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Changes over the years in tornado-warning performance in the United States can be modeled from the perspective of signal detection theory. From this view, it can be seen that there have been distinct periods of change in performance, most likely associated with deployment of radars, and changes in scientific understanding and training. The model also makes it clear that improvements in the false alarm ratio can only occur at the cost of large decreases in the probability of detection, or with large improvements in the overall quality of the warning system.
Changes over the years in tornado-warning performance in the United States can be modeled from the perspective of signal detection theory. From this view, it can be seen that there have been distinct periods of change in performance, most likely associated with deployment of radars, and changes in scientific understanding and training. The model also makes it clear that improvements in the false alarm ratio can only occur at the cost of large decreases in the probability of detection, or with large improvements in the overall quality of the warning system.
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
Very few studies on the occurrence of tornadoes in Poland have been performed and, therefore, their temporal and spatial variability have not been well understood. This article describes an updated climatology of tornadoes in Poland and the major problems related to the database. In this study, the results of an investigation of tornado occurrence in a 100-yr historical record (1899–1998) and a more recent 15-yr observational dataset (1999–2013) are presented. A total of 269 tornado cases derived from the European Severe Weather Database are used in the analysis. The cases are divided according to their strength on the F scale with weak tornadoes (unrated/F0/F1; 169 cases), significant tornadoes (F2/F3/F4; 66 cases), and waterspouts (34 cases). The tornado season extends from May to September (84% of all cases) with the seasonal peak for tornadoes occurring over land in July (23% of all land cases) and waterspouts in August (50% of all waterspouts). On average 8–14 tornadoes (including 2–3 waterspouts) with 2 strong tornadoes occur each year and 1 violent one occurs every 12–19 years. The maximum daily probability for weak and significant tornadoes occurs between 1500 and 1800 UTC while it occurs between 0900 and 1200 UTC for waterspouts. Tornadoes over land are most likely to occur in the south-central part of the country known as the “Polish Tornado Alley.” Cases of strong, and even violent, tornadoes that caused deaths indicate that the possibility of a large-fatality tornado in Poland cannot be ignored.
Abstract
Very few studies on the occurrence of tornadoes in Poland have been performed and, therefore, their temporal and spatial variability have not been well understood. This article describes an updated climatology of tornadoes in Poland and the major problems related to the database. In this study, the results of an investigation of tornado occurrence in a 100-yr historical record (1899–1998) and a more recent 15-yr observational dataset (1999–2013) are presented. A total of 269 tornado cases derived from the European Severe Weather Database are used in the analysis. The cases are divided according to their strength on the F scale with weak tornadoes (unrated/F0/F1; 169 cases), significant tornadoes (F2/F3/F4; 66 cases), and waterspouts (34 cases). The tornado season extends from May to September (84% of all cases) with the seasonal peak for tornadoes occurring over land in July (23% of all land cases) and waterspouts in August (50% of all waterspouts). On average 8–14 tornadoes (including 2–3 waterspouts) with 2 strong tornadoes occur each year and 1 violent one occurs every 12–19 years. The maximum daily probability for weak and significant tornadoes occurs between 1500 and 1800 UTC while it occurs between 0900 and 1200 UTC for waterspouts. Tornadoes over land are most likely to occur in the south-central part of the country known as the “Polish Tornado Alley.” Cases of strong, and even violent, tornadoes that caused deaths indicate that the possibility of a large-fatality tornado in Poland cannot be ignored.
Abstract
In the United States, tornado activity of a given year is usually assessed in terms of the total number of human-reported tornadoes. Such assessments fail to account for the seldom-acknowledged fact that an active (or inactive) tornado year for the United States does not necessarily equate with activity (or inactivity) everywhere in the country. The authors illustrate this by comparing the geospatial tornado distributions from 1987, 2004, and 2011. Quantified in terms of the frequency of daily tornado occurrence (or “tornado days”), the high activity in the South Atlantic and upper Midwest regions was a major contributor to the record-setting number of tornadoes in 2004. The high activity in 2011 arose from significant tornado occurrences in the Southeast and lower Midwest. The authors also show that the uniqueness of the activity during these years can be determined by modeling the local statistical behavior of tornado days by a gamma distribution.
Abstract
In the United States, tornado activity of a given year is usually assessed in terms of the total number of human-reported tornadoes. Such assessments fail to account for the seldom-acknowledged fact that an active (or inactive) tornado year for the United States does not necessarily equate with activity (or inactivity) everywhere in the country. The authors illustrate this by comparing the geospatial tornado distributions from 1987, 2004, and 2011. Quantified in terms of the frequency of daily tornado occurrence (or “tornado days”), the high activity in the South Atlantic and upper Midwest regions was a major contributor to the record-setting number of tornadoes in 2004. The high activity in 2011 arose from significant tornado occurrences in the Southeast and lower Midwest. The authors also show that the uniqueness of the activity during these years can be determined by modeling the local statistical behavior of tornado days by a gamma distribution.
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
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
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
The Storm Prediction Center has issued daily convective outlooks since the mid-1950s. This paper represents an initial effort to examine the quality of these forecasts. Convective outlooks are plotted on a latitude–longitude grid with 80-km grid spacing and evaluated using storm reports to calculate verification measures including the probability of detection, frequency of hits, and critical success index. Results show distinct improvements in forecast performance over the duration of the study period, some of which can be attributed to apparent changes in forecasting philosophies.
Abstract
The Storm Prediction Center has issued daily convective outlooks since the mid-1950s. This paper represents an initial effort to examine the quality of these forecasts. Convective outlooks are plotted on a latitude–longitude grid with 80-km grid spacing and evaluated using storm reports to calculate verification measures including the probability of detection, frequency of hits, and critical success index. Results show distinct improvements in forecast performance over the duration of the study period, some of which can be attributed to apparent changes in forecasting philosophies.
Abstract
Among the Storm Prediction Center’s (SPC) probabilistic convective outlook products are forecasts specifically targeted at significant severe weather: tornadoes that produce EF2 or greater damage, wind gusts of at least 75 mi h−1, and hail with diameters of 2 in. or greater. During the period of 2005–15, for outlooks issued beginning on day 3 and through the final update to the day 1 forecast, the accuracy and skill of these significant severe outlooks are evaluated. To achieve this, criteria for the identification of significant severe weather events were developed, with a focus on determining days for which outlooks were not issued, but should have been based on the goals of the product. Results show that significant tornadoes and hail are generally well identified by outlooks, but significant wind events are underforecast. There exist differences between verification measures when calculating them based on 1) only those days for which outlooks were issued and 2) days with outlooks or missed events; specifically, there were improvements in the frequency of daily skillful forecasts when disregarding missed events. With the greatest number of missed events associated with significant wind events, forecasts for this hazard are identified as an area of future focus for the SPC.
Abstract
Among the Storm Prediction Center’s (SPC) probabilistic convective outlook products are forecasts specifically targeted at significant severe weather: tornadoes that produce EF2 or greater damage, wind gusts of at least 75 mi h−1, and hail with diameters of 2 in. or greater. During the period of 2005–15, for outlooks issued beginning on day 3 and through the final update to the day 1 forecast, the accuracy and skill of these significant severe outlooks are evaluated. To achieve this, criteria for the identification of significant severe weather events were developed, with a focus on determining days for which outlooks were not issued, but should have been based on the goals of the product. Results show that significant tornadoes and hail are generally well identified by outlooks, but significant wind events are underforecast. There exist differences between verification measures when calculating them based on 1) only those days for which outlooks were issued and 2) days with outlooks or missed events; specifically, there were improvements in the frequency of daily skillful forecasts when disregarding missed events. With the greatest number of missed events associated with significant wind events, forecasts for this hazard are identified as an area of future focus for the SPC.
