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the available statistical guidance for predicting outbreak characteristics particularly when combined with other models. Fig . 1. Example tornado clusters. Each point is the tornadogenesis location shaded by EF rating. The black line is the spatial extent of the tornadoes occurring on that convective day and is defined by the minimum convex hull encompassing the set of locations. In this paper, we focus on outbreaks rather than on individual tornadoes. The larger space and time scales associated
the available statistical guidance for predicting outbreak characteristics particularly when combined with other models. Fig . 1. Example tornado clusters. Each point is the tornadogenesis location shaded by EF rating. The black line is the spatial extent of the tornadoes occurring on that convective day and is defined by the minimum convex hull encompassing the set of locations. In this paper, we focus on outbreaks rather than on individual tornadoes. The larger space and time scales associated
Abstract
Tornado–hazard assessment is hampered by a population bias in the available data. Here, the authors demonstrate a way to statistically quantify this bias using the ratio of city to country report densities. The expected report densities come from a model of the number of reports as a function of distance from the nearest city center. On average since 1950, reports near cities with populations of at least 1000 in a 5.5° latitude × 5.5° longitude region centered on Russell, Kansas, exceed those in the country by 70% [54%, 84%; 95% confidence interval (CI)]. The model is applied to 10-yr moving windows to show that the percentage is decreasing with time. Over the most recent period (2002–11), the tornado report density in the city is slightly fewer than 3 reports (100 km2)−1 (100 yr)−1, and this value is statistically indistinguishable from the report density in the country. On average, the population bias is less pronounced for Fujita (F) scale F0 tornadoes, but the bias disappears more quickly over time for the F1 and stronger tornadoes. The authors show evidence that this decline could be related in part to an increase in the number of storm chasers. The population-bias model can enhance the usefulness of the Storm Prediction Center's tornado database and help create more meaningful spatial climatologies.
Abstract
Tornado–hazard assessment is hampered by a population bias in the available data. Here, the authors demonstrate a way to statistically quantify this bias using the ratio of city to country report densities. The expected report densities come from a model of the number of reports as a function of distance from the nearest city center. On average since 1950, reports near cities with populations of at least 1000 in a 5.5° latitude × 5.5° longitude region centered on Russell, Kansas, exceed those in the country by 70% [54%, 84%; 95% confidence interval (CI)]. The model is applied to 10-yr moving windows to show that the percentage is decreasing with time. Over the most recent period (2002–11), the tornado report density in the city is slightly fewer than 3 reports (100 km2)−1 (100 yr)−1, and this value is statistically indistinguishable from the report density in the country. On average, the population bias is less pronounced for Fujita (F) scale F0 tornadoes, but the bias disappears more quickly over time for the F1 and stronger tornadoes. The authors show evidence that this decline could be related in part to an increase in the number of storm chasers. The population-bias model can enhance the usefulness of the Storm Prediction Center's tornado database and help create more meaningful spatial climatologies.
. Senkbeil , 2014 : Factors contributing to tornadogenesis in landfalling Gulf of Mexico tropical cyclones . Meteor. Appl. , 21 , 940 – 947 , https://doi.org/10.1002/met.1437 . 10.1002/met.1437 Senkbeil , J. C. , and S. C. Sheridan , 2006 : A postlandfall hurricane classification system for the United States . J. Coastal Res. , 22 , 1025 – 1034 , https://doi.org/10.2112/05-0532.1 . 10.2112/05-0532.1 Senkbeil , J. C. , D. M. Brommer , P. G. Dixon , M. E. Brown , and K. Sherman
. Senkbeil , 2014 : Factors contributing to tornadogenesis in landfalling Gulf of Mexico tropical cyclones . Meteor. Appl. , 21 , 940 – 947 , https://doi.org/10.1002/met.1437 . 10.1002/met.1437 Senkbeil , J. C. , and S. C. Sheridan , 2006 : A postlandfall hurricane classification system for the United States . J. Coastal Res. , 22 , 1025 – 1034 , https://doi.org/10.2112/05-0532.1 . 10.2112/05-0532.1 Senkbeil , J. C. , D. M. Brommer , P. G. Dixon , M. E. Brown , and K. Sherman
, and Tennessee. Quick Response Rep. 165, Natural Hazards Research Applications and Information Center, Boulder, CO, 27 pp. [Available online at www.colorado.edu/hazards/research/qr/qr165/qr165.pdf .] Rhodes, C. M. , and Senkbeil J. C. , 2014 : Factors contributing to tornadogenesis in landfalling Gulf of Mexico tropical cyclones . Meteor. Appl. , 21, 940–947 , doi: 10.1002/met.1437 . Ripberger, J. T. , Silva C. L. , Jenkins-Smith H. C. , Carlson D. E. , James M. , and Herron K. G
, and Tennessee. Quick Response Rep. 165, Natural Hazards Research Applications and Information Center, Boulder, CO, 27 pp. [Available online at www.colorado.edu/hazards/research/qr/qr165/qr165.pdf .] Rhodes, C. M. , and Senkbeil J. C. , 2014 : Factors contributing to tornadogenesis in landfalling Gulf of Mexico tropical cyclones . Meteor. Appl. , 21, 940–947 , doi: 10.1002/met.1437 . Ripberger, J. T. , Silva C. L. , Jenkins-Smith H. C. , Carlson D. E. , James M. , and Herron K. G
appear more associated with a tornado (e.g., stronger ground wind, damaging hail), even though the likelihood of a tornado event is much lower. Therefore, these cues cannot directly reveal the presence of a tornado (or the potential for tornadogenesis), and likelihood judgments of tornadoes based on these environmental cues may not accurately represent the actual likelihood (for a more detailed discussion, see Broomell 2020 ). Additional evidence supports our theory that DMs reasonably attempt to
appear more associated with a tornado (e.g., stronger ground wind, damaging hail), even though the likelihood of a tornado event is much lower. Therefore, these cues cannot directly reveal the presence of a tornado (or the potential for tornadogenesis), and likelihood judgments of tornadoes based on these environmental cues may not accurately represent the actual likelihood (for a more detailed discussion, see Broomell 2020 ). Additional evidence supports our theory that DMs reasonably attempt to
” events) occur when a tornado is reported, and no tornado warning was issued by the time of tornadogenesis ( Brotzge and Erickson 2010 ). Research examining unwarned tornado events found that approximately 25% of tornadoes from 2000 to 2004 fall into this category, with most rated as weak tornadoes that do not result in fatalities ( Brotzge and Erickson 2010 ). Unfortunately, a negative result of unwarned tornadoes is that they may breed public distrust in the NWS and warning process if
” events) occur when a tornado is reported, and no tornado warning was issued by the time of tornadogenesis ( Brotzge and Erickson 2010 ). Research examining unwarned tornado events found that approximately 25% of tornadoes from 2000 to 2004 fall into this category, with most rated as weak tornadoes that do not result in fatalities ( Brotzge and Erickson 2010 ). Unfortunately, a negative result of unwarned tornadoes is that they may breed public distrust in the NWS and warning process if
some places primarily because the soil was already saturated. Future studies of TORFFs may benefit from a longitudinal analysis of an event rather than a snapshot during the warning overlap. This will help reconcile the features that affect the flash flooding over the longer period with those supporting tornadogenesis. As mentioned in Rogash and Racy (2002) , sometimes the tornado and flooding came from separate events. Second, from the social science perspective, we are missing critical
some places primarily because the soil was already saturated. Future studies of TORFFs may benefit from a longitudinal analysis of an event rather than a snapshot during the warning overlap. This will help reconcile the features that affect the flash flooding over the longer period with those supporting tornadogenesis. As mentioned in Rogash and Racy (2002) , sometimes the tornado and flooding came from separate events. Second, from the social science perspective, we are missing critical
