The Decreasing Population Bias in Tornado Reports across the Central Plains

Elsner, James B.; Michaels, Laura; Scheitlin, Kelsey N.; Elsner, Ian J.

doi:10.1175/wcas-d-12-00040.1

Cited by 60 publications

(45 citation statements)

References 10 publications

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“…In this way, urban properties can be as vulnerable as the weakest in their immediate vicinity and damage levels have some dependence on building density. However, the similar tornado severity distributions between smaller cities and very rural areas implied by the data in Elsner et al (2013), and mentioned above, suggest that debris loading may not be a factor between these two contrasting areas. Published estimates of the contribution of missiles to total tornado damage do not exist, because it is difficult to distinguish the contribution due to engineering or construction aspects from those due to complex tornadic wind fields.…”

Section: Stronger Tornadoes In Metro Areasmentioning

confidence: 56%

“…Their results indicate that touchdown densities in the most rural parts are statistically indistinguishable from smaller cities in their latest 2002-2011 period for F0, F1 and F2+ categories. Taking into account that Elsner et al (2013) study a much larger contrast and much lower values of population densities than used here, their results are a strong indication of little differential in missing tornadoes between metro and non-metro areas in this analysis. In summary, the evidence indicates that missed tornadoes explain little of the raised tornado frequency in metro areas.…”

Section: Increased Total Tornado Occurrence In Metro Areasmentioning

confidence: 68%

“…The minimum population density of any zip used in the twenty non-metro areas exceeds 20 persons km −2 and significantly exceeds the limits found by King (1997) and Newark (1983). Elsner et al (2013) quantified the population bias as a function of distance from city centre, where their cities are more similar to the non-metro zips used in this study, and large rural areas have population densities often below 1 person km −2 in their study area around Kansas. Their results indicate that touchdown densities in the most rural parts are statistically indistinguishable from smaller cities in their latest 2002-2011 period for F0, F1 and F2+ categories.…”

Section: Increased Total Tornado Occurrence In Metro Areasmentioning

confidence: 99%

“…4. Elsner et al (2013) examine touchdown densities as a function of distance from the nearest population centre, and resolve the severity distribution into three categories, F0, F1 and F2+ in their Figs. 11 to 13.…”

Section: Stronger Tornadoes In Metro Areasmentioning

confidence: 99%

See 3 more Smart Citations

Increased tornado hazard in large metropolitan areas

Cusack

2014

Atmospheric Research

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Section: Stronger Tornadoes In Metro Areasmentioning

confidence: 56%

Section: Increased Total Tornado Occurrence In Metro Areasmentioning

confidence: 68%

Section: Increased Total Tornado Occurrence In Metro Areasmentioning

confidence: 99%

Section: Stronger Tornadoes In Metro Areasmentioning

confidence: 99%

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Increased tornado hazard in large metropolitan areas

Cusack

2014

Atmospheric Research

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“…This is not the case in general as a single thunderstorm can spawn a cluster of tornadoes over a compact area [9]. In addition, tornado reports tend to be more numerous near cities as compared to rural areas but this spatial variation is decreasing with time [10]. Improvements in observing practices over time tend to result in more tornado reports, especially reports of weak tornadoes [11,12].…”

Section: Introductionmentioning

confidence: 99%

Statistical models for predicting tornado rates: Case studies from Oklahoma and the Mid South USA

Elsner¹,

Fricker²,

Jagger³

et al. 2016

Int. J. SAFE

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The destructive impact tornadoes have on communities has sparked interest in predicting the risk of impacts on seasonal time scales. Here, the authors demonstrate how to build statistical models for predicting tornado rates. They test the models with tornado counts accumulated over a 45-year period aggregated to counties in the State of Oklahoma and to cells in a latitude/longitude grid across a large portion of south central United States. The spatial model provides a fit to the counts, which includes terms for the spatial correlation and the population effect. A space-time model not only provides a similar fit to annual counts but also includes a term for a time-varying climate factor. This work contributes to methods for forecasting severe convective storms on the seasonal time scale. Keywords: climate, risk prediction, space-time model, statistical model, tornadoes. 1 INTRODUCTION Seasonal climate forecasts are now a matter of routine. Predictions of how much rain and heat can be expected during the summer are issued during spring by weather agencies across the globe, by region and by country. Even single seasonal predictions of, say how many hurricanes can be expected along a coastline are available and accurate enough to warrant attention by the property insurance industry. However, what is missing from the suite of seasonal forecasting products are long-range forecasts of severe convective storm activity. Potentially useful skills (accuracy above random guess) at predicting tornado activity prior to the start of the season has been noted recently [1,2]. However, given the large gaps in our knowledge of how climate influences severe weather and the dearth of methods to forecast it on the seasonal scale, basic and applied research is needed, which focuses on statistical modeling, diagnostic understanding, and methods to predict. A major impediment to issuing seasonal convective storm forecasts is that the events of interest are too small (e.g. tornado) to be resolved within the current dynamical forecast models. Long-lead predictions of severe weather environments can be made with dynamical models but the necessary conditions do not sufficiently distinguish between days with and without tornadoes.An alternative approach is to fit a statistical model to a historical tornado database. Climate patterns related to active and inactive seasons provide the essential information to make predictions. However, population growth and changes to procedures for rating tornadoes result in a heterogeneous database. Various methods for dealing with data artifacts have been proposed [3][4][5] with most assuming a uniform region of activity and estimating occurrence rates within a subset of the region likely to be most accurate. For example, tornado reports are

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Generalised additive point process models for natural hazard occurrence

Youngman

Economou

2017

Environmetrics

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Point processes are a natural class of models for representing occurrences of various types of natural hazard event. Flexibly implementing such models is often hindered by intractable likelihood forms. Consequently, the rates of point processes tend to be reduced to parametric forms, or the processes are discretised to give data of readily modelled “count‐per‐unit” type. This work proposes generalised additive model forms for point process rates. The resulting low‐rank spatiotemporal representations of rates, coupled with the Laplace approximation, makes the restricted likelihood relatively tractable and hence inference for such models possible. The models can also be interpreted from a regression perspective. The proposed models are used to estimate different types of Cox process and then spatiotemporal variation in European windstorms. Through a combination of thin‐plate and cubic regression splines and their tensor product, established relationships between where windstorms occur and the state of the North Atlantic Oscillation are confirmed and then expanded to bring detailed understanding of within‐year variation, which has otherwise not been possible with count‐based models.

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The Decreasing Population Bias in Tornado Reports across the Central Plains

Cited by 60 publications

References 10 publications

Increased tornado hazard in large metropolitan areas

Increased tornado hazard in large metropolitan areas

Statistical models for predicting tornado rates: Case studies from Oklahoma and the Mid South USA

Generalised additive point process models for natural hazard occurrence

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