm’s pressure, winds, storm surge, and other variables to create a measure of economic impact. This model shows promise for both operational use ( Pilkington and Mahmoud 2017a ) and for the theoretical assessment of risk ( Pilkington and Mahmoud 2017b ), but it only offers analysis of potential economic loss on an overall storm-level scale. It is not

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John A. Hart and Ariel E. Cohen

state of the atmosphere. However, they did identify the possibility of using diagnostic data to explicitly express probabilistic information in severe thunderstorm forecasting, in which the combination of occurrences of events and nonevents (null cases) is used to derive the probability of event occurrence. This notion is the foundation of a companion paper, in which Hart and Cohen (2016) developed the Statistical Severe Convective Risk Assessment Model (SSCRAM). This system couples 9 yr of

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Mark S. Allen and F. Anthony Eckel

Using Weather and Climate Forecasts . The National Academies Press, 112 pp. [Available online at .] National Weather Service/Climate Prediction Center , cited 2011 : Climatological and weather linkage . [Available online at .] Palmer, T. N. , 2002 : The economic value of ensemble forecasts as a tool for risk assessment: From days to decades . Quart. J. Roy. Meteor. Soc. , 128 , 747 – 774 . Richardson, D. S. , 2000

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Julie L. Demuth, Rebecca E. Morss, Jeffrey K. Lazo, and Douglas C. Hilderbrand

2011 ) but also because it can have economic implications ( Sutter and Erickson 2010 ). Thus, explicitly indicating when a weather threat is in effect can help people understand and respond during the time period that they are actually at risk. In summary, the research reported upon here illustrates that, in general, the design of forecast information influences how recipients attend to and interpret the information, which has implications for their weather risk assessment. More specifically

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Mark D. Powell, Eric W. Uhlhorn, and Jeffrey D. Kepert

azimuthal variation in the SWF, FBV mention a difference of 0.04 in the along-the-sonde surface wind factor between the left and right sides of the storm. Their value is smaller than PUK ’s 0.10, which is more consistent with theoretical arguments ( Kepert 2001 ; Kepert and Wang 2001 ) and previous case studies ( Kepert 2006 ; Schwendike and Kepert 2008 ). The reasons for the differences are not entirely clear but likely include that their assessment of asymmetry suffers from the limitations noted

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S. J. Murray, A. D. Smith, and J. C. Phillips

storm and the rate of rising river levels. Flood hazard assessments offer an alternative means of rapidly monitoring peak flood risk and may be useful for informing the response to flash floods, particularly during the development of an event. These are typically based on a simple scoring system whereby the contributions to a potential flood from each of its most critical components are regularly evaluated in terms of their “severity.” However, there can be weaknesses with such methods and their

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Melanie Bieli, Adam H. Sobel, Suzana J. Camargo, and Michael K. Tippett

implications for risk assessment ( Loridan et al. 2015 ), ET has received little attention in the development of TC hazard models for the (re)insurance industry (e.g., AIR Worldwide 2015 ) or in academia (e.g., Emanuel et al. 2006 ; Hall and Jewson 2007 ; Lee et al. 2018 ). The primary output of a hazard model is the annual probability of a storm intensity exceeding a given threshold at a specific location. Hazard models used in the industry (so-called catastrophe, or “cat” models) additionally

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F. Anthony Eckel, Mark S. Allen, and Matthew C. Sittel

spectrum of possible outcomes but the spectrum itself is also unclear. Consider the difference between betting at a roulette table versus betting on a horse race. In roulette, a bet is risky but the possible outcomes and the uncertainty of winning are known precisely (e.g., chance of the ball landing on any single number is and pays 35:1). In horse race betting, the odds of a particular horse winning are estimated so not only does a bet carry risk, but that risk is vague (i.e., ambiguous). This paper

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Valdir Innocentini, Ernesto Caetano, and Jonas Takeo Carvalho

.0.CO;2 . Machado, A. A. , Calliari L. J. , Melo E. , and Klein A. H. F. , 2010 : Historical assessment of extreme coastal sea state conditions in southern Brazil and their relation to erosion episodes . Pan-Amer. J. Aquat. Sci. , 5 , 277 – 286 . Paice, N. , 1998 : Autumnal funnel clouds over west Hampshire: A precursor to wintertime tornadoes . Weather , 53 , 419 – 424 , doi:10.1002/j.1477-8696.1998.tb06360.x . Portilla, J. , Ocampo-Torres F. J. , and Monbaliu J. , 2009

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