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1. Introduction In this paper we investigate the use of public appeals to manage electric energy demand in severe-weather emergencies. We focus on a severe cold weather event that blanketed the central area of the United States in February 2021. The storm almost completely covered Southwest Power Pool’s (SPP) 14-state balancing authority area, stretching from Texas to North Dakota. In response to predicted service interruptions, SPP issued a series of energy emergency alerts that mandated
1. Introduction In this paper we investigate the use of public appeals to manage electric energy demand in severe-weather emergencies. We focus on a severe cold weather event that blanketed the central area of the United States in February 2021. The storm almost completely covered Southwest Power Pool’s (SPP) 14-state balancing authority area, stretching from Texas to North Dakota. In response to predicted service interruptions, SPP issued a series of energy emergency alerts that mandated
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
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
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
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
, preserve, and share the stories of resilience of tornado survivors as well as to provide inspiration to help individuals or communities at risk from similar threats in the future. The study is expected to add to the larger historical narrative of tornado risk, expand the scholarly understanding of disaster experience and the nature of disaster resilience, and inform better emergency management practices for reducing disaster risks and vulnerability. 2. Literature review a. Resilience The term
, preserve, and share the stories of resilience of tornado survivors as well as to provide inspiration to help individuals or communities at risk from similar threats in the future. The study is expected to add to the larger historical narrative of tornado risk, expand the scholarly understanding of disaster experience and the nature of disaster resilience, and inform better emergency management practices for reducing disaster risks and vulnerability. 2. Literature review a. Resilience The term
= 0.28*) and purchasing home insurance ( r = 0.36*). Other demographics like age, being female, tenure, and house composition variables are not significantly correlated with risk perceptions or adjustment intentions much. With respect to coping appraisals of response efficacy, protecting person effectively is significantly correlated with each of the adjustment intentions, especially for intentions of signing up for smartphone alerts ( r = 0.41**), developing an emergency plan ( r = 0
= 0.28*) and purchasing home insurance ( r = 0.36*). Other demographics like age, being female, tenure, and house composition variables are not significantly correlated with risk perceptions or adjustment intentions much. With respect to coping appraisals of response efficacy, protecting person effectively is significantly correlated with each of the adjustment intentions, especially for intentions of signing up for smartphone alerts ( r = 0.41**), developing an emergency plan ( r = 0
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
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
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
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
). 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
). 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
. 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
. 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
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
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
