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Patrick King

- line damaging winds. This is discussed further in Joe et al. (1995) All events in the database were used in this analysis except those without an accurate location or with an unknown F scale. These events composed about 10% of the original database. Newark (1984) included the possible events to retain“events at the lower end of the probability scale for corroboration or rejection at a later time as more information becomes available.” He then treated all three categories on an equal basis in

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R. E. Abdel-Aal and M. A. Elhadidy

echowere considered to contain dense cirrus rather thanone of the cell stages. When nonprecipitating cell typesy and d are assigned in the analysis, they are not allowedto develop in the forecast procedure because of uncertainties about their existence and about which of themmay subsequently develop. However, type y is also assigned to daughter cells forecast by the scheme, and isused then as the starting point for predicting the development of those cells. Once the most probable dominant cell stage

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Makenzie J. Krocak, Jinan N. Allan, Joseph T. Ripberger, Carol L. Silva, and Hank C. Jenkins-Smith

Abstract

Nocturnal tornadoes are challenging to forecast and even more challenging to communicate. Numerous studies have evaluated the forecasting challenges, but fewer have investigated when and where these events pose the greatest communication challenges. This study seeks to evaluate variation in confidence among US residents in receiving and responding to tornado warnings by hour-of-day. Survey experiment data comes from the Severe Weather and Society Survey, an annual survey of US adults. Results indicate that respondents are less confident about receiving warnings overnight, specifically in the early morning hours (12 AM to 4 AM local time). We then use the survey results to inform an analysis of hourly tornado climatology data. We evaluate where nocturnal tornadoes are most likely to occur during the time frame when residents are least confident in their ability to receive tornado warnings. Results show that the Southeast experiences the highest number of nocturnal tornadoes during the time period of lowest confidence, as well as the largest proportion of tornadoes in that time frame. Finally, we estimate and assess two multiple linear regression models to identify individual characteristics that may influence a respondent’s confidence in receiving a tornado between 12 AM and 4 AM. These results indicate that age, race, weather awareness, weather sources, and the proportion of nocturnal tornadoes in the local area relate to warning reception confidence. The results of this study should help inform policymakers and practitioners about the populations at greatest risk for challenges associated with nocturnal tornadoes. Discussion focuses on developing more effective communication strategies, particularly for diverse and vulnerable populations.

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Gregory E. Gahrs, Scott Applequist, Richard L. Pfeffer, and Xu-Feng Niu

(December–March). The predictors used were Nested Grid Model (NGM) gridded analyses and predictions of various meteorological quantities for the period December 1992–March 1996 obtained from the National Center for Atmospheric Research (NCAR) archive (available online at http://dss.ucar.edu/datasets/ds069.5/ ). This method is known as model output statistics (MOS; Glahn and Lowry 1972 ). The statistical methodologies included linear regression, discriminant analysis, logistic regression, neural

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Paul W. Mielke Jr., Kenneth J. Berry, Christopher W. Landsea, and William M. Gray

1. Introduction Meteorologists have long recognized the importance of accurately quantifying statistical forecast skill. One of the primary tools of meteorological forecasting is multiple regression analysis ( Murphy and Winkler 1984 ) where, given data on a response variable y i and associated predictor variables x ij , where j = 1, . . . , p ; i = 1, . . . , n ; p denotes the number of predictors; and n represents the number of events; the goal is to find some function of the x

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Darrell R. Massie and Mark A. Rose

) were usually more accurate as well. It was also shown that dramatic changes in airmass moisture characteristics can have a significant effect on temperature forecasting. During December–March, most large eta regression forecast errors occurred during times when much drier air moved into the region. A further analysis involving the eta regression forecasts and NGM MOS yielded more conclusive results regarding the use of regression forecasts as an aid in forecasting maximum temperatures. In Nashville

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Abdoulaye Deme, Alain Viltard, and Pierre de Félice

difficult to miss small rainfall amounts. 5. Conclusions Sixty-five indices were computed using NCEP–NCAR reanalyses. Several indices were taken from the literature that had already been applied to predict rainfall occurrences, tornadoes, or storms in western Africa, the United States, and France. Some other indices such as NCAPE (index 3, Table 1 ) were computed and included in the analysis. Using all of the indices, linear regression equations were established to predict daily rain amount classes in

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Sim D. Aberson

due to many rawinsonde sites in the Caribbean Basin reporting only every 24 h. The calculation of the statistical separation time between forecasts (AD) also confirms that correlations between successive forecasts are not very large. Despite these relatively small correlations, all possible combinations of the lagged-average predictors, and also those related to the recent performance of VICBAR, are tested by the linear regression analysis, though only some are included in the final analyses. If

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Ariel E. Cohen, Joel B. Cohen, Richard L. Thompson, and Bryan T. Smith

present. Meanwhile, the present study continues to extend the foundation that the previously mentioned studies have laid, specifically in terms of providing a multivariable analysis of tornado probabilities and tornado wind speeds based upon damage ratings. In particular, this present work will employ multivariable regression analysis to develop linear statistical models ( Pindyck and Rubinfeld 1981 ) that simultaneously combine many of the variables that Smith et al. (2015) and Thompson et al

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William F. Ryan, Charles A. Piety, and Eric D. Luebehusen

institute voluntary pollution controls (ozone action days) based on a forecast of high [O 3 ] (code red). b. Operational procedure The forecasts are prepared using output from a multiple linear regression algorithm as forecast guidance, which is then supplemented by expert analysis. The forecast guidance is developed, as discussed in more detail below, using the perfect prognosis approach ( Klein et al. 1959 ) in which a statistical relationship is determined between observed values of the

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