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Harold E. Brooks

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.

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Harold E. Brooks
and
James Correia Jr.

Abstract

Tornado warnings are one of the flagship products of the National Weather Service. We update the time series of various metrics of performance in order to provide baselines over the 1986–2016 period for lead time, probability of detection, false alarm ratio, and warning duration. We have used metrics (mean lead time for tornadoes warned in advance, fraction of tornadoes warned in advance) that work in a consistent way across the official changes in policy for warning issuance, as well as across points in time when unofficial changes took place. The mean lead time for tornadoes warned in advance was relatively constant from 1986 to 2011, while the fraction of tornadoes warned in advance increased through about 2006, and the false alarm ratio slowly decreased. The largest changes in performance take place in 2012 when the default warning duration decreased, and there is an apparent increased emphasis on reducing false alarms. As a result, the lead time, probability of detection, and false alarm ratio all decrease in 2012.

Our analysis is based, in large part, on signal detection theory, which separates the quality of the warning system from the threshold for issuing warnings. Threshold changes lead to trade-offs between false alarms and missed detections. Such changes provide further evidence for changes in what the warning system as a whole considers important, as well as highlighting the limitations of measuring performance by looking at metrics independently.

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Makenzie J. Krocak
and
Harold E. Brooks

Abstract

While there has been an abundance of research dedicated to the seasonal climatology of severe weather, very little has been done to study hazardous weather probabilities on smaller scales. To this end, local hourly climatological estimates of tornadic event probabilities were developed using storm reports from NOAA’s Storm Prediction Center. These estimates begin the process of analyzing tornado frequencies on a subdaily scale.

Characteristics of the local tornado climatology are investigated, including how the diurnal cycle varies in space and time. Hourly tornado probabilities are peaked for both the annual and diurnal cycles in the plains, whereas the southeast United States has a more variable pattern. Areas that have similar total tornado threats but differ in the distribution of that threat are highlighted. Additionally, areas that have most of the tornado threat concentrated in small time frames both annually and diurnally are compared to areas that have a low-level threat at all times. These differences create challenges related to staffing requirements and background understanding of the tornado threat unique to each region.

This work is part of a larger effort to provide background information for probabilistic forecasts of hazardous weather that are meaningful over broad time and space scales, with a focus on scales broader than the typical time and space scales of the events of interest (including current products on the “watch” scale). A large challenge remains to continue describing probabilities as the time and space scales of the forecast become comparable to the scale of the event.

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Makenzie J. Krocak
and
Harold E. Brooks

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.

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Nathan M. Hitchens
and
Harold E. Brooks

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.

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Nathan M. Hitchens
and
Harold E. Brooks

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.

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Makenzie J. Krocak
and
Harold E. Brooks

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.

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Nathan M. Hitchens
and
Harold E. Brooks

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.

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Makenzie J. Krocak
and
Harold E. Brooks

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.

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Harold E. Brooks
and
Charles A. Doswell III

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.

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