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Jerome D. Fast, B. Lance O'steen, and Robert P. Addis

626 JOURNAL OF APPLIED METEOROLOGY VOLUME34Advanced Atmospheric Modeling for Emergency Response JEROME D. FAST, B. LANCE O'STEEN, AND ROBERT P. ADDISSavannah River Technology Center, Westinghouse Savannah River Company, ,4iken, South Carolina(Manuscript received 7 September 1993, in final form 14 February 1994)ABSTRACT Atmospheric transport and diffusion models are an important part

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Frauke Hoss and Paul Fischbeck

waves, and snow and ice storms. The participating EMs were asked to select the one hazard they found most difficult to respond to in the last 10 years, and they subsequently answered the questions about forecast use for that hazard. In this way, it is possible to examine whether the emergency managers’ environments have had any impact on the survey responses and the use of weather information. The survey was distributed online in April and May 2014 to EMs throughout the United States. The survey was

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Roger G. Carter and Robert E. Keislar

). The algorithm described in this paper applies analog forecasting to the generation of mesoscale wind fields for the purpose of forecasting transport and diffusion in an emergency response setting. The algorithm operates on data collected from the Eastern Idaho Mesonet ( Fig. 1 ). This network consists of 32 meteorological towers located on and around the Idaho National Engineering and Environmental Laboratory (INEEL). The towers are spread over an area nearly 200 km long and 100 km wide. Wind

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Noah Dormady, Anthony Fasano, Alfredo Roa-Henriquez, Drew Flanagan, William Welch, and Dylan Wood

Research evaluating household evacuation decisions in response to hurricane evacuation orders is extensive ( Baker 1991 ; Dash and Gladwin 2007 ; Thompson et al. 2017 ). However, very little is known about how those evacuation orders are made by emergency managers (EMs) and other public safety professionals. When a hurricane is approaching, what explains the timing of voluntary evacuation orders? When mandatory evacuation orders are issued, why are some communities evacuated and others not

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Heidi Kreibich, Paul Hudson, and Bruno Merz

of a constant term (α), β are vectors of coefficient terms, H i is a vector of hydrological/flood-related factors as a proxy of risk, EM i is a vector of emergency measures and response factors, FE is a vector of flood-related experiences, and SES i is a vector of socioeconomic status factors, while ε i represents the error term. A logit regression model is employed because the dependent variable (knowing what to do) is binary (i.e., 0 or 1). A full list of variables is presented in

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Christopher T. Emrich and Susan L. Cutter

. Schmidtlein, M. C. , Deutsch R. , Piegorsch W. W. , and Cutter S. L. , 2008 : A sensitivity analysis of the Social Vulnerability Index . Risk Anal. , 28 , 1099 – 1114 . Scott, P. A. , Stone D. A. , and Allen M. R. , 2004 : Human contribution to the European heatwave of 2003 . Nature , 432 , 610 – 614 . South Carolina Emergency Management Division , cited 2009 : South Carolina drought response plan, appendix 10 of the South Carolina emergency operations plan . [Available online at

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Katerina Papagiannaki, Vassiliki Kotroni, Kostas Lagouvardos, Isabelle Ruin, and Antonis Bezes

meteorological stations was used to represent the space–time characteristics of the rainfall episode in each subarea. The effects of the episode were described by two datasets: (i) the emergency impact measured by the number of citizens’ calls for help to the emergency line of the fire service; and (ii) the individual coping responses, measured through the analysis of an online survey aiming at collecting perceptual and behavioral responses of the witnesses of the rainfall episode. The rest of the paper is

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Kelly E. McCusker, David S. Battisti, and Cecilia M. Bitz

agents. Third, the spatial distribution of stratospheric sulfate aerosol versus carbon dioxide is not identical, with carbon dioxide being well mixed in the troposphere and a sulfate layer limited to the lower stratosphere. The latter effect, we will show, has profound implications for the response of the climate—especially for the effectiveness of geoengineering to avoid the two polar emergencies that we consider here. Even if the negating effect of a sulfate layer was perfect, there is also some

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Charles M. Kuster, Pamela L. Heinselman, Jeffrey C. Snyder, Katie A. Wilson, Douglas A. Speheger, and James E. Hocker

1. Introduction Many public safety officials (e.g., emergency managers and first responders) use weather-radar data to support decisions ranging from directing storm spotters to coordinating search and rescue efforts (e.g., Morris et al. 2002 ; Baumgart et al. 2008 ; Weaver et al. 2014 ). In a national survey of approximately 900 public safety officials working in the United States, Weaver et al. (2014) found that 30.3% of respondents would activate their emergency-response system based

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Dale A. Morris, Kenneth C. Crawford, Kevin A. Kloesel, and Gayland Kitch

. Even in “more normal” periods, “nonsevere” weather regularly affects human activities such as outdoor entertainment events and outside work crews. In addition, responses to wildfires, hazardous materials incidents, and acts of terrorism are impacted by environmental conditions. Before OK-FIRST, Oklahoma was a microcosm of the entire country in how agencies responded to emergencies in that local decision-support systems generally suffered from a near-complete lack of current and relevant

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