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- Author or Editor: Laurence S. Kalkstein x
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Abstract
The heat stress index (HSI) is a new, comprehensive summer index that evaluates daily relative stress for locations throughout the United States based on deviations from the norm. The index is based on apparent temperature and other derived meteorological variables, including cloud cover, cooling degree-days, and consecutive days of extreme heat. Statistical distributions of meteorological variables are derived for 10-day periods of the annual cycle so that percentile values for each parameter can be determined. The daily percentile values for each variable are then summed, and a statistical distribution is fit to the summed frequencies. The daily HSI value is the percentile associated with the location of the daily summed value under the summation curve. The index is analyzed and spatially verified by comparing intra- and interregional results. Although stations from various climate regions have different criteria defining an excessive heat stress event, neighboring stations typically produce similar HSI results because they are usually affected by the same air mass. To test the effectiveness of the HSI, a relationship between the index results and mortality values is made. Overall, the highest mortality days are associated with the highest HSI values, but high-HSI days are not always associated with high numbers of deaths. A mortality study such as this one is just one of many potential environmental applications of the HSI. Other applications include implementing the index to correlate extreme weather conditions with resource consumption, such as electric-utility load, to determine conditions for which load levels are excessive. The ability to forecast the HSI using a variety of weather forecasting tools has also generated interest within various industries that have a need to issue weather stress advisories, watches, and warnings.
Abstract
The heat stress index (HSI) is a new, comprehensive summer index that evaluates daily relative stress for locations throughout the United States based on deviations from the norm. The index is based on apparent temperature and other derived meteorological variables, including cloud cover, cooling degree-days, and consecutive days of extreme heat. Statistical distributions of meteorological variables are derived for 10-day periods of the annual cycle so that percentile values for each parameter can be determined. The daily percentile values for each variable are then summed, and a statistical distribution is fit to the summed frequencies. The daily HSI value is the percentile associated with the location of the daily summed value under the summation curve. The index is analyzed and spatially verified by comparing intra- and interregional results. Although stations from various climate regions have different criteria defining an excessive heat stress event, neighboring stations typically produce similar HSI results because they are usually affected by the same air mass. To test the effectiveness of the HSI, a relationship between the index results and mortality values is made. Overall, the highest mortality days are associated with the highest HSI values, but high-HSI days are not always associated with high numbers of deaths. A mortality study such as this one is just one of many potential environmental applications of the HSI. Other applications include implementing the index to correlate extreme weather conditions with resource consumption, such as electric-utility load, to determine conditions for which load levels are excessive. The ability to forecast the HSI using a variety of weather forecasting tools has also generated interest within various industries that have a need to issue weather stress advisories, watches, and warnings.
Among all atmospheric hazards, heat is the most deadly. With such recent notable heat events as the Chicago Heat Wave of 1995, much effort has gone into redeveloping both the methods by which it is determined whether a day will be “oppressive,” as well as the mitigation plans that are implemented when an oppressive day is forecast to occur.
This article describes the techniques that have been implemented in the development of new synoptic-based heat watch–warning systems. These systems are presently running for over two dozen locations worldwide, including Chicago, Illinois; Toronto, Ontario, Canada; Rome, Italy; and Shanghai, China; with plans for continued expansion. Compared to traditional systems based on arbitrary thresholds of one or two meteorological variables, these new systems account for the local human response by focusing upon the identification of the weather conditions most strongly associated with historical increases in mortality. These systems must be constructed based on the premise that weather conditions associated with increased mortality show considerable variability on a spatial scale. In locales with consistently hot summers, weather/mortality relationships are weaker, and it is only the few hottest days each year that are associated with a response. In more temperate climates, relationships are stronger, and a greater percentage of days can be associated with an increase in mortality.
Considering the ease of data transfer via the World-Wide Web, the development of these systems includes Internet file transfers and Web page creation as components. Forecasts of mortality and recommendations to call excessive-heat warnings are available to local meteorological forecasters, local health officials, and other civic authorities, who ultimately determine when warnings are called and when intervention plans are instituted.
Among all atmospheric hazards, heat is the most deadly. With such recent notable heat events as the Chicago Heat Wave of 1995, much effort has gone into redeveloping both the methods by which it is determined whether a day will be “oppressive,” as well as the mitigation plans that are implemented when an oppressive day is forecast to occur.
This article describes the techniques that have been implemented in the development of new synoptic-based heat watch–warning systems. These systems are presently running for over two dozen locations worldwide, including Chicago, Illinois; Toronto, Ontario, Canada; Rome, Italy; and Shanghai, China; with plans for continued expansion. Compared to traditional systems based on arbitrary thresholds of one or two meteorological variables, these new systems account for the local human response by focusing upon the identification of the weather conditions most strongly associated with historical increases in mortality. These systems must be constructed based on the premise that weather conditions associated with increased mortality show considerable variability on a spatial scale. In locales with consistently hot summers, weather/mortality relationships are weaker, and it is only the few hottest days each year that are associated with a response. In more temperate climates, relationships are stronger, and a greater percentage of days can be associated with an increase in mortality.
Considering the ease of data transfer via the World-Wide Web, the development of these systems includes Internet file transfers and Web page creation as components. Forecasts of mortality and recommendations to call excessive-heat warnings are available to local meteorological forecasters, local health officials, and other civic authorities, who ultimately determine when warnings are called and when intervention plans are instituted.
The objective of this study is to provide an evaluation of the magnitude of apparent temperature and the weather stress index (WSI) in winter across the United States. In addition, two extremely cold winters, 1976–77 and 1981–82, are analyzed in terms of their relative severity with the assistance of the WSI.
Mean apparent temperatures around the nation for 0300 LST in January show an expected latitudinal trend, with the lowest apparent temperatures found in the north central United States. Although this distribution roughly approximates that of mean air temperature for January, there are significant differences. Large disparities between mean 0300 LST apparent temperature and air temperature exist from Kansas to Minnesota. Much-smaller disparities are found in the East and the South, heightening the latitudinal gradient for apparent temperature. The severity of winter conditions in the north-central United States is clearly noted when evaluating the WSI; in parts of North Dakota and Minnesota, the apparent temperature corresponding to the 99-percent WSI at 0300 LST is below −45°C.
The winter of 1981–82 is credited as having the most-severe individual winter day since 1948. On 11 January 1982, WSI values exceeding 99 percent covered over two-thirds of the nation. However, based on the duration of stressful weather conditions, the winter of 1976–77 was more stressful than 1981–82. During 1976–77, the most-stressful conditions were encountered in the populous East, and in a sizable area over one-third of the days were described as stressful (WSI exceeding 90 percent). These proportions were much lower during the winter of 1981–82.
The objective of this study is to provide an evaluation of the magnitude of apparent temperature and the weather stress index (WSI) in winter across the United States. In addition, two extremely cold winters, 1976–77 and 1981–82, are analyzed in terms of their relative severity with the assistance of the WSI.
Mean apparent temperatures around the nation for 0300 LST in January show an expected latitudinal trend, with the lowest apparent temperatures found in the north central United States. Although this distribution roughly approximates that of mean air temperature for January, there are significant differences. Large disparities between mean 0300 LST apparent temperature and air temperature exist from Kansas to Minnesota. Much-smaller disparities are found in the East and the South, heightening the latitudinal gradient for apparent temperature. The severity of winter conditions in the north-central United States is clearly noted when evaluating the WSI; in parts of North Dakota and Minnesota, the apparent temperature corresponding to the 99-percent WSI at 0300 LST is below −45°C.
The winter of 1981–82 is credited as having the most-severe individual winter day since 1948. On 11 January 1982, WSI values exceeding 99 percent covered over two-thirds of the nation. However, based on the duration of stressful weather conditions, the winter of 1976–77 was more stressful than 1981–82. During 1976–77, the most-stressful conditions were encountered in the populous East, and in a sizable area over one-third of the days were described as stressful (WSI exceeding 90 percent). These proportions were much lower during the winter of 1981–82.
A relative climatological index is developed to evaluate interregional variations in human discomfort and the impacts of weather on a variety of socioeconomic parameters. The “weather stress index” is designed to assess the frequency and magnitude of the most uncomfortable weather conditions, and data inputs are limited to air temperature, dewpoint, and wind speed. The index is constructed by calculating the apparent temperature using a simple algorithm and comparing how a particular day's apparent temperature varies from the mean for that day at that locale. The index ranges from 0 percent to 100 percent, with the most uncomfortable apparent temperatures exhibiting the highest values.
A geographical distribution of July apparent temperatures at the 95 percent and 99 percent weather-stress-index level indicates that the central and south central United States experience the highest apparent temperatures in the nation. These conditions occur when the surface flow permits maritime air to intrude while a 500-mb ridge is present to encourage atmospheric subsidence. The combination of these events almost never occurs in the Desert Southwest, and the highest apparent temperatures here do not reach the levels encountered in the central United States.
The use of the weather stress index should enhance interregional evaluation and facilitate the development of large-scale models for analyses of numerous climate-impact relationships.
A relative climatological index is developed to evaluate interregional variations in human discomfort and the impacts of weather on a variety of socioeconomic parameters. The “weather stress index” is designed to assess the frequency and magnitude of the most uncomfortable weather conditions, and data inputs are limited to air temperature, dewpoint, and wind speed. The index is constructed by calculating the apparent temperature using a simple algorithm and comparing how a particular day's apparent temperature varies from the mean for that day at that locale. The index ranges from 0 percent to 100 percent, with the most uncomfortable apparent temperatures exhibiting the highest values.
A geographical distribution of July apparent temperatures at the 95 percent and 99 percent weather-stress-index level indicates that the central and south central United States experience the highest apparent temperatures in the nation. These conditions occur when the surface flow permits maritime air to intrude while a 500-mb ridge is present to encourage atmospheric subsidence. The combination of these events almost never occurs in the Desert Southwest, and the highest apparent temperatures here do not reach the levels encountered in the central United States.
The use of the weather stress index should enhance interregional evaluation and facilitate the development of large-scale models for analyses of numerous climate-impact relationships.
Abstract
Studies which utilize a long-term temperature record in determining the possibility of a global warming have led to conflicting results. We suggest that a time-series evaluation of mean annual temperatures is not sufficiently robust to determine the existence of a long-term warming. We propose the utilization of an air mass-based synoptic climatological approach, as it is possible that local changes within particular air masses have been obscured by the gross scale of temperature time-series evaluations used in previous studies of this type. An automated synoptic index was constructed for the winter months in four western North American Arctic locations to determine if the frequency of occurrence of the coldest and mildest air masses has changed and if the physical character of these air masses has shown signs of modification over the past 40 years. It appears that the frequencies of the majority of the coldest air masses have tended to decrease, while those of the warmest air masses have increased. In addition, the very coldest air masses at each site have warmed between 1°C to almost 4°C over the same time interval. A technique is suggested to determine whether these changes are possibly attributable to anthropogenic influences.
Abstract
Studies which utilize a long-term temperature record in determining the possibility of a global warming have led to conflicting results. We suggest that a time-series evaluation of mean annual temperatures is not sufficiently robust to determine the existence of a long-term warming. We propose the utilization of an air mass-based synoptic climatological approach, as it is possible that local changes within particular air masses have been obscured by the gross scale of temperature time-series evaluations used in previous studies of this type. An automated synoptic index was constructed for the winter months in four western North American Arctic locations to determine if the frequency of occurrence of the coldest and mildest air masses has changed and if the physical character of these air masses has shown signs of modification over the past 40 years. It appears that the frequencies of the majority of the coldest air masses have tended to decrease, while those of the warmest air masses have increased. In addition, the very coldest air masses at each site have warmed between 1°C to almost 4°C over the same time interval. A technique is suggested to determine whether these changes are possibly attributable to anthropogenic influences.
Abstract
The selection of the proper clustering procedure to use in the development of an objective synoptic methodology may have far-reaching implications on the composition of the final “homogeneous” groupings. The goal of this study is to evaluate three common clustering techniques (Ward's, average linkage, and centroid) to determine which yields the most meaningful synoptic classification. The three clustering procedures were applied to a temporal synoptic index which classified days in Mobile, Alabama into meteorologically homogeneous units. The final meteorological groupings differed widely among the three pressures. Ward's tended to produce groups with relatively similar numbers of days. Thus, many extreme weather days were grouped with less extreme days, and the final meteorological units did not duplicate reality with great precision. The centroid procedure produced one very large group and many single-day groups, yielding unsatisfactory results. The average linkage procedure, which minimizes within-cluster variance, produced the most realistic synoptic groupings and properly combined extreme weather days into distinct meteorological units.
Abstract
The selection of the proper clustering procedure to use in the development of an objective synoptic methodology may have far-reaching implications on the composition of the final “homogeneous” groupings. The goal of this study is to evaluate three common clustering techniques (Ward's, average linkage, and centroid) to determine which yields the most meaningful synoptic classification. The three clustering procedures were applied to a temporal synoptic index which classified days in Mobile, Alabama into meteorologically homogeneous units. The final meteorological groupings differed widely among the three pressures. Ward's tended to produce groups with relatively similar numbers of days. Thus, many extreme weather days were grouped with less extreme days, and the final meteorological units did not duplicate reality with great precision. The centroid procedure produced one very large group and many single-day groups, yielding unsatisfactory results. The average linkage procedure, which minimizes within-cluster variance, produced the most realistic synoptic groupings and properly combined extreme weather days into distinct meteorological units.
Abstract
It has been suggested that previous results indicating an increase in surface temperatures over the past 40 years within the coldest air masses at four stations in the western North American Arctic may be attributed to the shorter residence lime of these air masses through the time period. If true, this contradicts the original contention that these air masses have undergone physical character changes, possibly attributed to anthropogenic sources, during the period. A reevaluation of the data at two of these stations indicates that a long-term warming is, in fact, taking place even when residence time is kept constant. Thus, it is suggested that changes in the physical character of these very cold air masses are due to factors other than residence time.
Abstract
It has been suggested that previous results indicating an increase in surface temperatures over the past 40 years within the coldest air masses at four stations in the western North American Arctic may be attributed to the shorter residence lime of these air masses through the time period. If true, this contradicts the original contention that these air masses have undergone physical character changes, possibly attributed to anthropogenic sources, during the period. A reevaluation of the data at two of these stations indicates that a long-term warming is, in fact, taking place even when residence time is kept constant. Thus, it is suggested that changes in the physical character of these very cold air masses are due to factors other than residence time.
Abstract
This study examines the impact of a changing climate on heat-related mortality in 40 large cities in the United States. A synoptic climatological procedure, the spatial synoptic classification, is used to evaluate present climate–mortality relationships and project how potential climate changes might affect these values. Specifically, the synoptic classification is combined with downscaled future climate projections for the decadal periods of 2020–29, 2045–55, and 2090–99 from a coupled atmospheric–oceanic general circulation model. The results show an increase in excessive heat event (EHE) days and increased heat-attributable mortality across the study cities with the most pronounced increases projected to occur in the Southeast and Northeast. This increase becomes more dramatic toward the end of the twenty-first century as the anticipated impact of climate change intensifies. The health impact associated with different emissions scenarios is also examined. These results suggest that a “business as usual” approach to greenhouse gas emissions mitigation could result in twice as many heat-related deaths by the end of the century than a lower emissions scenario. Finally, a comparison of future estimates of heat-related mortality during EHEs is presented using algorithms developed during two different, although overlapping, time periods, one that includes some recent large-scale significant EHE intervention strategies (1975–2004), and one without (1975–95). The results suggest these public health responses can significantly decrease heat-related mortality.
Abstract
This study examines the impact of a changing climate on heat-related mortality in 40 large cities in the United States. A synoptic climatological procedure, the spatial synoptic classification, is used to evaluate present climate–mortality relationships and project how potential climate changes might affect these values. Specifically, the synoptic classification is combined with downscaled future climate projections for the decadal periods of 2020–29, 2045–55, and 2090–99 from a coupled atmospheric–oceanic general circulation model. The results show an increase in excessive heat event (EHE) days and increased heat-attributable mortality across the study cities with the most pronounced increases projected to occur in the Southeast and Northeast. This increase becomes more dramatic toward the end of the twenty-first century as the anticipated impact of climate change intensifies. The health impact associated with different emissions scenarios is also examined. These results suggest that a “business as usual” approach to greenhouse gas emissions mitigation could result in twice as many heat-related deaths by the end of the century than a lower emissions scenario. Finally, a comparison of future estimates of heat-related mortality during EHEs is presented using algorithms developed during two different, although overlapping, time periods, one that includes some recent large-scale significant EHE intervention strategies (1975–2004), and one without (1975–95). The results suggest these public health responses can significantly decrease heat-related mortality.
The Philadelphia, Pennsylvania, Hot Weather-Health Watch/Warning System was initiated in 1995 to alert the city's population to take precautionary actions when hot weather posed risks to health. The number of lives saved and the economic benefit of this system were estimated using data from 1995 to 1998. Excess mortality in people 65 yr of age and older was defined as reported mortality minus mortality predicted by a historical trend line developed over the period of 1964–88. Excess mortality during heat waves was explained using multiple linear regression. Two variables were convincingly associated with mortality: the time of season when a particular heat wave started, and a warning variable indicating whether or not a heat wave warning had been issued. The estimated coefficient of the warning variable was about −2.6, suggesting that when a warning was issued, 2.6 lives were saved, on average, for each warning day and for 3 days after the warning ended. Given the number of warnings issued over the 3-yr period, the system saved an estimated 117 lives. Estimated dollar costs for running the system were small compared with estimates of the value of a life.
The Philadelphia, Pennsylvania, Hot Weather-Health Watch/Warning System was initiated in 1995 to alert the city's population to take precautionary actions when hot weather posed risks to health. The number of lives saved and the economic benefit of this system were estimated using data from 1995 to 1998. Excess mortality in people 65 yr of age and older was defined as reported mortality minus mortality predicted by a historical trend line developed over the period of 1964–88. Excess mortality during heat waves was explained using multiple linear regression. Two variables were convincingly associated with mortality: the time of season when a particular heat wave started, and a warning variable indicating whether or not a heat wave warning had been issued. The estimated coefficient of the warning variable was about −2.6, suggesting that when a warning was issued, 2.6 lives were saved, on average, for each warning day and for 3 days after the warning ended. Given the number of warnings issued over the 3-yr period, the system saved an estimated 117 lives. Estimated dollar costs for running the system were small compared with estimates of the value of a life.
Last summer, Philadelphia, Pennsylvania, instituted a new Hot Weather–Health Watch/Warning System (PWWS) to alert the city's residents of potentially oppressive weather situations that could negatively affect health. In addition, the system was used by the Philadelphia Department of Public Health for guidance in the implementation of mitigation procedures during dangerous weather. The system is based on a synoptic climatological procedure that identifies “oppressive” air masses historically associated with increased human mortality. Airmass occurrence can be predicted up to 48 h in advance with use of model output statistics guidance forecast data. The development and statistical basis of the system are discussed, and an analysis of the procedure's ability to forecast weather situations associated with elevated mortality counts is presented. The PWWS, through greater public awareness of excessive heat conditions, may have played an important role in reducing Philadelphia's total heat-related deaths during the summer of 1995.
Last summer, Philadelphia, Pennsylvania, instituted a new Hot Weather–Health Watch/Warning System (PWWS) to alert the city's residents of potentially oppressive weather situations that could negatively affect health. In addition, the system was used by the Philadelphia Department of Public Health for guidance in the implementation of mitigation procedures during dangerous weather. The system is based on a synoptic climatological procedure that identifies “oppressive” air masses historically associated with increased human mortality. Airmass occurrence can be predicted up to 48 h in advance with use of model output statistics guidance forecast data. The development and statistical basis of the system are discussed, and an analysis of the procedure's ability to forecast weather situations associated with elevated mortality counts is presented. The PWWS, through greater public awareness of excessive heat conditions, may have played an important role in reducing Philadelphia's total heat-related deaths during the summer of 1995.
