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Topographic Implications on Tornado Climatology

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Topographic Implications on Tornado Climatology Oberlin College, Dept. of Geology May 2016 Topographic Implications! for Tornado Climatology Kirk Pearson Geology, Oberlin College Oberlin, OH 44074 [email protected]! ! SUMMARY ! Reports of North America’s infamous tornado A U.S. map is then generated that colors areas alley date back to the mid-17th century, yet its based on tornado likelihood (Figure III). mythic image is somewhat misguided. Based Virtually all areas with high storm density, on large-scale data analysis, the United States including the tornado clusters, are located in lacks a singular “alley” of storms and instead the midwest and Great Plains. This is due to contains a patchwork of high density tornado the regions being disproportionately flat, clusters (Frates 2012). Each of these clusters balanced in moisture levels, and bordered by has a statistically significant outlier in regards two major mountain ranges to the east and to tornado frequency— the most dense of west (Brooks 2004). These conditions these outliers has over one thousand times the generate high cyclostrophic balance— an national average. While these clusters have atmospheric state that gives rise to the vast been previously described, little is known majority of tornadoes (Frye and Mote 2010). about why specific North American locations ! have such incredible tornadic potential. An Plotted on a North American map, the five understanding of these conditions could lead high-density clusters do not share any to better accuracy in storm prediction and obvious geographic similarities. However, increased safety for highly prone regions. tornado frequencies do not correspond ! exclusively to latitude (Mercer et al. 2008) as The following study reports on similarities much as other limiting factors, such as between these clustered regions and describes topography (Weaver 2012) and precipitation a model for regional tornado prediction based (Galaway 1979). By overlaying a digital on topography and precipitation. Due to the elevation map as well as annual rainfall data, highly unpredictable and short-lived nature of similarities between tornado clusters become these storms, tornado climatology typically much more apparent. Zonal statistics are analyzes historical datasets of storm pathways calculated on these high potential regions, (Smith and Marsh 2016). Data from the illuminating the correlation between tornado NOAA Severe Weather Project is used to clustering and these environmental calculate the pathways of all reported characteristics. tornadoes from 1950 though 2015. Storms are ! visualized in ArcGIS by algorithmically The five largest U.S. tornado clusters all share graphing a line between each storm’s two narrow topographic and climactic ranges terminal points (Figure II). Tornado clusters (Figures VII and VIII). These factors attain a are quantified by calculating the density of balance of optimal storm cohesion, updraft, storm pathways in every region of the and low atmospheric density. While several continental United States. other factors that contribute to tornado !1 of !11 Oberlin College, Dept. of Geology May 2016 climatology have been described, such as soil moisture. The resulting storms, called moisture (Frye and Mote 2010), decade-long supercells, form when uprising warm air weather oscillations (Enfield et al. 2001) and meets two rapidly moving currents global climate change (Diffenbaugh et al. (Rasmussen et al. 1998). Depending on the 2008), this study suggests that storms can be storm’s level of moisture, a supercell’s predicted with significant acuity as a function rotation may increase due to especially high of local topography and precipitation. This is cyclostrophic balance. If rotation is at a high especially significant as both of these enough velocity, downdrafting winds will pull statistical measures are easily collected in the cloud vortex to the ground, forming the virtually all regions of the world— even those tornado’s visible wind column. that do not have official geographic ! databases. Storms across tornado alley are not randomly ! scattered, but tend to have a preference for a With this model, a map of all regions on Earth small number of very specific locations that satisfy the elevation and climate ranges is (Figure III). Understanding the propensity of generated (Figure IX). Not only does this these clusters to form tornado-rich supercells model bear striking resemblance to maps of allows for us to correlate storm probability tornado distribution (Brooks 2004), but also with other local factors. As supercells depend suggests at the possible presence of tornado greatly on moisture levels and elevational clusters in underreported areas. These areas, stability, we can look for correlations between such as Southeast Africa and Patagonia, have regional topography and precipitation on few official storm reporting services as well number of tornadoes reported. as low population densities. This model ! suggests the presence of never before This study seeks to find a relationship described tornado alleys, that very possibly between geography and tornado presence and exist despite the lack of reported incidents. to use this function as a model to predict ! tornado frequency across the globe. North INTRODUCTION America is an excellent place to base the The North American tornado alley spans the study off of, due to both its high number of majority of the midwest and Great Plains, in storms and availability of reliable, the plateau between the United States’ two government-regulated data. major mountain ranges (Figure II). This ! region reports more tornadoes than any other METHODS in the world by a large margin, due to the The NOAA Severe Weather Project was area’s ideal tornadic conditions (Weaver et al. founded in 1950 to increase understanding of 2012). Air currents from both coasts deadly storms and aid the ability to predict experience a rapid drop in pressure due to the them. Since then, the database has come to quick geomorphic transition from mountain to amass over one hundred thousand reported plain. This decrease leads to a proportional tornadoes in the continental United States increase in fluid velocity (Brooks et al. 2003), (Smith and Marsh 2016). In addition to the destabilizing the currents of cooler air. Rising estimated wind speeds, damage, and death condensation from these air currents leads to counts, the dataset also contains intense updraft and increased stratospheric geocoordinates of the start and endpoints of !2 of !11 Oberlin College, Dept. of Geology May 2016 Define Points to tornado_ NOAA_tornado.csv Line Projection pathways Turns terminal points into In attribute table, Spreadsheet of Tornado visible lines (Fig I) remove all storms with Data, with terminal magnitude 0 endpoints plotted Line Reclassify tornado_zones Density NAmerica_DEM Plots five zones of high tornado frequency (Fig II) NAmerica_Ann_ Torn_Elev_Stat Precip Zonal Statistics Create as Table Vertical Bar Torn_Precip_Stat Zonal statistics for high tornado Layer packages for global data regions (Fig IV and V) Global_DEM Global_Precip Graphs of distributions (Fig VI Elev_Dist Precip_Dist and VII) Reclassify Map of world with Zone III elevation AND precipitation (Fig IX) Global_ZoneIII_Elev Global_ZoneIII_Precip Intersect global_torn_clusters Global areas with Zone III elevation and precipitation Figure I: A workflow of this study in ArcGIS, intended for the ease of replication. !3 of !11 Oberlin College, Dept. of Geology May 2016 Tornado Data: NOAA Elevation Data: USGS Figure II: An image of all NOAA-reported EF1-EF5 tornadoes (1950-2015) with highest density clusters circled. each tornado pathway (workflow starting with alley. Zone III regions, the densest 5%, show Figure I). Isolating tornadoes with an EF0 the areas with especially high risk. Zones IV ranking on the Enhanced Fujita-Pearson scale and V pose the highest risk for tornado — those with wind speeds under 72 mph formation, having densities respectively in the (Glickman 2000), a map that connects all top 1% and 0.1% of the study area. terminal points shows us the distribution of ! all EF1-EF5 tornadoes reported to the It should be noted, however, that cluster database (Figure II). identification is fraught with multiple ! categorization issues. A study by Michael The software ArcGIS can be used to Frates, for instance, finds four clusters instead categorize the map of storm distribution into of five as it concerns itself only with EF3- areas of varying storm density using the line EF5 tornadoes with a 20-mile path. As the density algorithm (Figure III). Zone I regions map of storm densities (Figure III) does not represent the entire study area and are not discriminate between tornadoes based on their indicated on the map. Zone II regions indicate magnitude, allowances must be made in order low density, and are most obviously a to prevent bias in statistical methods. This reflection of topography. The Rocky study, focusing on all non-EF0 tornadoes, Mountain region, for example, has an does not claim that high-frequency clusters elevation where air currents are too stable for have the most deadly tornadoes. The average tornadoes to form (Weaver et al. 2012). The storm magnitude in each of the clusters shows region highlights the rough outline of tornado a small range of 1.77-1.95 on the Enhanced !4 of !11 Oberlin College, Dept. of Geology May 2016 Tornado Density Regions Zone II (10%) Zone III (5%) Zone IV (1%) Zone V (.1%) Tornado Data: NOAA Elevation Data: USGS Figure III: An image of U.S. tornado density against topography. Zone I regions (the lowest density) are not shown. Zone II shows regions with the 10% highest density, Zone III with the highest 5% and so forth. River Cluster: Avg. Mag: 1.9296 Opportunity Cluster: Avg. Mag: 1.78497 Witchita Cluster: Avg. Mag: 1.959 Dixie Clusters (Joined): Avg. Mag: 1.77921 Tornado Data: NOAA Elevation Data: USGS Figure IV: An image that shows the average magnitude of tornadoes on the Enhanced Fujita-Pearson Scale (EF1- EF5), purely to illustrate that high density regions need not be regions with abnormally high magnitudes. !5 of !11 Oberlin College, Dept. of Geology May 2016 !Fujita-Pearson scale (Figure IV).
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