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Chad M. Shafer, Andrew E. Mercer, Michael B. Richman, Lance M. Leslie, and Charles A. Doswell III


The areal extent of severe weather parameters favorable for significant severe weather is evaluated as a means of identifying major severe weather outbreaks. The first areal coverage method uses kernel density estimation (KDE) to identify severe weather outbreak locations. A selected severe weather parameter value is computed at each grid point within the region identified by KDE. The average, median, or sum value is used to diagnose the event’s severity. The second areal coverage method finds the largest contiguous region where a severe weather parameter exceeds a specified threshold that intersects the KDE region. The severe weather parameter values at grid points within the parameter exceedance region are computed, with the average, median, or sum value used to diagnose the event’s severity. A total of 4057 severe weather outbreaks from 1979 to 2008 are analyzed. An event is considered a major outbreak if it exceeds a selected ranking index score (developed in previous work), and is a minor event otherwise. The areal coverage method is also compared to Storm Prediction Center (SPC) day-1 convective outlooks from 2003 to 2008. Comparisons of the SPC forecasts and areal coverage diagnoses indicate the areal coverage methods have similar skill to SPC convective outlooks in discriminating major and minor severe weather outbreaks. Despite a seemingly large sample size, the rare-events nature of the dataset leads to sample size sensitivities. Nevertheless, the findings of this study suggest that areal coverage should be tested in a forecasting environment as a means of providing guidance in future outbreak scenarios.

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Matthew D. Biddle, Ryan P. Brown, Charles A. Doswell III, and David R. Legates


Previously published claims of large regional (northern vs southern states) differences in risks of fatality associated with tornadoes in the United States are reexamined. This new study extends earlier claims to include 1) data from a much longer time frame, 2) injuries as well as fatalities, and 3) more precise estimates of meteorological features of tornado events (specifically, a precise calculation of daytime vs nighttime and pathlength). The current study also includes formal mediation analyses involving variables that might explain regional differences. Results indicate that significant increases in the risk of fatality and injury do occur in southern states as compared with northern states. Mediation models show that these regional differences remain significant when meteorological factors of nocturnal occurrence and pathlength are included. Thus, these meteorological factors cannot explain regional differences in risk of fatality and injury, a failure that is unlikely to reflect a lack of data or a lack of precision in the measurement of potential mediators.

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David M. Schultz, John V. Cortinas Jr., and Charles A. Doswell III


Wetzel and Martin present an ingredients-based methodology for forecasting winter season precipitation. Although they are to be commended for offering a framework for winter-weather forecasting, disagreements arise with some of their specific recommendations. In particular, this paper clarifies the general philosophy of ingredients-based methodologies and shows how the methodology presented by Wetzel and Martin has the potential to be misinterpreted by their choice of diagnostics (including their PVQ and the so-called traditional techniques) and their use of cloud microphysics. Given that winter-weather forecasts are imperfect at present, this paper advocates continued exploration of scientifically based forecasting techniques.

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Andrew E. Mercer, Chad M. Shafer, Charles A. Doswell III, Lance M. Leslie, and Michael B. Richman


Tornadoes often strike as isolated events, but many occur as part of a major outbreak of tornadoes. Nontornadic outbreaks of severe convective storms are more common across the United States but pose different threats than do those associated with a tornado outbreak. The main goal of this work is to distinguish between significant instances of these outbreak types objectively by using statistical modeling techniques on numerical weather prediction output initialized with synoptic-scale data. The synoptic-scale structure contains information that can be utilized to discriminate between the two types of severe weather outbreaks through statistical methods. The Weather Research and Forecast model (WRF) is initialized with synoptic-scale input data (the NCEP–NCAR reanalysis dataset) on a set of 50 significant tornado outbreaks and 50 nontornadic severe weather outbreaks. Output from the WRF at 18-km grid spacing is used in the objective classification. Individual severe weather parameters forecast by the model near the time of the outbreak are analyzed from simulations initialized at 24, 48, and 72 h prior to the outbreak. An initial candidate set of 15 variables expected to be related to severe storms is reduced to a set of 6 or 7, depending on lead time, that possess the greatest classification capability through permutation testing. These variables serve as inputs into two statistical methods, support vector machines and logistic regression, to classify outbreak type. Each technique is assessed based on bootstrap confidence limits of contingency statistics. An additional backward selection of the reduced variable set is conducted to determine which variable combination provides the optimal contingency statistics. Results for the contingency statistics regarding the verification of discrimination capability are best at 24 h; at 48 h, modest degradation is present. By 72 h, the contingency statistics decline by up to 15%. Overall, results are encouraging, with probability of detection values often exceeding 0.8 and Heidke skill scores in excess of 0.7 at 24-h lead time.

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Chad M. Shafer, Andrew E. Mercer, Charles A. Doswell III, Michael B. Richman, and Lance M. Leslie


Uncertainty exists concerning the links between synoptic-scale processes and tornado outbreaks. With continuously improving computer technology, a large number of high-resolution model simulations can be conducted to study these outbreaks to the storm scale, to determine the degree to which synoptic-scale processes appear to influence the occurrence of tornado outbreaks, and to determine how far in advance these processes are important. To this end, 50 tornado outbreak simulations are compared with 50 primarily nontornadic outbreak simulations initialized with synoptic-scale input using the Weather Research and Forecasting (WRF) mesoscale model to determine if the model is able to distinguish the outbreak type 1, 2, and 3 days in advance of the event. The model simulations cannot resolve tornadoes explicitly; thus, the use of meteorological covariates (in the form of numerous severe-weather parameters) is necessary to determine whether or not the model is predicting a tornado outbreak. Results indicate that, using the covariates, the WRF model can discriminate outbreak type consistently at least up to 3 days in advance. The severe-weather parameters that are most helpful in discriminating between outbreak types include low-level and deep-layer shear variables and the lifting condensation level. An analysis of the spatial structures and temporal evolution, as well as the magnitudes, of the severe-weather parameters is critical to diagnose the outbreak type correctly. Thermodynamic instability parameters are not helpful in distinguishing the outbreak type, primarily because of a strong seasonal dependence and convective modification in the simulations.

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Erik N. Rasmussen, Jerry M. Straka, Robert Davies-Jones, Charles A. Doswell III, Frederick H. Carr, Michael D. Eilts, and Donald R. MacGorman

This paper describes the Verification of the Origins of Rotation in Tornadoes Experiment planned for 1994 and 1995 to evaluate a set of hypotheses pertaining to tornadogenesis and tornado dynamics. Observations of state variables will be obtained from five mobile mesonet vehicles, four mobile ballooning laboratories, three movie photography teams, portable Doppler radar teams, two in situ tornado instruments deployment teams, and the T-28 and National Atmospheric and Oceanic Administration P-3 aircraft. In addition, extensive use will be made of the new generation of observing systems, including the WSR-88D Doppler radars, demonstration wind profiler network, and National Weather Service rawinsondes.

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Harold E. Brooks, Charles A. Doswell III, Xiaoling Zhang, A. M. Alexander Chernokulsky, Eigo Tochimoto, Barry Hanstrum, Ernani de Lima Nascimento, David M. L. Sills, Bogdan Antonescu, and Brad Barrett


The history of severe thunderstorm research and forecasting over the past century has been a remarkable story involving interactions between technological development of observational and modeling capabilities, research into physical processes, and the forecasting of phenomena with the goal of reducing loss of life and property. Perhaps more so than any other field of meteorology, the relationship between researchers and forecasters has been particularly close in the severe thunderstorm domain, with both groups depending on improved observational capabilities.

The advances that have been made have depended on observing systems that did not exist 100 years ago, particularly radar and upper-air systems. They have allowed scientists to observe storm behavior and structure and the environmental setting in which storms occur. This has led to improved understanding of processes, which in turn has allowed forecasters to use those same observational systems to improve forecasts. Because of the relatively rare and small-scale nature of many severe thunderstorm events, severe thunderstorm researchers have developed mobile instrumentation capabilities that have allowed them to collect high-quality observations in the vicinity of storms.

Since much of the world is subject to severe thunderstorm hazards, research has taken place around the world, with the local emphasis dependent on what threats are perceived in that area, subject to the availability of resources to study the threat. Frequently, the topics of interest depend upon a single event, or a small number of events, of a particular kind that aroused public or economic interests in that area. International cooperation has been an important contributor to collecting and disseminating knowledge.

As the AMS turns 100, the range of research relating to severe thunderstorms is expanding. The time scale of forecasting or projecting is increasing, with work going on to study forecasts on the seasonal to subseasonal time scales, as well as addressing how climate change may influence severe thunderstorms. With its roots in studying weather that impacts the public, severe thunderstorm research now includes significant work from the social science community, some as standalone research and some in active collaborative efforts with physical scientists.

In addition, the traditional emphases of the field continue to grow. Improved radar and numerical modeling capabilities allow meteorologists to see and model details that were unobservable and not understood a half century ago. The long tradition of collecting observations in the field has led to improved quality and quantity of observations, as well as the capability to collect them in locations that were previously inaccessible. Much of that work has been driven by the gaps in understanding identified by theoretical and operational practice.

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Pieter Groenemeijer, Tomáš Púčik, Alois M. Holzer, Bogdan Antonescu, Kathrin Riemann-Campe, David M. Schultz, Thilo Kühne, Bernold Feuerstein, Harold E. Brooks, Charles A. Doswell III, Hans-Joachim Koppert, and Robert Sausen


The European Severe Storms Laboratory (ESSL) was founded in 2006 to advance the science and forecasting of severe convective storms in Europe. ESSL was a grassroots effort of individual scientists from various European countries. The purpose of this article is to describe the 10-yr history of ESSL and present a sampling of its successful activities. Specifically, ESSL developed and manages the only multinational database of severe weather reports in Europe: the European Severe Weather Database (ESWD). Despite efforts to eliminate biases, the ESWD still suffers from spatial inhomogeneities in data collection, which motivates ESSL’s research into modeling climatologies by combining ESWD data with reanalysis data. ESSL also established its ESSL Testbed to evaluate developmental forecast products and to provide training to forecasters. The testbed is organized in close collaboration with several of Europe’s national weather services. In addition, ESSL serves a central role among the European scientific and forecast communities for convective storms, specifically through its training activities and the series of European Conferences on Severe Storms. Finally, ESSL conducts wind and tornado damage assessments, highlighted by its recent survey of a violent tornado in northern Italy.

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