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Abstract
Hurricane Ike made landfall near Galveston, Texas, on 13 September 2008 as a large category 2 storm that generated significant storm surge and flooding. This article presents findings from an empirical case study of Texas coastal residents’ perceptions of hurricane risk, protective decision making, and opinions of hurricane forecasts related to Hurricane Ike. The results are based on data from interviews with 49 residents affected by Hurricane Ike, conducted approximately five weeks after landfall. While most interviewees were aware that Ike was potentially dangerous, many were surprised by how much coastal flooding the hurricane caused and the resulting damage. For many—even long-time residents—Ike was a learning experience. As the hurricane approached, interviewees and their households made complex, evolving preparation and evacuation decisions. Although evacuation orders were an important consideration for some interviewees, many obtained information about Ike frequently from multiple sources to evaluate their own risk and make protective decisions. Given the storm surge and damage Ike caused, a number of interviewees believed that Ike’s classification on the Saffir–Simpson scale did not adequately communicate the risk Ike posed. The “certain death” statement issued by the National Weather Service helped convince several interviewees to evacuate. However, others had strong negative opinions of the statement that may negatively influence their interpretation of and response to future warnings. As these findings indicate, empirical studies of how intended audiences obtain, interpret, and use hurricane forecasts and warnings provide valuable knowledge that can help design more effective ways to convey hurricane risk.
Abstract
Hurricane Ike made landfall near Galveston, Texas, on 13 September 2008 as a large category 2 storm that generated significant storm surge and flooding. This article presents findings from an empirical case study of Texas coastal residents’ perceptions of hurricane risk, protective decision making, and opinions of hurricane forecasts related to Hurricane Ike. The results are based on data from interviews with 49 residents affected by Hurricane Ike, conducted approximately five weeks after landfall. While most interviewees were aware that Ike was potentially dangerous, many were surprised by how much coastal flooding the hurricane caused and the resulting damage. For many—even long-time residents—Ike was a learning experience. As the hurricane approached, interviewees and their households made complex, evolving preparation and evacuation decisions. Although evacuation orders were an important consideration for some interviewees, many obtained information about Ike frequently from multiple sources to evaluate their own risk and make protective decisions. Given the storm surge and damage Ike caused, a number of interviewees believed that Ike’s classification on the Saffir–Simpson scale did not adequately communicate the risk Ike posed. The “certain death” statement issued by the National Weather Service helped convince several interviewees to evacuate. However, others had strong negative opinions of the statement that may negatively influence their interpretation of and response to future warnings. As these findings indicate, empirical studies of how intended audiences obtain, interpret, and use hurricane forecasts and warnings provide valuable knowledge that can help design more effective ways to convey hurricane risk.
Abstract
Climate change is projected to increase the number of days producing excessive heat across the southwestern United States, increasing population exposure to extreme heat events. Extreme heat is currently the main cause of weather-related mortality in the United States, where the negative health effects of extreme heat are disproportionately distributed among geographic regions and demographic groups. To more effectively identify vulnerability to extreme heat, complementary local-level studies of adaptive capacity within a population are needed to augment census-based demographic data and downscaled weather and climate models. This pilot study, conducted in August 2009 in Phoenix, Arizona, reports responses from 359 households in three U.S. Census block groups identified as heat-vulnerable based on heat distress calls, decedent records, and demographic characteristics. This study sought to understand social vulnerability to extreme heat at the local level as a complex phenomenon with explicit characterization of coping and adaptive capacity among urban residents.
Abstract
Climate change is projected to increase the number of days producing excessive heat across the southwestern United States, increasing population exposure to extreme heat events. Extreme heat is currently the main cause of weather-related mortality in the United States, where the negative health effects of extreme heat are disproportionately distributed among geographic regions and demographic groups. To more effectively identify vulnerability to extreme heat, complementary local-level studies of adaptive capacity within a population are needed to augment census-based demographic data and downscaled weather and climate models. This pilot study, conducted in August 2009 in Phoenix, Arizona, reports responses from 359 households in three U.S. Census block groups identified as heat-vulnerable based on heat distress calls, decedent records, and demographic characteristics. This study sought to understand social vulnerability to extreme heat at the local level as a complex phenomenon with explicit characterization of coping and adaptive capacity among urban residents.
Abstract
The false alarm rate (FAR) measures the fraction of forecasted events that did not occur, and it remains one of the key metrics for verifying National Weather Service (NWS) weather warnings. The national FAR for tornado warnings in 2003 was 0.76, indicating that only one in four tornado warnings was verified. The NWS’s goal for 2010 is to reduce this value to 0.70. Conventional wisdom is that false alarms reduce the public’s willingness to respond to future events. This paper questions this conventional wisdom. In addition, this paper argues that the metrics used to evaluate false alarms do not accurately represent the numbers of actual false alarms or the forecasters’ abilities because current metrics categorize events as either a hit or a miss and do not give forecasters credit for close calls. Aspects discussed in this paper include how the NWS FAR is measured, how humans respond to warnings, and what are alternative approaches to measure FAR. A conceptual model is presented as a framework for a new perspective on false alarms that includes close calls, providing a more balanced view of forecast verification.
Abstract
The false alarm rate (FAR) measures the fraction of forecasted events that did not occur, and it remains one of the key metrics for verifying National Weather Service (NWS) weather warnings. The national FAR for tornado warnings in 2003 was 0.76, indicating that only one in four tornado warnings was verified. The NWS’s goal for 2010 is to reduce this value to 0.70. Conventional wisdom is that false alarms reduce the public’s willingness to respond to future events. This paper questions this conventional wisdom. In addition, this paper argues that the metrics used to evaluate false alarms do not accurately represent the numbers of actual false alarms or the forecasters’ abilities because current metrics categorize events as either a hit or a miss and do not give forecasters credit for close calls. Aspects discussed in this paper include how the NWS FAR is measured, how humans respond to warnings, and what are alternative approaches to measure FAR. A conceptual model is presented as a framework for a new perspective on false alarms that includes close calls, providing a more balanced view of forecast verification.
Abstract
Two items need to be clarified from an earlier work of the authors. The first is that the layout of the 2 × 2 contingency table was reversed from standard practice, with the titles of “observed event” and “forecast” transposed. The second is that FAR should have represented “false alarm ratio,” not “false alarm rate.” Unfortunately, the terminology used in the atmospheric sciences is confusing, with authors as early as 1965 having used the terminology differently from currently accepted practice. More recent studies are not much better. A survey of peer-reviewed articles published in American Meteorological Society journals between 2001 and 2007 found that, of 26 articles using those terms, 10 (38%) used them inconsistently with the currently accepted definitions. This article recommends that authors make explicit how their verification statistics are calculated in their manuscripts and consider using the terms probability of false detection and probability of false alarm instead of false alarm rate and false alarm ratio.
Abstract
Two items need to be clarified from an earlier work of the authors. The first is that the layout of the 2 × 2 contingency table was reversed from standard practice, with the titles of “observed event” and “forecast” transposed. The second is that FAR should have represented “false alarm ratio,” not “false alarm rate.” Unfortunately, the terminology used in the atmospheric sciences is confusing, with authors as early as 1965 having used the terminology differently from currently accepted practice. More recent studies are not much better. A survey of peer-reviewed articles published in American Meteorological Society journals between 2001 and 2007 found that, of 26 articles using those terms, 10 (38%) used them inconsistently with the currently accepted definitions. This article recommends that authors make explicit how their verification statistics are calculated in their manuscripts and consider using the terms probability of false detection and probability of false alarm instead of false alarm rate and false alarm ratio.
Abstract
The West Nile region in northwestern Uganda is a focal point for human plague, which peaks in boreal autumn and is spread by fleas that travel on rodent hosts. The U.S. Centers for Disease Control and Prevention is collaborating with the National Center for Atmospheric Research to quantitatively address the linkages between climate and human plague in this region. The aim of this paper is to advance knowledge of the climatic conditions required to maintain enzootic cycles and to trigger epizootic cycles and ultimately to target limited surveillance, prevention, and control resources. A hybrid dynamical–statistical downscaling technique was applied to simulations from the Weather Research and Forecasting Model (WRF) to generate a multiyear 2-km climate dataset for modeling plague in the West Nile region. The resulting dataset resolves the spatial variability and annual cycle of temperature, humidity, and rainfall in West Nile relative to satellite-based and in situ records. Topography exerts a first-order influence on the climatic gradients in West Nile, which lies in a transition zone between the drier East African Plateau and the wetter Congo Basin, and between the unimodal rainfall regimes of the Sahel and the bimodal rainfall regimes characteristic of equatorial East Africa. The results of a companion paper in which the WRF-based climate fields were applied to develop an improved logistic regression model of human plague occurrence in West Nile are summarized, revealing robust positive associations with rainfall at the tails of the rainy season and negative associations with rainfall during a dry spell each summer.
Abstract
The West Nile region in northwestern Uganda is a focal point for human plague, which peaks in boreal autumn and is spread by fleas that travel on rodent hosts. The U.S. Centers for Disease Control and Prevention is collaborating with the National Center for Atmospheric Research to quantitatively address the linkages between climate and human plague in this region. The aim of this paper is to advance knowledge of the climatic conditions required to maintain enzootic cycles and to trigger epizootic cycles and ultimately to target limited surveillance, prevention, and control resources. A hybrid dynamical–statistical downscaling technique was applied to simulations from the Weather Research and Forecasting Model (WRF) to generate a multiyear 2-km climate dataset for modeling plague in the West Nile region. The resulting dataset resolves the spatial variability and annual cycle of temperature, humidity, and rainfall in West Nile relative to satellite-based and in situ records. Topography exerts a first-order influence on the climatic gradients in West Nile, which lies in a transition zone between the drier East African Plateau and the wetter Congo Basin, and between the unimodal rainfall regimes of the Sahel and the bimodal rainfall regimes characteristic of equatorial East Africa. The results of a companion paper in which the WRF-based climate fields were applied to develop an improved logistic regression model of human plague occurrence in West Nile are summarized, revealing robust positive associations with rainfall at the tails of the rainy season and negative associations with rainfall during a dry spell each summer.
Abstract
Extreme heat is the leading cause of weather-related mortality in the United States, suggesting the necessity for better understanding population vulnerability to extreme heat. The work presented here is part of a larger study examining vulnerability to extreme heat in current and future climates [System for Integrated Modeling of Metropolitan Extreme Heat Risk (SIMMER)] and was undertaken to assess Houston, Texas, residents’ adaptive capacity to extreme heat. A comprehensive, semistructured survey was conducted by telephone at 901 households in Houston in 2011. Frequency and logistic regression analyses were conducted. Results show that 20% of the survey respondents reported heat-related symptoms in the summer of 2011 despite widespread air conditioning availability throughout Houston. Of those reporting heat-related symptoms experienced in the home (n = 56), the majority could not afford to use air conditioning because of the high cost of electricity. This research highlights the efficacy of community-based surveys to better understand adaptive capacity at the household level; this survey contextualizes population vulnerability and identifies more targeted intervention strategies and adaptation actions.
Abstract
Extreme heat is the leading cause of weather-related mortality in the United States, suggesting the necessity for better understanding population vulnerability to extreme heat. The work presented here is part of a larger study examining vulnerability to extreme heat in current and future climates [System for Integrated Modeling of Metropolitan Extreme Heat Risk (SIMMER)] and was undertaken to assess Houston, Texas, residents’ adaptive capacity to extreme heat. A comprehensive, semistructured survey was conducted by telephone at 901 households in Houston in 2011. Frequency and logistic regression analyses were conducted. Results show that 20% of the survey respondents reported heat-related symptoms in the summer of 2011 despite widespread air conditioning availability throughout Houston. Of those reporting heat-related symptoms experienced in the home (n = 56), the majority could not afford to use air conditioning because of the high cost of electricity. This research highlights the efficacy of community-based surveys to better understand adaptive capacity at the household level; this survey contextualizes population vulnerability and identifies more targeted intervention strategies and adaptation actions.
Abstract
Meningitis remains a major health burden throughout Sahelian Africa, especially in heavily populated northwest Nigeria with an annual incidence rate ranging from 18 to 200 per 100 000 people for 2000–11. Several studies have established that cases exhibit sensitivity to intra- and interannual climate variability, peaking during the hot and dry boreal spring months, raising concern that future climate change may increase the incidence of meningitis in the region. The impact of future climate change on meningitis risk in northwest Nigeria is assessed by forcing an empirical model of meningitis with monthly simulations of seven meteorological variables from an ensemble of 13 statistically downscaled global climate model projections from phase 5 of the Coupled Model Intercomparison Experiment (CMIP5) for representative concentration pathway (RCP) 2.6, 6.0, and 8.5 scenarios, with the numbers representing the globally averaged top-of-the-atmosphere radiative imbalance (in W m−2) in 2100. The results suggest future temperature increases due to climate change have the potential to significantly increase meningitis cases in both the early (2020–35) and late (2060–75) twenty-first century, and for the seasonal onset of meningitis to begin about a month earlier on average by late century, in October rather than November. Annual incidence may increase by 47% ± 8%, 64% ± 9%, and 99% ± 12% for the RCP 2.6, 6.0, and 8.5 scenarios, respectively, in 2060–75 with respect to 1990–2005. It is noteworthy that these results represent the climatological potential for increased cases due to climate change, as it is assumed that current prevention and treatment strategies will remain similar in the future.
Abstract
Meningitis remains a major health burden throughout Sahelian Africa, especially in heavily populated northwest Nigeria with an annual incidence rate ranging from 18 to 200 per 100 000 people for 2000–11. Several studies have established that cases exhibit sensitivity to intra- and interannual climate variability, peaking during the hot and dry boreal spring months, raising concern that future climate change may increase the incidence of meningitis in the region. The impact of future climate change on meningitis risk in northwest Nigeria is assessed by forcing an empirical model of meningitis with monthly simulations of seven meteorological variables from an ensemble of 13 statistically downscaled global climate model projections from phase 5 of the Coupled Model Intercomparison Experiment (CMIP5) for representative concentration pathway (RCP) 2.6, 6.0, and 8.5 scenarios, with the numbers representing the globally averaged top-of-the-atmosphere radiative imbalance (in W m−2) in 2100. The results suggest future temperature increases due to climate change have the potential to significantly increase meningitis cases in both the early (2020–35) and late (2060–75) twenty-first century, and for the seasonal onset of meningitis to begin about a month earlier on average by late century, in October rather than November. Annual incidence may increase by 47% ± 8%, 64% ± 9%, and 99% ± 12% for the RCP 2.6, 6.0, and 8.5 scenarios, respectively, in 2060–75 with respect to 1990–2005. It is noteworthy that these results represent the climatological potential for increased cases due to climate change, as it is assumed that current prevention and treatment strategies will remain similar in the future.
Abstract
One of the goals of the Warning Project is to understand how people receive warnings of hazardous weather and subsequently use this information to make decisions. As part of the project, 519 surveys from Austin, Texas, floodplain residents were collected and analyzed. About 90% of respondents understood that a tornado warning represented a more serious and more likely threat than a tornado watch. Most respondents (86%) were not concerned about a limited number of false alarms or close calls reducing their confidence in future warnings, suggesting no cry-wolf effect. Most respondents reported safe decisions in two hypothetical scenarios: a tornado warning issued while the respondent was home and a tornado visible by the respondent while driving. However, nearly half the respondents indicated that they would seek shelter from a tornado under a highway overpass if they were driving. Despite the limitations of this study, these results suggest that more education is needed on the dangers of highway overpasses as shelter from severe weather.
Abstract
One of the goals of the Warning Project is to understand how people receive warnings of hazardous weather and subsequently use this information to make decisions. As part of the project, 519 surveys from Austin, Texas, floodplain residents were collected and analyzed. About 90% of respondents understood that a tornado warning represented a more serious and more likely threat than a tornado watch. Most respondents (86%) were not concerned about a limited number of false alarms or close calls reducing their confidence in future warnings, suggesting no cry-wolf effect. Most respondents reported safe decisions in two hypothetical scenarios: a tornado warning issued while the respondent was home and a tornado visible by the respondent while driving. However, nearly half the respondents indicated that they would seek shelter from a tornado under a highway overpass if they were driving. Despite the limitations of this study, these results suggest that more education is needed on the dangers of highway overpasses as shelter from severe weather.
Abstract
Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.
Abstract
Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.
