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Abstract
Reported path lengths and widths of tornadoes have been modeled using Weibull distributions for different Fujita (F) scale values. The fits are good over a wide range of lengths and widths. Path length and width tend to increase with increasing F scale, although the temporal nonstationarity of the data for some parts of the data (such as width of F3 tornadoes) is large enough that caution must be exercised in interpretation of short periods of record. The statistical distributions also demonstrate that, as the length or width increases, the most likely F-scale value associated with the length or width tends to increase. Nevertheless, even for long or wide tornadoes, there is a significant probability of a range of possible F values, so that simple observation of the length or width is insufficient to make an accurate estimate of the F scale.
Abstract
Reported path lengths and widths of tornadoes have been modeled using Weibull distributions for different Fujita (F) scale values. The fits are good over a wide range of lengths and widths. Path length and width tend to increase with increasing F scale, although the temporal nonstationarity of the data for some parts of the data (such as width of F3 tornadoes) is large enough that caution must be exercised in interpretation of short periods of record. The statistical distributions also demonstrate that, as the length or width increases, the most likely F-scale value associated with the length or width tends to increase. Nevertheless, even for long or wide tornadoes, there is a significant probability of a range of possible F values, so that simple observation of the length or width is insufficient to make an accurate estimate of the F scale.
Abstract
After the tornadoes of 3 May 1999, the Federal Emergency Management Agency formed a Building Performance Assessment Team (BPAT) to examine the main tornado paths during the outbreak and to make recommendations based on the damage they saw. This is the first time a tornado disaster has been subjected to a BPAT investigation. Some aspects of the BPAT final report are reviewed and considered in the context of tornado preparedness in Kansas and Oklahoma. Although the preparedness efforts of many public and private institutions apparently played a large role in reducing casualties from the storm, a number of building deficiencies were found during the BPAT's evaluation. Especially in public facilities, there are several aspects of tornado preparedness that could be improved. Moreover, there is clear evidence that a nonnegligible fraction of the damage associated with these storms could have been mitigated with some relatively simple and inexpensive construction enhancements. Widespread implementation of these enhancements would reduce projectile loading and its associated threats to both life and property.
Abstract
After the tornadoes of 3 May 1999, the Federal Emergency Management Agency formed a Building Performance Assessment Team (BPAT) to examine the main tornado paths during the outbreak and to make recommendations based on the damage they saw. This is the first time a tornado disaster has been subjected to a BPAT investigation. Some aspects of the BPAT final report are reviewed and considered in the context of tornado preparedness in Kansas and Oklahoma. Although the preparedness efforts of many public and private institutions apparently played a large role in reducing casualties from the storm, a number of building deficiencies were found during the BPAT's evaluation. Especially in public facilities, there are several aspects of tornado preparedness that could be improved. Moreover, there is clear evidence that a nonnegligible fraction of the damage associated with these storms could have been mitigated with some relatively simple and inexpensive construction enhancements. Widespread implementation of these enhancements would reduce projectile loading and its associated threats to both life and property.
Abstract
One of the challenges of providing probabilistic information on a multitude of spatiotemporal scales is ensuring that information is both accurate and useful to decision-makers. Focusing on larger spatiotemporal scales (i.e., from convective outlook to weather watch scales), historical severe weather reports are analyzed to begin to understand the spatiotemporal scales that hazardous weather events are contained within. Reports from the Storm Prediction Center’s report archive are placed onto grids of differing spatial scales and then split into 24-h convective outlook days (1200–1200 UTC). These grids are then analyzed temporally to assess over what fraction of the day a single location would generally experience severe weather events. Different combinations of temporal and spatial scales are tested to determine how the reference class (or the choice of what scales to use) alters the probabilities of severe weather events. Results indicate that at any given point in the United States on any given day, more than 95% of the daily reports within 40 km of the point occur in a 4-h period. Therefore, the SPC 24-h convective outlook probabilities can be interpreted as 4-h convective outlook probabilities without a significant change in meaning. Additionally, probabilities and threat periods are analyzed at each location and different times of year. These results indicate little variability in the duration of severe weather events, which allows for a consistent definition of an “event” for all locations in the continental United States.
Abstract
One of the challenges of providing probabilistic information on a multitude of spatiotemporal scales is ensuring that information is both accurate and useful to decision-makers. Focusing on larger spatiotemporal scales (i.e., from convective outlook to weather watch scales), historical severe weather reports are analyzed to begin to understand the spatiotemporal scales that hazardous weather events are contained within. Reports from the Storm Prediction Center’s report archive are placed onto grids of differing spatial scales and then split into 24-h convective outlook days (1200–1200 UTC). These grids are then analyzed temporally to assess over what fraction of the day a single location would generally experience severe weather events. Different combinations of temporal and spatial scales are tested to determine how the reference class (or the choice of what scales to use) alters the probabilities of severe weather events. Results indicate that at any given point in the United States on any given day, more than 95% of the daily reports within 40 km of the point occur in a 4-h period. Therefore, the SPC 24-h convective outlook probabilities can be interpreted as 4-h convective outlook probabilities without a significant change in meaning. Additionally, probabilities and threat periods are analyzed at each location and different times of year. These results indicate little variability in the duration of severe weather events, which allows for a consistent definition of an “event” for all locations in the continental United States.
Abstract
The 3 May 1999 Oklahoma City tornado was the deadliest in the United States in over 20 years, with 36 direct fatalities. To understand how this event fits into the historical context, the record of tornado deaths in the United States has been examined. Almost 20 000 deaths have been reported associated with more than 3600 tornadoes in the United States since 1680. A cursory examination of the record shows a break in 1875. Prior to then, it is likely that many killer tornadoes failed to be reported. When the death toll is normalized by population, a near-constant rate of death is apparent until about 1925, when a sharp fall begins. The rate was about 1.8 people per million population in 1925 and was less than 0.12 people per million by 2000. The decrease in fatalities has resulted from two primary causes: a decrease in the number of killer tornadoes and a decrease in the number of fatalities in the most deadly tornadoes. Current death rates for mobile home residents, however, are still nearly what the overall national rate was prior to 1925 and are about 20 times the rate of site-built home residents. The increase in the fraction of the U.S. population living in mobile homes has important implications for future reductions in the death toll.
Abstract
The 3 May 1999 Oklahoma City tornado was the deadliest in the United States in over 20 years, with 36 direct fatalities. To understand how this event fits into the historical context, the record of tornado deaths in the United States has been examined. Almost 20 000 deaths have been reported associated with more than 3600 tornadoes in the United States since 1680. A cursory examination of the record shows a break in 1875. Prior to then, it is likely that many killer tornadoes failed to be reported. When the death toll is normalized by population, a near-constant rate of death is apparent until about 1925, when a sharp fall begins. The rate was about 1.8 people per million population in 1925 and was less than 0.12 people per million by 2000. The decrease in fatalities has resulted from two primary causes: a decrease in the number of killer tornadoes and a decrease in the number of fatalities in the most deadly tornadoes. Current death rates for mobile home residents, however, are still nearly what the overall national rate was prior to 1925 and are about 20 times the rate of site-built home residents. The increase in the fraction of the U.S. population living in mobile homes has important implications for future reductions in the death toll.
Abstract
The authors have carried out verification of 590 12–24-h high-temperature forecasts from numerical guidance products and human forecasters for Oklahoma City, Oklahoma, using both a measures-oriented verification scheme and a distributions-oriented scheme. The latter captures the richness associated with the relationship of forecasts and observations, providing insight into strengths and weaknesses of the forecasting systems, and showing areas in which improvement in accuracy can be obtained.
The analysis of this single forecast element at one lead time shows the amount of information available from a distributions-oriented verification scheme. In order to obtain a complete picture of the overall state of forecasting, it would be necessary to verify all elements at all lead times. The authors urge the development of such a national verification scheme as soon as possible, since without it, it will be impossible to monitor changes in the quality of forecasts and forecasting systems in the future.
Abstract
The authors have carried out verification of 590 12–24-h high-temperature forecasts from numerical guidance products and human forecasters for Oklahoma City, Oklahoma, using both a measures-oriented verification scheme and a distributions-oriented scheme. The latter captures the richness associated with the relationship of forecasts and observations, providing insight into strengths and weaknesses of the forecasting systems, and showing areas in which improvement in accuracy can be obtained.
The analysis of this single forecast element at one lead time shows the amount of information available from a distributions-oriented verification scheme. In order to obtain a complete picture of the overall state of forecasting, it would be necessary to verify all elements at all lead times. The authors urge the development of such a national verification scheme as soon as possible, since without it, it will be impossible to monitor changes in the quality of forecasts and forecasting systems in the future.
Abstract
The authors discuss the relationship between budget-cutting exercises and knowledge of the value of weather services. The complex interaction between quality (accuracy) and value of weather forecasts prevents theoretical approaches from contributing much to the discussion, except perhaps to indicate some of the sources for its complexity. The absence of comprehensive theoretical answers indicates the importance of empirical determinations of forecast value; as it stands, the United States is poorly equipped to make intelligent decisions in the current and future budget situations. To obtain credible empirical answers, forecasters will need to develop closer working relationships with their users than ever before, seeking specific information regarding economic value of forecasts. Some suggestions for developing plausible value estimates are offered, based largely on limited studies already in the literature. Efforts to create closer ties between forecasters and users can yield diverse benefits, including the desired credible estimates of the value of forecasts, as well as estimates of the sensitivity of that value to changes in accuracy of the forecasts. The authors argue for the development of an infrastructure to make these empirical value estimates, as a critical need within weather forecasting agencies, public and private, in view of continuing budget pressures.
Abstract
The authors discuss the relationship between budget-cutting exercises and knowledge of the value of weather services. The complex interaction between quality (accuracy) and value of weather forecasts prevents theoretical approaches from contributing much to the discussion, except perhaps to indicate some of the sources for its complexity. The absence of comprehensive theoretical answers indicates the importance of empirical determinations of forecast value; as it stands, the United States is poorly equipped to make intelligent decisions in the current and future budget situations. To obtain credible empirical answers, forecasters will need to develop closer working relationships with their users than ever before, seeking specific information regarding economic value of forecasts. Some suggestions for developing plausible value estimates are offered, based largely on limited studies already in the literature. Efforts to create closer ties between forecasters and users can yield diverse benefits, including the desired credible estimates of the value of forecasts, as well as estimates of the sensitivity of that value to changes in accuracy of the forecasts. The authors argue for the development of an infrastructure to make these empirical value estimates, as a critical need within weather forecasting agencies, public and private, in view of continuing budget pressures.
Abstract
While many studies have looked at the quality of forecast products, few have attempted to understand the relationship between them. We begin to consider whether or not such an influence exists by analyzing storm-based tornado warning product metrics with respect to whether they occurred within a severe weather watch and, if so, what type of watch they occurred within. The probability of detection, false alarm ratio, and lead time all show a general improvement with increasing watch severity. In fact, the probability of detection increased more as a function of watch-type severity than the change in probability of detection during the time period of analysis. False alarm ratio decreased as watch type increased in severity, but with a much smaller magnitude than the difference in probability of detection. Lead time also improved with an increase in watch-type severity. Warnings outside of any watch had a mean lead time of 5.5 min, while those inside of a particularly dangerous situation tornado watch had a mean lead time of 15.1 min. These results indicate that the existence and type of severe weather watch may have an influence on the quality of tornado warnings. However, it is impossible to separate the influence of weather watches from possible differences in warning strategy or differences in environmental characteristics that make it more or less challenging to warn for tornadoes. Future studies should attempt to disentangle these numerous influences to assess how much influence intermediate products have on downstream products.
Abstract
While many studies have looked at the quality of forecast products, few have attempted to understand the relationship between them. We begin to consider whether or not such an influence exists by analyzing storm-based tornado warning product metrics with respect to whether they occurred within a severe weather watch and, if so, what type of watch they occurred within. The probability of detection, false alarm ratio, and lead time all show a general improvement with increasing watch severity. In fact, the probability of detection increased more as a function of watch-type severity than the change in probability of detection during the time period of analysis. False alarm ratio decreased as watch type increased in severity, but with a much smaller magnitude than the difference in probability of detection. Lead time also improved with an increase in watch-type severity. Warnings outside of any watch had a mean lead time of 5.5 min, while those inside of a particularly dangerous situation tornado watch had a mean lead time of 15.1 min. These results indicate that the existence and type of severe weather watch may have an influence on the quality of tornado warnings. However, it is impossible to separate the influence of weather watches from possible differences in warning strategy or differences in environmental characteristics that make it more or less challenging to warn for tornadoes. Future studies should attempt to disentangle these numerous influences to assess how much influence intermediate products have on downstream products.
Abstract
The Storm Prediction Center issues four categorical convective outlooks with lead times as long as 48 h, the so-called day 3 outlook issued at 1200 UTC, and as short as 6 h, the day 1 outlook issued at 0600 UTC. Additionally, there are four outlooks issued during the 24-h target period (which begins at 1200 UTC on day 1) that serve as updates to the last outlook issued prior to the target period. These outlooks, issued daily, are evaluated over a relatively long period of record, 1999–2011, using standard verification measures to assess accuracy; practically perfect forecasts are used to assess skill. Results show a continual increase in the skill of all outlooks during the study period, and increases in the frequency at which these outlooks are skillful on an annual basis.
Abstract
The Storm Prediction Center issues four categorical convective outlooks with lead times as long as 48 h, the so-called day 3 outlook issued at 1200 UTC, and as short as 6 h, the day 1 outlook issued at 0600 UTC. Additionally, there are four outlooks issued during the 24-h target period (which begins at 1200 UTC on day 1) that serve as updates to the last outlook issued prior to the target period. These outlooks, issued daily, are evaluated over a relatively long period of record, 1999–2011, using standard verification measures to assess accuracy; practically perfect forecasts are used to assess skill. Results show a continual increase in the skill of all outlooks during the study period, and increases in the frequency at which these outlooks are skillful on an annual basis.
Abstract
One of the challenges of providing probabilistic information on a multitude of spatiotemporal scales is ensuring that information is both accurate and useful to decision-makers. Focusing on larger spatiotemporal scales (i.e., from convective outlook to weather watch scales), historical severe weather reports are analyzed to begin to understand the spatiotemporal scales that hazardous weather events are contained within. Reports from the Storm Prediction Center’s report archive are placed onto grids of differing spatial scales and then split into 24-h convective outlook days (1200–1200 UTC). These grids are then analyzed temporally to assess over what fraction of the day a single location would generally experience severe weather events. Different combinations of temporal and spatial scales are tested to determine how the reference class (or the choice of what scales to use) alters the probabilities of severe weather events. Results indicate that at any given point in the United States on any given day, more than 95% of the daily reports within 40 km of the point occur in a 4-h period. Therefore, the SPC 24-h convective outlook probabilities can be interpreted as 4-h convective outlook probabilities without a significant change in meaning. Additionally, probabilities and threat periods are analyzed at each location and different times of year. These results indicate little variability in the duration of severe weather events, which allows for a consistent definition of an “event” for all locations in the continental United States.
Abstract
One of the challenges of providing probabilistic information on a multitude of spatiotemporal scales is ensuring that information is both accurate and useful to decision-makers. Focusing on larger spatiotemporal scales (i.e., from convective outlook to weather watch scales), historical severe weather reports are analyzed to begin to understand the spatiotemporal scales that hazardous weather events are contained within. Reports from the Storm Prediction Center’s report archive are placed onto grids of differing spatial scales and then split into 24-h convective outlook days (1200–1200 UTC). These grids are then analyzed temporally to assess over what fraction of the day a single location would generally experience severe weather events. Different combinations of temporal and spatial scales are tested to determine how the reference class (or the choice of what scales to use) alters the probabilities of severe weather events. Results indicate that at any given point in the United States on any given day, more than 95% of the daily reports within 40 km of the point occur in a 4-h period. Therefore, the SPC 24-h convective outlook probabilities can be interpreted as 4-h convective outlook probabilities without a significant change in meaning. Additionally, probabilities and threat periods are analyzed at each location and different times of year. These results indicate little variability in the duration of severe weather events, which allows for a consistent definition of an “event” for all locations in the continental United States.
