Dixie Alley: Fact Or Fallacy : an in Depth Analysis of Tornado Distribution in Alabama

Mississippi State University Scholars Junction

Theses and Dissertations Theses and Dissertations

1-1-2004

Dixie alley: Fact or Fallacy : An In Depth Analysis of Tornado Distribution in Alabama

Kristin Nichole Hurley

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DIXIE ALLEY:FACT OR FALLACY

AN IN DEPTH ANALYSIS OF

TORNADO DISTRIBUTION

IN ALABAMA

Kristin Nichole Hurley

A Thesis Submitted to the Faculty of Mississippi State University in Partial Fulfillment of the Requirements for the Degree of Master of Science in Geoscience in the Department of Geosciences

Mississippi State, Mississippi

May 2004

Kristin Nichole Hurley

2004

DIXIE ALLEY: FACT OR FALLACY

AN IN DEPTH ANALYSIS OF

TORNADO DISTRIBUTION

IN ALABAMA

Kristin Nichole Hurley

______Michael E. Brown Charles L. Wax Assistant Professor of Geosciences Professor of Geosciences (Director of Thesis) (Committee Member)

______John C. Rodgers, III John E. Mylroie Assistant Professor of Geosciences Graduate Coordinator of the Department (Committee Member) of Geosciences

______Mark S. Binkley Philip B. Oldham Professor and Head of the Department of Dean and Professor of the College of Geosciences Arts and Sciences

Name: Kristin Nichole Hurley

Date of Degree: May 8, 2004

Institution: Mississippi State University

Major Field: Geoscience

Major Professor: Dr. Michael E. Brown

Title of Study: DIXIE ALLEY: FACT OR FALLACY AN IN DEPTH ANALYSIS OF TORNADO DISTRIBUTION IN ALABAMA

Pages in Study: 82

Candidate for Degree of Master of Science

Alabama, also known as the Dixie state, is no stranger to severe weather.

Severe weather can occur during much of the year. Experienced local forecasters have long suspected that North and Central Alabama has its own tornado alley. Many

of these forecasters have noticed storm tracks as well as tornado tracks to be similar

to past historic events. Many questions have risen about the exact influential factors

that cause convective initiation and tornadic development. For example the effects of

terrain, water, and population on tornado climatology will be discussed in this study.

The sometimes unreliable climatology of tornadoes will be addressed as well as the

history of storm reporting. Tornado clusters were found and further explained

regarding relationships with terrain, water, and population. Through this research, it

is concluded that there are two distinct tornado regions that exist in North and Central

Alabama.

ACKNOWLEDGEMENTS

I would like to thank my advisor, Dr. Michael E. Brown, for making this thesis and the defense possible. I appreciate all of the hard work and dedication he has contributed to this research. Much appreciation is due for my committee members, Dr. Charles L. Wax and Dr. John C Rodgers, III. I would also like to thank several of my co-workers at the National Weather Service Office in Birmingham,

Alabama for giving me words of encouragement daily and keeping me straight on task. The Meteorologist in Charge, Kenneth Graham, also deserves many thanks because if it were not for him, I would not have the opportunities in the National

Weather Service as I do today. I would also like to express my gratitude to my parents for their love and support.

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS...... ii

LIST OF TABLES...... iv

LIST OF FIGURES ...... v

CHAPTER

I. INTRODUCTION ...... 1

Introduction...... 1 Objectives ...... 3 Hypothesis...... 5

II. REVIEW OF LITERATURE ...... 6

III. DATA AND METHODS ...... 19

IV. RESULTS AND DISCUSSIONS...... 22

Spatial Distributions...... 22 Temporal Distribution...... 31 Tornado Threat...... 40 Directional Threat Contours ...... 61 Inferences and Interpretations...... 64 Future Investigations...... 76

V. CONCLUSIONS...... 79

LITERATURE CITED ...... 81

iii

LIST OF TABLES

TABLE Page

1 Fujita Tornado Intensity Scale...... 4 2 Raw Data of Counties, County Seats, Latitudes, Longitudes, Population, and Elevation ...... 44 3 ACF Numbers for All Tornadoes for County Seats...... 46 4 Top 15 ACF Numbers for County Seats...... 48 5 Bottom 15 ACF Numbers for County Seats ...... 48 6 County Seats Top 15 in Population illustrated with All ACF Numbers...... 49 7 County Seats Bottom 15 in Population illustrated with All ACF Numbers...... 49 8 County Seats Top 15 in Elevation with All ACF Numbers...... 50 9 County Seats Bottom 15 in Elevation with All ACF Numbers ...... 50 10 ACF Numbers for Significant Tornadoes for County Seats ...... 51 11 Top 15 Significant ACF Numbers for County Seats ...... 53 12 Bottom 15 Significant ACF Numbers for County Seats...... 53 13 County Seats Top 15 in Population illustrated with Significant ACF Numbers ...... 54 14 County Seats Bottom 15 in Population illustrated with Significant ACF Numbers ...... 54 15 County Seats Top 15 in Elevation with Significant ACF Numbers ...... 55 16 County Seats Bottom 15 in Elevation with Significant ACF Numbers...... 55

LIST OF FIGURES

FIGURE Page

1 Topography map of Alabama ...... 2 2 F2 and Greater to All Tornadoes between 1950-1992...... 7 3 Annual number of violent, strong, and weak tornadoes in the United States...... 9 4 Tornado path plots for the eastern half of the United States between 1880-1982 ...... 17 5 Same as Figure 4 only zoomed in for Alabama...... 18 6 Plot of F0 Tornadoes between 1950-1999...... 23 7 Plot of F1 Tornadoes between 1950-1999...... 24 8 Plot of F2 Tornadoes between 1950-1999...... 25 9 Plot of F3 Tornadoes between 1950-1999...... 26 10 Plot of F4 Tornadoes between 1950-1999...... 27 11 Plot of F5 Tornadoes between 1950-1999...... 28 12 Plot of Significant Tornadoes (F2-F5) between 1950-1999 ...... 29 13 Plot of All Tornadoes (F2-F5) between 1950-1999...... 30 14 Distribution of All Tornadoes according to Time of Day...... 32 15 Monthly Distribution of All Tornadoes ...... 33 16 Yearly Distribution of All Tornadoes...... 34 17 Distribution of Tornado Intensities...... 35 18 Distribution of Significant Tornadoes (F2-F5) according to Time of Day...... 36 19 Monthly Distribution of Significant Tornadoes (F2-F5...... 37 20 Yearly Distribution of Significant Tornadoes (F2-F5 ...... 38 21 Distribution of Significant Tornado Intensities ...... 39 22 Map of County Seats and Counties in Alabama ...... 41

FIGURE Page

23 Current Threat Contours for All Tornadoes...... 42 24 Current Threat Contours for Significant Tornadoes (F2-F5...... 43 25 Cluster Map of All Tornadoes ...... 56 26 Cluster Map for Significant Tornadoes...... 57 27 County Population Map ...... 58 28 Rural Population Map...... 59 29 Metropolitan Statistical Areas Map ...... 60 30 Directional Threat Sectors for All Tornadoes between 1950-1999...... 62 31 Directional Threat Sectors for Significant Tornadoes between 1950-1999 ...... 63 32 Counties with the Top 15 Highest ACF Numbers...... 75

CHAPTER I

INTRODUCTION

Alabama, the “heart of Dixie”, is no stranger to severe weather yet, relatively few research studies have been conducted concerning severe weather much less tornadoes in the state. In fact, due to the lower frequency of severe weather events when compared to the Great Plains, which is commonly referred to as Tornado Alley, research in the

Southeastern United States is very limited (Anthony 1988). This is surprising considering that tornadoes in Alabama are common anytime of the year. Alabama averages twenty-three tornadoes a year within its sixty-seven counties. The state usually experiences two distinct seasons of severe weather. One is the traditional spring severe weather season and the other is a late severe weather season usually around November.

Both severe weather seasons are a consequence of both warm and cold air masses clashing as the seasons change. An increase in the number of tornadoes reported during the month of November is addressed in research conducted by Ostby (1993). According to the study, there has always been a slight increase of tornadoes during the November months, but in the past ten years, a significant increase on the order of 146% has been documented (Ostby 1993).

Alabama has an assortment of geographic and topographic features as illustrated in Figure 1. The Tennessee River makes up what is known as the Tennessee Valley in

Figure 1: Topography map of Alabama.

Northern Alabama. The southernmost reaches of the Appalachian Mountains extend through the Northeast and Central regions of the state with the highest peak in Clay

County with an elevation of 2,407 feet. The west and south central regions of the state consist of mainly flat land.

Statistically, Alabama ranks first in the percentage of significant tornadoes

(those rated as F2 or higher on the Fujita Intensity Scale) (See Table 1) between the years 1953-1991 (Grazulis 1993). However a misconception has formed through the years that tornadoes are well documented. In fact, many tornadoes go unreported.

The public’s awareness and geographical parameters make tornado climatologies somewhat unreliable since these storms are not reported consistently from county to county (Tecson et al., 1983).

Objectives:

The primary objective of this research is to conduct a thorough analysis of the tornado events from 1950-1999 in Alabama and identify any spatial or temporal cluster patterns that may exist. A further explanation will be provided to correlate any clustered patterns found to the topography, land use, and population of Alabama.

Temporal patterns will be analyzed for the diurnal, monthly, and yearly patterns of tornadoes in Alabama. Through analysis of tornado initiation points, I hope to show that a Dixie Tornado Alley does exist in North and Central Alabama.

Table 1: Fujita Tornado Intensity Scale.

Fujita Tornado Intensity Scale Scale Winds Damage F0 40-72 Light F1 73-112 Moderate F2 113-157 Considerable F3 158-206 Severe F4 207-260 Devastating F5 261-318 Incredible

Significant tornadoes (F2-F5) from 1950-1999 will also be plotted and analyzed for potential spatial and temporal cluster patterns. Spatial patterns of significant tornadoes will be evaluated as it is theorized that significant tornadoes rarely occur along the Gulf of Mexico. Temporal patterns will also be evaluated to relate different tornado outbreaks with time of year; more specifically months and time of day will be closely analyzed. This information will provide a climatological foundation so meteorologists can become better prepared once a specific tornado season is approaching.

Not only can this research provide valuable information to meteorologists and forecasters in Alabama, but also a wide variety of professions can take advantage of the findings. These include engineers, architects, researchers, insurance companies, emergency managers, and many more.

Hypothesis:

I. Tornadoes in Alabama show a significant clustered pattern.

II. The clustering of tornadoes is related to changes in terrain and population.

III. A tornado alley does exist in North and Central Alabama.

CHAPTER II

REVIEW OF LITERATURE

In Alabama, tornadoes have occurred in every county in the state. There are

several localized maxima tornado frequencies, which may lead meteorologists and

researchers to believe that a Southeastern United States tornado alley may exist. This

tornado alley, which will be referred to as “Dixie Alley” in this paper, is a smaller

scale tornado alley in terms of spatial size and number of tornadoes when compared

to the classic Great Plains Tornado Alley.

In a study conducted by Livingston and Schaefer (1995), strong and

significant tornadoes occur in a large area from Southern Illinois and Central Indiana

southward to Southern Alabama and Mississippi and from Northwest Georgia and

Alabama westward to Southeast Oklahoma and Arkansas. As seen in Figure 2, the

North and Central counties in Alabama are highlighted to demonstrate that over fifty percent of the tornadoes that are reported in these counties are F2 or greater in intensity (Livingston and Schaefer 1995). In fact, there is a greater concentration of counties with fifty percent or more of the tornadoes F2 or greater in the Southeast when compared to the Great Plains. There is a significant difference of reported strong and violent tornadoes between the North and Central Alabama counties to the

South Alabama counties. Influences due to sea breezes and tropical cyclones inhibit

6 7

Figure 2: F2 and Greater to All Tornadoes between 1950-1992. The bold counties are over 50 percent of all reported tornadoes are strong and violent. (From Livingston and Schaefer 1995).

the southern tier of the state from observing any large quantity of strong and violent tornadoes. In fact, South Alabama has a smoother interior due to the low elevations near the Gulf of Mexico compared to the hilly terrain of North and Central Alabama.

A correlation is then developed from supported facts and data that the more stronger and violent tornadoes form in rougher terrain (North and Central Alabama) than in the coastal plain of South Alabama (Livingston and Schaefer 1995).

A recent trend in tornado reports is a noticeable increase in weak tornadoes.

Many speculated reasons for this increase include additional training and education of the public and meteorologists leading to better awareness for these weak tornadoes.

As illustrated in Figure 3, tornado trends through the years are compared between weak (F0-F1), strong (F2-F3), and violent (F4-F5) tornadoes in the United States indicate that there is a slow increase in reported tornadoes mainly due to the increase of weak tornadoes (Weiss and Vescio 1998). Annually, the violent tornado reports have stayed fairly constant while the strong tornado reports have decreased slightly since 1955 (Weiss and Vescio 1998).

In order for a storm to be reported, someone’s life has to be directly or indirectly effected by that storm. The responsibility of collecting severe weather data has fallen upon local National Weather Service offices across the country. The

National Weather Service has conveniently defined severe weather events as wind speeds above 50 knots, 0.75 inches of hail or greater, and a tornado (Anthony 1988).

Figure 3: Annual number of violent (F4-F5), strong (F2-F3), and weak (F0-F1) tornadoes in the United States. (From Weiss and Vescio 1998).

Since the National Weather Service offices issue the watches and warnings, it makes sense that storm data be collected and maintained by the local agencies.

According to Changnon Jr. (1982), three phases have been encountered during the tornado data collection process. During the years of 1916 through 1953, a state director collected newspaper clippings, mailed storm reports from cooperative observers, and monitored word of mouth from the community. From 1954 through

1972, a concentrated effort in relaying weather awareness and safety to residents was provided by the U.S. Weather Bureau (later named the National Weather Service).

Along with awareness efforts, the U.S. Weather Bureau changed its classification system for tornadoes, used newspaper clipping services, and formed a State

Climatologist position within each state. These changes resulted in an increase in the frequency of tornado reports (Changnon Jr. 1982). However, from this data arose the problem of population bias related to regions of high population density. In other words, if even a strong tornado hits nothing but trees in a forest, chances are that the

National Weather Service will never hear about the event. Yet if a weak short-lived tornado occurs near a population center, it will likely be seen and properly reported.

Therefore, the official record of tornado occurrences is likely biased in populated areas.

During the years of 1973 through 1980, reductions in the National Weather

Service prevented most offices from going into the field to verify a tornado report

(Changnon Jr. 1982). If damage occurred from a possible tornado, it was likely

recorded as an actual tornado but did not have ground truth. These actions led to a discontinuity in tornado climatology. From 1981 to the present, most reports of tornadoes are well documented and surveyed by a trained National Weather Service meteorologist. Technology and training of the public has helped National Weather

Service officials in collecting storm data. These advancements include the training of storm spotters, emergency managers, media, and law enforcement. In addition, the general public often provides pictures, video, and other information which help the

National Weather Service to determine the intensity of a tornado and its damage

(Edwards 2003).

During the 1990’s, the National Weather Service created the warning verification program which placed a heightened importance on verifying severe weather warnings. This program was developed in hopes of gaining further information to help protect lives downstream (Edwards 2003). However, F-scale assignments are made upon personal interpretations, which leads to the vulnerability of creating false classifications (Twisdale 1982). In Figure 3, the annual U.S. tornado frequency is shown.

Storm reports:

The trend of the increase of tornado reports is likely the result of an increase in population, improved storm reporting communications, and the establishment of emergency management agencies as well as emergency warning systems (Twisdale

1982). Other factors of gathering storm information within the past ten years include

more trained storm spotters, ham radio operators, and media providing non-stop severe weather coverage. With radio, telephone, and internet as means of communications, warnings and reports of severe weather are received in a more timely matter when compared to history (Doswell et. al. 1998). In fact, means of communications have allowed a situation in which ground truth including tornadoes, wind, and/or hail will result in the National Weather Service issuing a warning.

In the entire storm data event archives, tornado reports are the most likely to be incorrect due to false reports or the lack of an event to be reported (Ostby 1993).

Often straight line wind damage is mistakenly identified as a weak tornado. These winds may produce damage similar to that of a tornado and may even occur with clouds that can be mistaken as funnel clouds (Changnon Jr. 1982). When looking at tornado climatologies, weak tornadoes must be further researched or perhaps ignored due to the inconsistent nature of reporting. In fact, most tornado climatologies are performed using only significant (F2-F5) tornado data.

According to research done by Schaefer and Galway (1982), weak tornadoes are typically reported in a uniform distribution except in population centers where they are over reported. Some weak tornadoes may not be reported in Alabama due to rough terrain, the lack of population densities, and possibly the mistaken identity of small tornado damage for straight line wind damage (Grazulis 1993). Some weak tornadoes may be mistakenly categorized due to a tornado hitting open country or no significant structures. Hence, these weak tornadoes in rural areas are almost always

under classified (Ostby 1993). In fact, there is a trend where only violent and significant tornadoes are reported in populated areas. For this reason, killer tornadoes must occur where there is a population density near the path of the tornado (Schaefer and Galway 1982).

Errors in storm data verification are quite common among severe weather climatological data. A number of reasons affect the quality and accuracy of a tornado and/or other severe weather data including: the credibility of the report, the effects of terrain and water, population densities, accuracy of the storm survey, night time storm events, and strength of the tornado (Weiss and Vescio 1998).

The lifespan, structure, and initiation of tornadoes are heavily dependent on wind shear profiles (Schmid and Lehre 1998). Topography can therefore influence the near-surface wind structure and thereby disrupt or enhance tornado formation.

Understanding the influences of terrain on supercells is a difficult task evidenced by the fact that research is extremely limited in this area (Bracken et. al. 1998).

According to Wasula, et. al. (2002), local forecasters in the New England region have theorized that several different mountain ranges and river valleys affect the beginning of convection and severe reports. Many severe weather events are never reported due to high elevations and the lack of population. It is certain that there are fewer severe weather reports in the highest elevations when compared to river valleys (Wasula et.al. 2002). Another influence that terrain has on an area is the microscale effects during a severe thunderstorm or more specifically to a tornado. In a study by Grazulis

and Abbey (1983), terrain roughness may increase low level inflow which may produce greater vertical velocities and enhance tornadic development. In a study conducted by Bracken et. al. (1998), the Great Barrington, Massachusetts tornado was enhanced by low level wind shear which increased a microscale shearing instability and caused vortex tube stretching just prior to tornadogenesis over the Catskill Creek

Valley and Hudson River Valley.

Dependent on the storm characteristics and motion, extreme roughness may lead to a situation where inflow to a storm decreases and tornado formation becomes unlikely (Grazulis and Abbey 1983). It is theorized that the changes in intensities in the low level mesocyclone may be due to an increase of terrain height, which may shrink the vortex tube, and vice versa when the terrain decreases in height (Bracken et. al. 1998). A key factor in the formation and evolution of severe thunderstorms is vertical wind shear. Severe weather parameters such as storm relative helicity and

Bulk Richardson Number are a few indices that help a meteorologist fully understand the vertical wind shear profile and help forecast severe weather. However, this vertical wind shear can be weakened or improved by terrain elevations (Schmid and

Lehre 1998).

It is unclear exactly what role forests, different soil types, and bodies of water take during convective initiation or even during severe weather. These three factors are all apart of the fragile microclimatology that exists on the local scale. Forests, soil, lakes, and rivers can influence the weather positively and negatively on an

everyday basis. Terrain roughness (excluding hills and mountains in this case) and evapotranspiration are just a few meteorological influences, which must be taken in account when considering tornado climatology (Livingston and Schaefer 1995).

According to Knupp and Garinger, the Southeast United States displays spatial inconsistency of severe weather diurnal patterns mostly due to a baroclinic zone formed by the contrast of land and ocean (1993). As mentioned above, most tornadoes spawned in the southern third of Alabama are due to tropical cyclones or sea breezes. Supercell thunderstorm development is rare but not out of the question.

Research indicates that lake effect breezes as well as sea breezes can extend several miles over land (Knupp and Garinger 1993). A sea breeze is displayed clearly in a study which states that there is a secondary peak in tornado production along the

Northern Gulf Coast during the morning hours (Knupp and Garinger 1993). With the additional wind shear that sea breezes provide, an explanation is formed as to why a secondary peak is seen in only tornado production and not in hail or wind damage.

It should be noted that this weather phenomenon does not occur along the Atlantic

Coast which supports the explanation of sea breezes causing weak tornadoes (Knupp and Garinger 1993).

Population density is a major contributor when collecting storm reports. The public and their awareness is the sole reason for success in severe weather warnings

(Doswell et. al. 1998). It is up to the individual to take the necessary actions during severe weather. The public is the main key in reporting severe weather. If no one

sees the tornado or its damage, the event may go unreported. This is very true when considering weak tornadoes.

Population through the years has changed drastically in the United States. An increasing population and urbanization can affect tornado statistics, because more people are able to witness a severe weather event (Weiss and Vescio 1998). Since

1925, a population movement began in the eastern half of the United States where citizens began moving away from the rural areas to the more populated cities

(Doswell et. al. 1998). With people moving away from the open country, the eyes and ears of the National Weather Service that were once located out in the rural locations were gone. If a population center is struck by a tornado, we can expect that the death toll and injury reports will rise drastically. Several maxima for tornadoes plotted are evident by the contours illustrated in Figure 4. Many large cities are included in these maxima including Oklahoma City, Oklahoma; St. Louis, Missouri;

Louisville, Kentucky; and locally Huntsville, Alabama as well as Birmingham,

Alabama as seen in Figure 5. Other large cities such as Memphis, Tennessee and

Atlanta, Georgia are not included in the maxima, so it is concluded that population does not solely account for tornado distributions but is a factor when considering tornado prone areas (Grazulis and Abbey 1983).

Figure 4: Tornado path plots for the eastern half of the United State between 1880- 1982. Twenty sub-regional maxima are defined numerically. (From Grazulis and Abbey Jr. 1983).

Figure 5: Same as Figure 4 only zoomed in for Alabama. (From Grazulis and Abbey Jr. 1983).

CHAPTER III

DATA AND METHODS

I will accomplish this study by plotting the spatial distribution of tornadoes for

the study period of 1950-1999. I chose this time frame due to the fact that data before

1950 is generally more unreliable compared to modern tornado reports. I also

examined the spatial and temporal distribution related to terrain, population, and land

use as potential explanations for cluster patterns which may exist.

Storm data was collected from numerous outlets including government agencies such as National Weather Service offices in Alabama, NCDC storm event database, and from Grazulis’s book Significant Tornadoes (1993). Each tornado was plotted by latitude and longitude. Also included in my research were the F-scale rating of the storm, date and time, and county in which the storm occurred.

Methods:

Each tornado occurrence was plotted utilizing the Site Assessment of Tornado

Threat-Third Edition (Satt 3.0) software application. Satt 3.0 software provides a display and analysis of tornado data for anywhere in the United States. For this project, I utilized Alabama tornado data exclusively. Satt 3.0 software includes all of the official National Weather Service data from 1950-2001. Tornado data was collected from 1950-1999 across Alabama. This data was then plotted with Satt 3.0.

19 20

Figures 6-11 indicate each plotted tornado distribution according to F-scale rating. I also plotted the significant tornadoes (F2-F5) and then all tornadoes in Alabama.

Temporal distributions were analyzed according to time of day, monthly, and annual occurrence for all tornadoes and significant tornadoes. A distribution was also developed for the different intensities of each tornado. This was done for all and significant tornadoes. Each section was then compared between the significant and all tornado results.

In an attempt to analyze tornado data properly to find a tornado alley, each county seat for each county had tornado data thoroughly analyzed in a 10 mile radius.

Computer-generated threat contour maps were analyzed for areas that illustrated high level threat areas for tornadoes. Illustrated below are the counties and county seats which were used in this study along with the populations, elevations, and latitude and longitude for each county seat. I then examined the Annual Coverage Fraction which is the total area covered by tornadoes within Alabama divided by the reference area and years of data considered for each county seat’s 10 mile radius. These findings were then listed in order from highest ACF to lowest ACF. The higher the ACF the better chance, statistically, that a tornado will occur and vice versa for the lower ACF.

The ACF statistics for each county seat were compared with each other.

Accomplishing this procedure allowed me to evaluate which city and surrounding area had the potentially greatest threat for tornadoes based on historical data.

Additional information such as population and elevation for each county seat were

also evaluated. The top 15 populated and highest elevated cities were compared with each county seat’s ACF number. This gave further insight on how population and terrain affect tornado reports.

After plotting each tornado using the Sat 3.0 software, clusters were found by visual comparison of tornado initiation points and are illustrated below for all and significant tornadoes. Using the tornado plots, a visual and hand analysis was completed in order to identify any cluster tornado patterns. A relationship between terrain, land use, and population was addressed and explained for each cluster pattern.

Tornado tracks were compared to terrain, land use, and population density maps in order to provide an explanation of tornado initiation and cluster patterns. These cluster patterns were determined by visual comparison of tornado initiation points.

Significant tornado (F2-F5) reports from 1950-1999 were also plotted and used to evaluate where spatial cluster patterns exist.

Threat sectors were developed for all and significant tornadoes. These threat sectors are direction oriented as a compass. These sectors illustrate the direction a tornado was coming from.

CHAPTER IV

RESULTS AND DISCUSSIONS

Spatial Distributions

The spatial distributions of tornado events in Alabama from 1950-1999 are represented in Figures 6-13. This data is from the National Weather Service and was plotted using SATT 3.00 software. Each intensity (F0-F5) of tornadoes is plotted individually. Figures 12 and 13 correspond to all (F0-F5) tornadoes plotted together as well as significant (F2-F5) tornadoes.

Figure 6: Plot of F0 Tornadoes between 1950-1999.

Figure 7: Plot of F1 Tornadoes between 1950-1999.

Figure 8: Plot of F2 Tornadoes between 1950-1999.

Figure 9: Plot of F3 Tornadoes between 1950-1999.

Figure 10: Plot of F4 Tornadoes between 1950-1999.

Figure 11: Plot of F5 Tornadoes between 1950-1999.

Figure 12: Plot of Significant Tornadoes (F2-F5) between 1950-1999.

Figure 13: Plot of All Tornadoes between 1950-1999.

Temporal Distributions:

Temporal distributions were graphed according to time of day, monthly, and annual occurrence for all tornadoes (Figures 14-16) and significant tornadoes (Figures

18-20). A distribution was also developed for the different intensities of each tornado as seen in Figures 17 and 21 utilizing the Fujita Tornado Intensity Scale.

2.5

1.5

1 Average Tornadoes per Hour

0.5

0 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Time

Figure 14: Distribution of All Tornadoes according to the Time of Day.

4.5

3.5

2.5

1.5 Average Tornadoes per Month

0.5

0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Months

Figure 15: Monthly Distribution of All Tornadoes.

30 Number of Tornadoes 20

0 5 0 5 0 5 0 5 0 5 5 5 6 6 7 7 8 8 9 9 19 19 19 19 19 19 19 19 19 19 Years

Figure 16: Yearly Distribution of All Tornadoes.

3 Average Tornadoes per Year Average Tornadoes per Year

0 F0 F1 F2 F3 F4 F5 F-Scale Intensity

Figure 17: Distribution of Tornado Intensities from 1950-1999.

1.2

0.8

0.6

0.4 Average Tornadoes per Hour

0.2

0 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Time

Figure 18: Distribution of Significant Tornadoes (F2-F5) according to the Time of Day.

2.5

1.5

1 Average Significant Tornadoes per Month 0.5

0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Months

Figure 19: Monthly Distribution of Significant Tornadoes (F2-F5).

15 Number of Significant Tornadoes 10

0 5 0 5 0 5 0 5 0 5 5 5 6 6 7 7 8 8 9 9 19 19 19 19 19 19 19 19 19 19 Years

Figure 20: Yearly Distribution of Significant Tornadoes (F2-F5).

2 Average Significant Tornadoes per Year 1

0 F2 F3 F4 F5 F-Scale Intensity

Figure 21: Distribution of Significant Tornado Intensities.

Tornado Threat:

Tornado threat contours were created to color-code areas which have the highest probability of tornadoes. This tornado threat contour is computer generated based on the Annual Coverage Fraction (ACF) values for Alabama. These contour maps were created for all (Figure 23) and significant (Figure 24) tornadoes. The higher the ACF number the greater the chance of a tornado. These contours were created so areas with a higher probability of a tornado are easily seen with the higher values. The ACF was found for all 67 county seats. A radius of ten miles was used around each city to calculate its ACF. It was decided that a smaller radius may not include enough tornado data, thus important information may have been left out. A larger radius may have caused overlapping of cities which would have contaminated the data with a single tornado being counted for possibly two or more city’s radius.

By using a ten mile radius, less overlapping occurred but enough data was extrapolated to complete the purpose of this research. These are listed below in order from maximum ACF to minimum ACF for both all (Table 3) and significant (Table

10) tornadoes. Additional tables were constructed to illustrate the ACF’s for the Top and Bottom 15 county seats in population and elevation for both all and significant tornadoes.

Figure 22: Map of County Seats and Counties in Alabama

Figure 23: Current Threat Contours for All Tornadoes

Figure 24: Current Threat Contours for Significant Tornadoes (F2-F5).

Table 2: Raw Data of Counties, County Seats, Latitudes, Longitudes, Population, and Elevation.

County County Seat Latitude Longitude Population Elevation (ft) Autauga County Prattville 32 27.847 -86 27.584 24,303 325 Baldwin County Bay Minette 30 52.998 -87 46.377 7,820 275 Barbour County Clayton 31 52.691 -85 26.983 1,475 500 Bibb County Centreville 32 56.675 -87 08.322 2,466 300 Blount County Oneonta 33 56.895 -86 28.361 5,576 875 Bullock County Union Springs 32 08.658 -85 42.891 3,670 475 Butler County Greenville 31 49.775 -86 37.060 7,228 450 Calhoun County Anniston 33 39.610 -85 49.893 24,276 800 Chambers County Lafayette 32 53.995 -85 24.068 3,234 825 Cherokee County Centre 34 09.126 -85 40.738 3,216 600 Chilton County Clanton 32 50.332 -86 37.764 7,800 600 Choctaw County Butler 32 05.377 -88 13.315 1,952 150 Clarke County Grove Hill 31 42.525 -87 46.627 1,438 425 Clay County Ashland 33 16.421 -85 50.157 1,965 1075 Cleburne County Heflin 33 38.937 -85 35.245 3,002 975 Coffee County Elba 31 24.874 -86 04.058 4,185 275 Colbert County Tuscumbia 34 43.872 -87 42.144 7,856 450 Conecuh County Evergreen 31 26.016 -86 57.424 3,630 300 Coosa County Rockford 32 53.379 -86 13.184 428 725 Covington County Andalusia 31 18.512 -86 29.005 8,794 350 Crenshaw County Luverne 31 42.993 -86 15.836 2,635 350 Cullman County Cullman 34 10.488 -86 50.611 13,995 800 Dale County Ozark 31 27.539 -85 38.423 15,119 325 Dallas County Selma 32 24.449 -87 01.261 20,512 125 DeKalb County Fort Payne 34 26.660 -85 43.185 12,938 1100 Elmore County Wetumpka 32 32.623 -86 12.715 5,726 175 Escambia County Brewton 31 06.314 -87 04.336 5,498 125 Etowah County Gadsden 34 00.834 -86 00.411 38,978 575 Fayette County Fayette 33 41.074 -87 49.849 4,922 400 Franklin County Russellville 34 30.469 -87 43.711 8,971 800 Geneva County Geneva 31 01.978 -85 51.827 4,388 100 Greene County Eutaw 32 50.435 -87 53.249 1,878 250 Hale County Greensboro 32 42.276 -87 35.757 2,731 250 Henry County Abbeville 31 34.307 -85 15.029 2,987 400 Houston County Dothan 31 13.389 -85 23.423 57,737 325 Jackson County Scottsboro 34 40.343 -86 02.051 14,762 600 Jefferson County Birmingham 33 31.100 -86 48.443 242,820 575 Lamar County Vernon 33 45.424 -88 06.535 2,143 350 Lauderdale County Florence 34 47.989 -87 40.637 36,264 550 Lawrence County Moulton 34 28.873 -87 17.593 3,260 625 Lee County Opelika 32 38.730 -85 22.691 23,498 800 Limestone County Athens 34 48.179 -86 58.301 18,967 700

Lowndes County Haynesville 32 11.045 -86 34.819 1,177 200 Macon County Tuskegee 32 25.445 -85 41.500 11,846 350 Madison County Huntsville 34 44.033 -86 35.317 158,216 650 Marengo County Linden 32 18.369 -87 47.886 2,424 150 Marion County Hamilton 34 08.540 -87 59.313 6,786 475 Marshall County Guntersville 34 21.489 -86 17.682 7,395 725 Mobile County Mobile 30 41.674 -88 02.624 198,915 25 Monroe County Monroeville 31 31.670 -87 19.482 6,862 400 Montgomery County Montgomery 32 22.793 -86 17.944 201,568 225 Morgan County Decatur 34 36.388 -86 59.003 53,929 600 Perry County Marion 32 37.940 -87 19.146 3,511 350 Pickens County Carrollton 33 15.703 -88 05.699 987 175 Pike County Troy 31 48.530 -85 58.198 13,935 500 Randolph County Wedowee 33 18.545 -85 29.078 818 850 Russell County Phenix City 32 28.258 -85 00.045 28,265 275 Shelby County Columbiana 33 10.694 -86 36.431 3,316 550 St. Clair County Ashville 33 50.215 -86 15.206 2,260 575 Sumter County Livingston 32 35.054 -88 11.236 3,297 175 Talladega County Talladega 33 26.162 -86 06.343 15,143 575 Tallapoosa County Dadeville 32 49.878 -85 45.821 3,212 600 Tuscaloosa County Tuscaloosa 33 12.187 -87 33.926 77,906 250 Walker County Jasper 33 49.878 -87 16.655 14,052 425 Washington County Chatom 31 27.905 -88 15.265 1,193 175 Wilcox County Camden 31 59.458 -87 17.432 2,257 250 Winston County Double Springs 34 08.790 -87 24.141 1,003 725

Table 3: ACF Numbers for All Tornadoes for County Seats.

County County Seat All ACF Autauga County Prattville 1.6855E-06 Baldwin County Bay Minette 1.954E-06 Barbour County Clayton 1.8862E-06 Bibb County Centreville 3.8263E-06 Blount County Oneonta 2.5546E-06 Bullock County Union Springs 4.492E-08 Butler County Greenville 1.9452E-06 Calhoun County Anniston 9.7862E-07 Chambers County Lafayette 5.8104E-07 Cherokee County Centre 4.4578E-07 Chilton County Clanton 1.8148E-06 Choctaw County Butler 5.6042E-07 Clarke County Grove Hill 1.1542E-06 Clay County Ashland 2.6869E-06 Cleburne County Heflin 9.7485E-07 Coffee County Elba 9.7098E-07 Colbert County Tuscumbia 2.5158E-06 Conecuh County Evergreen 1.7053E-06 Coosa County Rockford 1.0138E-06 Covington County Andalusia 4.574E-07 Crenshaw County Luverne 2.3011E-07 Cullman County Cullman 6.3759E-06 Dale County Ozark 1.4417E-06 Dallas County Selma 2.7568E-06 DeKalb County Fort Payne 3.0065E-06 Elmore County Wetumpka 1.1766E-06 Escambia County Brewton 9.0102E-07 Etowah County Gadsden 9.3046E-07 Fayette County Fayette 2.245E-07 Franklin County Russellville 7.4723E-07 Geneva County Geneva 6.3897E-07 Greene County Eutaw 5.8151E-07 Hale County Greensboro 5.958E-06 Henry County Abbeville 3.101E-07 Houston County Dothan 7.2458E-07 Jackson County Scottsboro 2.6844E-06 Jefferson County Birmingham 8.1029E-06 Lamar County Vernon 8.7766E-07 Lauderdale County Florence 2.7322E-06

Lawrence County Moulton 3.0283E-06 Lee County Opelika 2.3179E-06 Limestone County Athens 1.0083E-05 Lowndes County Haynesville 1.5154E-06 Macon County Tuskegee 2.4022E-07 Madison County Huntsville 1.4643E-05 Marengo County Linden 4.4215E-07 Marion County Hamilton 5.4119E-06 Marshall County Guntersville 8.1545E-06 Mobile County Mobile 1.0531E-06 Monroe County Monroeville 2.7125E-06 Montgomery County Montgomery 1.9373E-06 Morgan County Decatur 4.2882E-06 Perry County Marion 4.0583E-07 Pickens County Carrollton 4.1653E-06 Pike County Troy 1.8035E-06 Randolph County Wedowee 5.217E-07 Russell County Phenix City 1.6885E-06 Shelby County Columbiana 3.5979E-06 St. Clair County Ashville 4.4082E-06 Sumter County Livingston 1.0084E-06 Talladega County Talladega 2.0452E-06 Tallapoosa County Dadeville 1.2061E-06 Tuscaloosa County Tuscaloosa 3.805E-06 Walker County Jasper 2.9055E-06 Washington County Chatom 6.3558E-07 Wilcox County Camden 3.8302E-08 Winston County Double Springs 2.0221E-06

Table 4: Top 15 ACF Numbers for County Seats.

County County Seat Latitude Longitude Population Elevation All ACF Madison County Huntsville 34 44.033 -86 35.317 158,216 650 1.46E-05 Limestone County Athens 34 48.179 -86 58.301 18,967 700 1.01E-05 Marshall County Guntersville 34 21.489 -86 17.682 7,395 725 8.15E-06 Jefferson County Birmingham 33 31.100 -86 48.443 242,820 575 8.10E-06 Cullman County Cullman 34 10.488 -86 50.611 13,995 800 6.38E-06 Hale County Greensboro 32 42.276 -87 35.757 2,731 250 5.96E-06 Marion County Hamilton 34 08.540 -87 59.313 6,786 475 5.41E-06 St. Clair County Ashville 33 50.215 -86 15.206 2,260 575 4.41E-06 Morgan County Decatur 34 36.388 -86 59.003 53,929 600 4.29E-06 Pickens County Carrollton 33 15.703 -88 05.699 987 175 4.17E-06 Bibb County Centreville 32 56.675 -87 08.322 2,466 300 3.83E-06 Tuscaloosa County Tuscaloosa 33 12.187 -87 33.926 77,906 250 3.81E-06 Shelby County Columbiana 33 10.694 -86 36.431 3,316 550 3.60E-06 Lawrence County Moulton 34 28.873 -87 17.593 3,260 625 3.03E-06 DeKalb County Fort Payne 34 26.660 -85 43.185 12,938 1100 3.01E-06

Table 5: Bottom 15 ACF Numbers for County Seats.

County County Seat Latitude Longitude Population Elevation All ACF Wilcox County Camden 31 59.458 -87 17.432 2,257 250 3.8302E-08 Bullock County Union Springs 32 08.658 -85 42.891 3,670 475 4.492E-08 Fayette County Fayette 33 41.074 -87 49.849 4,922 400 2.245E-07 Crenshaw County Luverne 31 42.993 -86 15.836 2,635 350 2.3011E-07 Macon County Tuskegee 32 25.445 -85 41.500 11,846 350 2.4022E-07 Henry County Abbeville 31 34.307 -85 15.029 2,987 400 3.101E-07 Perry County Marion 32 37.940 -87 19.146 3,511 350 4.0583E-07 Marengo County Linden 32 18.369 -87 47.886 2,424 150 4.4215E-07 Cherokee County Centre 34 09.126 -85 40.738 3,216 600 4.4578E-07 Covington County Andalusia 31 18.512 -86 29.005 8,794 350 4.574E-07 Randolph County Wedowee 33 18.545 -85 29.078 818 850 5.217E-07 Choctaw County Butler 32 05.377 -88 13.315 1,952 150 5.6042E-07 Chambers County Lafayette 32 53.995 -85 24.068 3,234 825 5.8104E-07 Greene County Eutaw 32 50.435 -87 53.249 1,878 250 5.8151E-07 Washington County Chatom 31 27.905 -88 15.265 1,193 175 6.3558E-07

Table 6: County Seats Top 15 in Population illustrated with All ACF Numbers.

# County County Seat Latitude Longitude Population ALL ACF 1 Jefferson County Birmingham 33 31.100 -86 48.443 242,820 8.1029E-06 2 Montgomery County Montgomery 32 22.793 -86 17.944 201,568 1.9373E-06 3 Mobile County Mobile 30 41.674 -88 02.624 198,915 1.0531E-06 4 Madison County Huntsville 34 44.033 -86 35.317 158,216 1.4643E-05 5 Tuscaloosa County Tuscaloosa 33 12.187 -87 33.926 77,906 3.805E-06 6 Houston County Dothan 31 13.389 -85 23.423 57,737 7.2458E-07 7 Morgan County Decatur 34 36.388 -86 59.003 53,929 4.2882E-06 8 Etowah County Gadsden 34 00.834 -86 00.411 38,978 9.3046E-07 9 Lauderdale County Florence 34 47.989 -87 40.637 36,264 2.7322E-06 10 Russell County Phenix City 32 28.258 -85 00.045 28,265 1.6885E-06 11 Autauga County Prattville 32 27.847 -86 27.584 24,303 1.6855E-06 12 Calhoun County Anniston 33 39.610 -85 49.893 24,276 9.7862E-07 13 Lee County Opelika 32 38.730 -85 22.691 23,498 2.3179E-06 14 Dallas County Selma 32 24.449 -87 01.261 20,512 2.7568E-06 15 Limestone County Athens 34 48.179 -86 58.301 18,967 1.0083E-05

Table 7: County Seats Bottom15 in Population illustrated with All ACF Numbers.

# County County Seat Latitude Longitude Population ALL ACF 67 Coosa County Rockford 32 53.379 -86 13.184 428 1.0138E-06 66 Randolph County Wedowee 33 18.545 -85 29.078 818 5.217E-07 65 Pickens County Carrollton 33 15.703 -88 05.699 987 4.1653E-06 64 Winston County Double Springs 34 08.790 -87 24.141 1,003 2.0221E-06 63 Lowndes County Haynesville 32 11.045 -86 34.819 1,177 1.5154E-06 62 Washington County Chatom 31 27.905 -88 15.265 1,193 6.3558E-07 61 Clarke County Grove Hill 31 42.525 -87 46.627 1,438 1.1542E-06 60 Barbour County Clayton 31 52.691 -85 26.983 1,475 1.8862E-06 59 Greene County Eutaw 32 50.435 -87 53.249 1,878 5.8151E-07 58 Choctaw County Butler 32 05.377 -88 13.315 1,952 5.6042E-07 57 Clay County Ashland 33 16.421 -85 50.157 1,965 2.6869E-06 56 Lamar County Vernon 33 45.424 -88 06.535 2,143 8.7766E-07 55 Wilcox County Camden 31 59.458 -87 17.432 2,257 3.8302E-08 54 St. Clair County Ashville 33 50.215 -86 15.206 2,260 4.4082E-06 53 Marengo County Linden 32 18.369 -87 47.886 2,424 4.4215E-07

Table 8: County Seats Top 15 in Elevation with All ACF Numbers.

# County County Seat Latitude Longitude Population Elevation ACF ALL 1 DeKalb County Fort Payne 34 26.660 -85 43.185 12,938 1100 3.0065E-06 2 Clay County Ashland 33 16.421 -85 50.157 1,965 1075 2.6869E-06 3 Cleburne County Heflin 33 38.937 -85 35.245 3,002 975 9.7485E-07 4 Blount County Oneonta 33 56.895 -86 28.361 5,576 875 2.5546E-06 5 Randolph County Wedowee 33 18.545 -85 29.078 818 850 5.217E-07 6 Chambers County Lafayette 32 53.995 -85 24.068 3,234 825 5.8104E-07 7 Calhoun County Anniston 33 39.610 -85 49.893 24,276 800 9.7862E-07 8 Cullman County Cullman 34 10.488 -86 50.611 13,995 800 6.3759E-06 9 Franklin County Russellville 34 30.469 -87 43.711 8,971 800 7.4723E-07 10 Lee County Opelika 32 38.730 -85 22.691 23,498 800 2.3179E-06 11 Coosa County Rockford 32 53.379 -86 13.184 428 725 1.0138E-06 12 Marshall County Guntersville 34 21.489 -86 17.682 7,395 725 8.1545E-06 13 Winston County Double Springs 34 08.790 -87 24.141 1,003 725 2.0221E-06 14 Limestone County Athens 34 48.179 -86 58.301 18,967 700 1.0083E-05 15 Madison County Huntsville 34 44.033 -86 35.317 158,216 650 1.4643E-05

Table 9: County Seats Bottom 15 in Elevation with All ACF Numbers.

# County County Seat Latitude Longitude Population Elevation ACF ALL 67 Mobile County Mobile 30 41.674 -88 02.624 198,915 25 1.0531E-06 66 Geneva County Geneva 31 01.978 -85 51.827 4,388 100 6.3897E-07 65 Dallas County Selma 32 24.449 -87 01.261 20,512 125 2.7568E-06 64 Escambia County Brewton 31 06.314 -87 04.336 5,498 125 9.0102E-07 63 Choctaw County Butler 32 05.377 -88 13.315 1,952 150 5.6042E-07 62 Marengo County Linden 32 18.369 -87 47.886 2,424 150 4.4215E-07 61 Elmore County Wetumpka 32 32.623 -86 12.715 5,726 175 1.1766E-06 60 Pickens County Carrollton 33 15.703 -88 05.699 987 175 4.1653E-06 59 Sumter County Livingston 32 35.054 -88 11.236 3,297 175 1.0084E-06 58 Washington County Chatom 31 27.905 -88 15.265 1,193 175 6.3558E-07 57 Lowndes County Haynesville 32 11.045 -86 34.819 1,177 200 1.5154E-06 56 Montgomery County Montgomery 32 22.793 -86 17.944 201,568 225 1.9373E-06 55 Greene County Eutaw 32 50.435 -87 53.249 1,878 250 5.8151E-07 54 Hale County Greensboro 32 42.276 -87 35.757 2,731 250 5.958E-06 53 Tuscaloosa County Tuscaloosa 33 12.187 -87 33.926 77,906 250 3.805E-06 52 Wilcox County Camden 31 59.458 -87 17.432 2,257 250 3.8302E-08

Table 10: ACF Numbers for Significant Tornadoes for County Seats.

County County Seat SIG ACF Autauga County Prattville 1.21E-06 Baldwin County Bay Minette 1.95E-06 Barbour County Clayton 1.14E-06 Bibb County Centreville 3.57E-06 Blount County Oneonta 1.93E-06 Bullock County Union Springs N/A Butler County Greenville 1.93E-06 Calhoun County Anniston 8.07E-07 Chambers County Lafayette 2.98E-07 Cherokee County Centre 4.3E-07 Chilton County Clanton 1.74E-06 Choctaw County Butler 6.04E-07 Clarke County Grove Hill 9.17E-07 Clay County Ashland 2.13E-06 Cleburne County Heflin 9.55E-07 Coffee County Elba 1.72E-07 Colbert County Tuscumbia 2.03E-06 Conecuh County Evergreen 1.67E-06 Coosa County Rockford 8.99E-07 Covington County Andalusia 3.49E-07 Crenshaw County Luverne 7.24E-08 Cullman County Cullman 5.33E-06 Dale County Ozark 7.86E-07 Dallas County Selma 2.24E-06 DeKalb County Fort Payne 3.01E-06 Elmore County Wetumpka 8.01E-07 Escambia County Brewton 9E-07 Etowah County Gadsden 6.99E-07 Fayette County Fayette 1.02E-07 Franklin County Russellville 7.46E-07 Geneva County Geneva 6.39E-07 Greene County Eutaw 4.45E-08 Hale County Greensboro 5.79E-06 Henry County Abbeville 2.94E-07 Houston County Dothan 1.98E-07 Jackson County Scottsboro 2.68E-06 Jefferson County Birmingham 7.92E-06 Lamar County Vernon 7.08E-07

Lauderdale County Florence 1.73E-06 Lawrence County Moulton 3.03E-06 Lee County Opelika 2.19E-06 Limestone County Athens 9.73E-06 Lowndes County Haynesville 1.46E-06 Macon County Tuskegee 6.52E-08 Madison County Huntsville 1.39E-05 Marengo County Linden 4.4E-07 Marion County Hamilton 5.33E-06 Marshall County Guntersville 7.86E-06 Mobile County Mobile 9.12E-07 Monroe County Monroeville 2.47E-06 Montgomery County Montgomery 1.92E-06 Morgan County Decatur 4.28E-06 Perry County Marion 4.05E-07 Pickens County Carrollton 3.73E-06 Pike County Troy 1.45E-06 Randolph County Wedowee 4.63E-07 Russell County Phenix City 1.67E-06 Shelby County Columbiana 3.48E-06 St. Clair County Ashville 4.41E-06 Sumter County Livingston 1.01E-06 Talladega County Talladega 2.05E-06 Tallapoosa County Dadeville 2.36E-07 Tuscaloosa County Tuscaloosa 3.74E-06 Walker County Jasper 2.73E-06 Washington County Chatom 6.32E-07 Wilcox County Camden 3.83E-08 Winston County Double Springs 2E-06

Table 11: Top 15 Significant ACF Numbers for County Seats.

County County Seat Latitude Longitude Population Elevation SIG ACF Madison County Huntsville 34 44.033 -86 35.317 158,216 650 1.39E-05 Limestone County Athens 34 48.179 -86 58.301 18,967 700 9.73E-06 Jefferson County Birmingham 33 31.100 -86 48.443 242,820 575 7.92E-06 Marshall County Guntersville 34 21.489 -86 17.682 7,395 725 7.86E-06 Hale County Greensboro 32 42.276 -87 35.757 2,731 250 5.79E-06 Cullman County Cullman 34 10.488 -86 50.611 13,995 800 5.33E-06 Marion County Hamilton 34 08.540 -87 59.313 6,786 475 5.33E-06 St. Clair County Ashville 33 50.215 -86 15.206 2,260 575 4.41E-06 Morgan County Decatur 34 36.388 -86 59.003 53,929 600 4.28E-06 Tuscaloosa County Tuscaloosa 33 12.187 -87 33.926 77,906 250 3.74E-06 Pickens County Carrollton 33 15.703 -88 05.699 987 175 3.73E-06 Bibb County Centreville 32 56.675 -87 08.322 2,466 300 3.57E-06 Shelby County Columbiana 33 10.694 -86 36.431 3,316 550 3.48E-06 Lawrence County Moulton 34 28.873 -87 17.593 3,260 625 3.03E-06 DeKalb County Fort Payne 34 26.660 -85 .43185 12,938 1100 3.01E-06

Table 12: Bottom 15 Significant ACF Numbers for County Seats.

County County Seat Latitude Longitude Population Elevation SIG ACF Bullock County Union Springs 32 08.658 -85 42.891 3,670 475 N/A Wilcox County Camden 31 59.458 -87 17.432 2,257 250 3.8263E-08 Greene County Eutaw 32 50.435 -87 53.249 1,878 250 4.4529E-08 Macon County Tuskegee 32 25.445 -85 41.500 11,846 350 6.5153E-08 Crenshaw County Luverne 31 42.993 -86 15.836 2,635 350 7.2432E-08 Fayette County Fayette 33 41.074 -87 49.849 4,922 400 1.0201E-07 Coffee County Elba 31 24.874 -86 04.058 4,185 275 1.7215E-07 Houston County Dothan 31 13.389 -85 23.423 57,737 325 1.9758E-07 Tallapoosa County Dadeville 32 49.878 -85 45.821 3,212 600 2.3643E-07 Henry County Abbeville 31 34.307 -85 15.029 2,987 400 2.9439E-07 Chambers County Lafayette 32 53.995 -85 24.068 3,234 825 2.9764E-07 Covington County Andalusia 31 18.512 -86 29.005 8,794 350 3.4919E-07 Perry County Marion 32 37.940 -87 19.146 3,511 350 4.052E-07 Cherokee County Centre 34 09.126 -85 40.738 3,216 600 4.2954E-07 Marengo County Linden 32 18.369 -87 47.886 2,424 150 4.4021E-07

Table 13: County Seats Top 15 in Population illustrated with Significant ACF Numbers.

# County County Seat Latitude Longitude Population SIG ACF 1 Jefferson County Birmingham 33 31.100 -86 48.443 242,820 7.9242E-06 2 Montgomery County Montgomery 32 22.793 -86 17.944 201,568 1.9176E-06 3 Mobile County Mobile 30 41.674 -88 02.624 198,915 9.117E-07 4 Madison County Huntsville 34 44.033 -86 35.317 158,216 1.3913E-05 5 Tuscaloosa County Tuscaloosa 33 12.187 -87 33.926 77,906 3.744E-06 6 Houston County Dothan 31 13.389 -85 23.423 57,737 1.9758E-07 7 Morgan County Decatur 34 36.388 -86 59.003 53,929 4.2755E-06 8 Etowah County Gadsden 34 00.834 -86 00.411 38,978 6.9858E-07 9 Lauderdale County Florence 34 47.989 -87 40.637 36,264 1.7303E-06 10 Russell County Phenix City 32 28.258 -85 00.045 28,265 1.6651E-06 11 Autauga County Prattville 32 27.847 -86 27.584 24,303 1.2136E-06 12 Calhoun County Anniston 33 39.610 -85 49.893 24,276 8.0701E-07 13 Lee County Opelika 32 38.730 -85 22.691 23,498 2.1852E-06 14 Dallas County Selma 32 24.449 -87 01.261 20,512 2.2417E-06 15 Limestone County Athens 34 48.179 -86 58.301 18,967 9.7311E-06

Table 14: County Seats Bottom 15 in Population illustrated with Significant ACF Numbers.

# County County Seat Latitude Longitude Population SIG ACF 67 Coosa County Rockford 32 53.379 -86 13.184 428 8.9926E-07 66 Randolph County Wedowee 33 18.545 -85 29.078 818 4.6341E-07 65 Pickens County Carrollton 33 15.703 -88 05.699 987 3.7263E-06 64 Winston County Double Springs 34 08.790 -87 24.141 1,003 1.9979E-06 63 Lowndes County Haynesville 32 11.045 -86 34.819 1,177 1.4558E-06 62 Washington County Chatom 31 27.905 -88 15.265 1,193 6.3165E-07 61 Clarke County Grove Hill 31 42.525 -87 46.627 1,438 9.1679E-07 60 Barbour County Clayton 31 52.691 -85 26.983 1,475 1.1372E-06 59 Greene County Eutaw 32 50.435 -87 53.249 1,878 4.4529E-08 58 Choctaw County Butler 32 05.377 -88 13.315 1,952 6.042E-07 57 Clay County Ashland 33 16.421 -85 50.157 1,965 2.1302E-06 56 Lamar County Vernon 33 45.424 -88 06.535 2,143 7.0786E-07 55 Wilcox County Camden 31 59.458 -87 17.432 2,257 3.8263E-08 54 St. Clair County Ashville 33 50.215 -86 15.206 2,260 4.4075E-06 53 Marengo County Linden 32 18.369 -87 47.886 2,424 4.4021E-07

Table 15: County Seats Top 15 in Elevation with Significant ACF Numbers.

# County County Seat Latitude Longitude Population Elevation ACF SIG 1 DeKalb County Fort Payne 34 26.660 -85 43.185 12,938 1100 3.0064E-06 2 Clay County Ashland 33 16.421 -85 50.157 1,965 1075 2.1302E-06 3 Cleburne County Heflin 33 38.937 -85 35.245 3,002 975 9.5459E-07 4 Blount County Oneonta 33 56.895 -86 28.361 5,576 875 1.9334E-06 5 Randolph County Wedowee 33 18.545 -85 29.078 818 850 4.6341E-07 6 Chambers County Lafayette 32 53.995 -85 24.068 3,234 825 2.9764E-07 7 Calhoun County Anniston 33 39.610 -85 49.893 24,276 800 8.0701E-07 8 Cullman County Cullman 34 10.488 -86 50.611 13,995 800 5.3338E-06 9 Franklin County Russellville 34 30.469 -87 43.711 8,971 800 7.4642E-07 10 Lee County Opelika 32 38.730 -85 22.691 23,498 800 2.1852E-06 11 Coosa County Rockford 32 53.379 -86 13.184 428 725 8.9926E-07 12 Marshall County Guntersville 34 21.489 -86 17.682 7,395 725 7.8556E-06 13 Winston County Double Springs 34 08.790 -87 24.141 1,003 725 1.9979E-06 14 Limestone County Athens 34 48.179 -86 58.301 18,967 700 9.7311E-06 15 Madison County Huntsville 34 44.033 -86 35.317 158,216 650 1.3913E-05

Table 16: County Seats Bottom 15 in Elevation with Significant ACF Numbers.

# County County Seat Latitude Longitude Population Elevation ACF SIG 67 Mobile County Mobile 30 41.674 -88 02.624 198,915 25 9.117E-07 66 Geneva County Geneva 31 01.978 -85 51.827 4,388 100 6.3864E-07 65 Dallas County Selma 32 24.449 -87 01.261 20,512 125 2.2417E-06 64 Escambia County Brewton 31 06.314 -87 04.336 5,498 125 8.9993E-07 63 Choctaw County Butler 32 05.377 -88 13.315 1,952 150 6.042E-07 62 Marengo County Linden 32 18.369 -87 47.886 2,424 150 4.4021E-07 61 Elmore County Wetumpka 32 32.623 -86 12.715 5,726 175 8.0094E-07 60 Pickens County Carrollton 33 15.703 -88 05.699 987 175 3.7263E-06 59 Sumter County Livingston 32 35.054 -88 11.236 3,297 175 1.0067E-06 58 Washington County Chatom 31 27.905 -88 15.265 1,193 175 6.3165E-07 57 Lowndes County Haynesville 32 11.045 -86 34.819 1,177 200 1.4558E-06 56 Montgomery County Montgomery 32 22.793 -86 17.944 201,568 225 1.9176E-06 55 Greene County Eutaw 32 50.435 -87 53.249 1,878 250 4.4529E-08 54 Hale County Greensboro 32 42.276 -87 35.757 2,731 250 5.7927E-06 53 Tuscaloosa County Tuscaloosa 33 12.187 -87 33.926 77,906 250 3.744E-06 52 Wilcox County Camden 31 59.458 -87 17.432 2,257 250 3.8263E-08

Figure 25: Cluster Map of All Tornadoes.

Figure 26: Cluster Map for Significant Tornadoes.

Figure 27: County Population Map

Figure 28: Rural Population Map

Figure 29: Metropolitan Statistical Areas Map

Directional Threat Contours:

The typical direction in which most tornadoes orient from in this study is a southwesterly direction.

Figure 30: Directional Threat Sectors for All Tornadoes between 1950-1999.

Figure 31: Directional Threat Sectors for Significant Tornadoes between 1950-1999.

Inferences and Interpretations:

The distribution of all tornadoes in Alabama is directly related to the distribution of significant tornadoes. Not much deviation if any can be seen between the two distributions in time of day, months, and years (Figures 14-16 and 18-20).

The afternoon time period appears conducive for tornadoes from 1950-1999. The most frequent time period in both the significant and all tornado study is 1700 CST.

A secondary peak occurs at 1300 CST. A slightly higher distribution exists during the morning hours just before noon compared to the overnight hours. Knupp and

Garinger (1993) said that this morning peak of tornadoes was due to sea breeze lines.

This is clearly supported in my research, as there is no noteworthy difference in the distribution of significant tornadoes during the morning hours and overnight hours unlike the all tornado category as most sea breeze associated tornadoes are either F0 or F1 in intensity. Despite the time of day tornadoes occur, it is clear that they can occur during anytime of the year (Figure 14 and 18).

Monthly distributions of all and significant tornadoes look similar with a

March, April, and May peak which correlates to the spring severe weather season as seen in Figure 15. April is the most active tornado month with greater than four tornadoes averaged during the month of April for 50 years. However, tornadoes can occur during any month or season as seen in these distributions. An interesting finding is that it appears that Alabama has a secondary tornado season during the fall months of November and December as seen in Figure 19. Ostby (1993) pointed out that over the past ten years that tornado reporting during the month of November has

65 significantly increased. Whether it has significantly increased over the past ten years is extraneous, because there is without a doubt a secondary tornado season in

Alabama. With this finding, awareness can now be addressed so the public will no longer think that the spring season is the only severe weather season in Alabama.

The average number of tornadoes occurring in a year is approximately twenty- three. There have been a total of four years which have averaged over fifty tornadoes. Those years, 1957, 1973, 1974, and 1998, all coincide with major individual tornado outbreaks occurring across Alabama. There seems to be a general increase of tornado reporting through my study period (1950-1999) as seen in Figure

16. This may be due to the over reporting problem of weak tornadoes in metropolitan areas according to Schaefer and Galway (1982).

As seen in Figure 20, the annual distribution of significant tornadoes is generally on a decrease. This is possibly due to the fact that many reliable weather observers from rural locations decided to move to a more urban location. Also, through the years the National Weather Service has made a point to survey every storm. While different meteorologists may survey the damage, the opinions are usually similar due to extensive training and standard manuals that each meteorologist must follow. During the first thirty years of my study (1950-1980), reductions in Weather Forecast Offices made it difficult to survey damage for every possible tornado. Many reports of tornadoes during this thirty-year time span were frequently misclassified, as the storm was never surveyed. This may explain the general decrease in significant tornado storm reports.

According to Grazulis (1993), Alabama ranks first in the percentage of significant tornadoes between the years of 1953-1991. This is seen in Figure 20 as

15 years of the time span from 1953-1991 averaged over ten significant tornadoes per year. After 1991, there are no years that average over ten significant tornadoes. This finding supports the theory that the general reporting of significant tornadoes is on a decrease. This may be due to the rural population moving into cities or better training of storm survey meteorologists.

Comparing the two annual distributions for all tornadoes (Figure 16) and significant tornadoes (Figure 20), it is evident that the weak tornadoes (F0 and F1) affect the outcome of these two distribution graphs. As the all tornado graph slowly increases with years, the significant tornado graph decreases over time. This shows that weak tornadoes are being reported more often than the significant ones. This may be due to the over reporting problem with weak tornadoes. The difference is agreed by findings by Ostby (1993) as his study states that the variability of tornado climatology is due to an increase in weak tornado reports.

Tornado intensity distributions were dominated by F1 tornadoes where there are almost eight a year. Tornadoes rated F2 averaged about six a year across

Alabama. The surprising fact is that Alabama only averages four F0 tornadoes a year.

With this study conducted from 1950-1999, a conclusion can be made that this data is slightly skewed. Classification of F0 tornadoes did not become popular until the mid

1970s, so that may be a reason why the average of F1 tornadoes is so high and F0 tornadoes are unexpectedly lower as seen in Figure 17.

In this study, a ten mile radius around all 67 county seats were analyzed and compared for tornado distribution linked to location, population, and elevation. As illustrated in Table 4, the Top 15 Annual Coverage Fractions (ACFs) are spread all across the northern half of Alabama. This supports the idea that a tornado alley exists in North and Central Alabama. Huntsville located in Madison County, which is just south of the Tennessee State line, exemplifies the highest ACF number of 1.4643E-

05. Second on the list is Athens located in Limestone County which is just west of

Madison County. Athens recorded an ACF of 1.0083E-05. In Table 5, Camden located in Wilcox County had the lowest ACF out of all 67 county seats investigated, which was a figure of 3.8302E-08 meaning that statistically it has the smallest chance of a tornado occurring.

As far as population, the Top 15 ACF cities averaged 39,669 residents. This average includes Birmingham, Huntsville, Decatur, and Tuscaloosa. As a side note it also includes Carrollton located in Pickens County which has a population of 987.

The Bottom 15 ACF cities averaged 3,689 in population. The largest city in this category was Tuskegee in Macon County with 11,846. The smallest city was

Wedowee in Randolph County with 818 residents. It is clearly seen that population is a factor in this part of the study. The Top 15 ACF cities have over 1000% more residents than the cities in the Bottom 15 ACF. This finding illustrates that a population bias does exist when the whole study is considered.

Similar results were found when the study was narrowed down to just significant tornadoes. Once again, Huntsville and Athens were the top 2 cities with

68 the highest ACF numbers of 1.3913E-05 and 9.7311E-06 respectively. The only difference this time was Birmingham, which is located in Jefferson County, moved up to third with an ACF of 7.9242E-06 as seen in Table 11. In fact, none of the previous

Top 15 ACF for all tornadoes changed for the Top 15 ACF for significant tornadoes.

Union Springs in Bullock County has not recorded a single significant tornado from

1950 to 1999 in a ten mile radius around the city. Hence, this was number one on the

Bottom 15 Significant ACF list as seen in Table 12. It is also noted that Dothan is now included in the Bottom 15 Significant ACF which has a population of 57,737.

This has raised the average population for the Bottom 15 Significant ACF to 7,767.

However, this figure is still well below the 39,669 residents that the Top 15

Significant ACF’s register. This only gives the discretion of a 500% difference. This makes sense, because you usually do not need a large population center to report a significant tornado.

Comparing the Top 15 most populated county seats across the state to the Top

15 ACF numbers, I found that only 5 county seats of the Top 15 ACF’s exist in the highest ACF numbers for all and significant tornadoes. Those cities were

Birmingham, Huntsville, Tuscaloosa, Decatur, and Athens. However, one county seat, Carrollton who was in the Top 15 ACF for all and significant tornadoes, appears to have one of the lowest populations in the state. As Grazulis and Abbey noted in their study in 1983, some population centers are included in tornado maxima as seen in Figure 4. However, some large cities like Memphis and Atlanta are not included in the maxima. This is supported by the ACF figures mentioned above. Some

69 population centers such as Birmingham, Huntsville, and Tuscaloosa are included in the tornado maxima. But cities such as Montgomery, Mobile, Gadsden, Anniston, and Florence are some of the more populated cities which are not included in the Top

15 ACF numbers for all and significant tornadoes. Although population may play a role in tornado reporting, it is concluded that it is not the sole factor when examining tornado climatologies especially for significant tornadoes.

The effect that terrain may have on tornadoes is a little harder to analyze compared to population effects. The county seats with the highest elevation were analyzed to see if there was an increase or decrease in the number of Top 15 ACF cities for all and significant tornadoes. The findings were inconclusive as there were four of the Top 15 ACF’s in the highest elevation category and three of the Top 15

ACF’s in the bottom elevation category. When looking at the Top 15 ACF county seats, the average elevation was 485 feet. This is compared to 395 feet which was found for the Bottom 15 ACF county seats. Although this is only a small difference of 90 feet, this may be a start for more intense research to be conducted whether or not terrain may have some influence. Wasula et. al.(2002) said that there are fewer severe weather reports in the highest elevations when compared to river valleys. This statement may be true, but my research was based on county seats which are usually the heaviest populated city in the county. Four of the Top 15 highest elevated cities are also in the Top 15 ACF numbers for all and significant tornadoes. However, terrain influences on tornadoes may be more associated with the surrounding terrain of a city rather than the city’s elevation.

Grazulis and Abbey (1983) researched the effects that terrain roughness might have on low level inflow which may increase vertical velocities and initiate tornadoes. However, this inflow may be decreased if extreme roughness or position of the terrain prevents inflow from having a positive influence on the storm. Several key factors play a role in tornadic development associated with elevation such as the synoptic pattern, wind shear, and the position of the increased terrain. Similar results were found in a study by Schmid and Lehre (1998) where vertical wind could be strengthened or weakened by different terrain elevations. Hence, all factors need to be closely evaluated for the specific area if severe weather threatens.

After plotting each tornado across Alabama, it was clear that there were clusters in the All and Significant Tornado maps. As illustrated in Figure 25, there were seven clusters found. Cluster #1 is located in extreme Northern Alabama. This cluster coexists with Madison and Limestone Counties which were number one and two in the Top 15 ACFs for all and significant tornadoes. This cluster represents the

Huntsville and Decatur Metropolitan area as seen in Figure 29. However, Cluster #2 is not co-located in a metropolitan area. In fact, this cluster is located in Marion

County which was #7 on both the All and Significant Top 15 ACFs tornado lists.

This area is not heavily populated, and no major interstate systems are located in this cluster. Cluster #3 is a combination of several counties located in the Birmingham

Metropolitan areas. This cluster is the largest on the map and expands to include counties such as Cullman and De Kalb Counties which typically are not considered the metro area of Birmingham. Regardless, this cluster is the most heavily populated

71 according to Figure 27 when compared to other clusters. Cluster #4 which is the smallest cluster is located in Tuscaloosa County. Tuscaloosa County appears in the

Top 15 list for All and Significant Tornadoes ACFs. This is clearly around the population center of Tuscaloosa when comparing the location of this cluster to Figure

27. Cluster #5 is located in South-Central Alabama close to the Montgomery metropolitan area. Uniquely, this cluster is L-shaped and includes cities such as

Montgomery, Prattville, and Troy. However, none of these counties are located in the

Top15 ACF lists for All and Significant Tornadoes. Cluster #6 is also unique in shape as this cluster is situated west to east in an elongated oval. This cluster is very close to the Alabama-Florida state line in extreme Southern Alabama and includes

Dothan’s metropolitan area. Cluster #7 is easily seen as Mobile’s metro area. The upside down U-shape illustrates Mobile Bay.

When looking at the plots of just the Significant Tornadoes (F2-F5), only six clusters were found instead of seven as seen in Figure 26. Cluster #1 for significant tornadoes is the same as Cluster #1 in Figure 25 when all tornadoes were plotted.

Cluster #1 represents the Tennessee Valley in both figures. In Northern Alabama,

Cluster #2 and Cluster #3 are similar to the corresponding clusters found in Figure 25.

Cluster #4 and #5 matches up accordingly to Cluster #5 in Figure 25. However, in

Figure 26, the cities of Montgomery and Troy are more noticeable as they are now individual clusters. Cluster #6 in Figure 26 is closely related to Cluster #7 from

Figure 25. Once again the shape of the cluster of tornadoes is an upside down U- shape illustrating Mobile Bay. One major difference when comparing the two

72 tornado figures is that there is not a cluster located over Tuscaloosa County on the significant tornado figure like Cluster #4 on the all tornado figure (Figure 25).

There are numerous reasons why clusters exist where they do and do not.

Many areas that are not specified in a cluster area are more than likely classified as rural. This is especially true for the southern half of Alabama. As seen in Figure 28, many south and western counties are rural. Thus, a connection can be made when comparing tornado reports seen in Figure 25. This is supported by the fact that people are needed to report a tornado and if a tornado hits open country, it is likely that this storm will never be reported. Ostby (1993) in his study said that weak tornadoes may be under classified. Grazulis (1993) agreed giving reasons such as the lack of population densities, mistaken identity for straight line wind damage, and terrain roughness for the lack of weak tornado reports.

Another interesting finding was seen in Figure 26 on the Significant Tornado

Plot Map. A greater number of significant tornadoes strike Northern Alabama compared to Southern Alabama. Similar findings were found in a study by Knupp and Garinger (1993). Tropical cyclones and sea breezes create most tornadoes along the southern half of the state. This is clear when comparing Figure 25 and 26. A cluster of tornadoes (Cluster #6) is seen around Mobile Bay. In this cluster, no F4 or

F5 tornadoes exist. These tornadoes are more than likely associated with landfalling tropical cyclones or sea breezes. Rarely, cold fronts that typically cause the tornadoes in the northern half of the state create the same threat for significant tornadoes for the southern half. In fact, there were only two F4 tornadoes and no F5 tornadoes in the

73 southern half of Alabama. Livingston and Schaefer (1993) inferred that weaker tornadoes occur in the smoother terrain of South Alabama and the stronger, violent tornadoes occur in North and Central Alabama where the terrain is rougher as seen in

Figure 1. More support for this hypothesis is seen in a study by Knupp and Garinger

(1993). In their research, it was discovered that a secondary peak of tornadoes occurred during the morning hours along the Gulf Coast . They found that this secondary peak was caused by sea breeze influences. Hence, the most violent tornadoes are located in what has found to be a “Dixie Tornado Alley” in North

Alabama.

With all the clusters of tornadoes analyzed, several areas which may be heavily populated do not illustrate tornado occurrences. In North Alabama, terrain and elevation are the key factors why some tornadoes may never be reported. This is especially true in Northeastern Alabama. On Figure 25, a narrow space can be seen between Cluster #1 and #3. This is where the Tennessee River is located. A noticeable gap is seen between Cluster #2 and #3. This is where the Bankhead

National Forest is located in Winston County. Other areas which may not be apart of a cluster are explained by being less populated from either being a Wildlife

Management Area, bodies of water, or national forests. However, South Alabama is a little harder to explain why some areas have more tornado reports than others. One major reason is that most of South Alabama outside of metropolitan areas is extremely rural. Some other reasons may be lakes, rivers, Wildlife Management

Areas, national forests, or military bases.

So a conclusion can be made that there is a distinct difference between North and South Alabama as far as tornado distribution. In Figure 32 the counties highlighted in blue illustrate the Top 15 ACF numbers for all and significant tornadoes. A relationship between the counties and typical storm motion is also seen as most storms travel from a southwest to northeast nature as indicated by the

Directional Threat Sectors in Figure 30 and 31. The counties that are highlighted are in a formation where there are two distinct different tornado alleys. These two distinct separate alleys can be seen in Figure 32. One tornado alley exists in Northern

Alabama and another one in Central Alabama. Each alley is clustered with 7 counties with an exception being Marion County. Marion County may not be part of a cluster due to the county neighboring it, Winston County, as it is sparsely populated due to the Bankhead National Forest.

Through my research, I have found that there is not only one tornado alley through the state, but that there are two distinct and smaller tornado alleys located in

North and Central Alabama as seen in Figure 32.

Figure 32: Counties with the Top 15 Highest ACF Numbers.

Future Investigations:

For purposes of local National Weather Service office’s or Emergency

Management Agencies for specific counties, a smaller region for a tornado climatology study may be more useful. An individual National Weather Service office may be able to conduct a tornado climatology just for their County Warning

Area (CWA). This may provide more detailed information regarding tornado distribution in particular areas. It may even be possible to narrow this study for only individual counties. The specific county information is especially helpful to forecasters and emergency managers. Understanding the effects a counties population and terrain may have on tornadoes and tornado reports may lead to increase warning time and lives saved. A meteorologist may give special attention to a storm knowing that it is in a rural area and severe weather reports are unlikely.

Another future study might include just the northern two thirds of Alabama.

This would leave out the southern portion of the state which is mostly made up of weak tornadoes occurring from sea breezes and tropical cyclones. Typical weather systems such as upper level troughs associated with cold arctic fronts rarely occur during the year the closer the location is to the coast. Those two features are just a piece of what it takes for severe weather to occur. It is clearly seen through my research that the baroclinic zone of the Northern Gulf Coast provides a so-called safe zone from strong, violent tornadoes. Knupp and Garinger (1993) also provide proof in their study that the baroclinic zone displays spatial inconsistency in severe weather patterns. Once again, to better understand the climatology of significant tornadoes, it

77 may be best to leave out the southern third of storms considering most of these tornadoes are weak.

One more suggestion for future studies is to conduct the same type of research that this paper illustrates on other surrounding states. Since there is a distinct tornado alley in Northern Alabama, is there also one located in Northern and Central

Mississippi? The only way for this question to be answered is for more studies to be conducted and compared so better knowledge can be found of where frequent severe weather occurs. As Anthony (1988) explains in his research, studies of tornadoes in the Southeastern United States are limited compared to ample information for the

Great Plains. Thus research in this region is in its primitive stages and must be done as storms in the Southeast do not demonstrate the same characteristics as the Great

Plains.

In this study, the tornadoes that occurred during the years 1950-1999 were used. Several different combinations of years could have been used in this study with many different reasons supporting the different categories of years. Changnon Jr.

(1982) explained that through reductions of staff in the National Weather Service during the years of 1973-1980, that many tornadoes were taken by word as tornadoes.

Most storms were not surveyed to verify if a tornado occurred. During the early

1990’s, a warning verification program was developed in the National Weather

Service. Each storm had to be documented and surveyed if possible. In a study conducted by Edwards (2003), he pointed out that this program was developed for the sole purpose to save lives. One could argue that a study from 1990-2002 would have

78 been more appropriate to conduct since better tornado documentation is present during these years. A study from the years that the WSR-88D Doppler Radar was implemented may have lead to better results. The Doppler Radar in Birmingham and

Mobile were constructed and put in use in 1993. The Huntsville Doppler Radar was constructed in 1995. Perhaps a ten year study from 1993-2002 in which the WSR-

88D was in use might have provided results that would not be as variable. However, the purpose of this study was to construct a tornado climatology for all tornadoes in a time span that could be compared to what occurred in different decades. If fewer years were used in this study, it is possible that comparisons could not have been made. However, additional research may be needed during the time spans mentioned above for purposes of local tornado climatology studies.

In a study conducted by Livingston and Schaefer, North and Central Alabama counties have over fifty percent of their tornadoes F2 or greater as seen in Figure 3.

Another argument is not to include weak tornadoes in a study. As seen in Figure 4,

Weiss and Vescio (1998) discovered that there is an overall slow increase in the reports of tornadoes mainly resulting from the increase in weak (F0-F1) tornadoes, while significant tornadoes have remained constant. However, in my study I wanted to do a complete tornado climatology including all storms. Using just significant tornadoes may be a study in the future to better understand the climatology.

CHAPTER VI

CONCLUSIONS

My research provided a detailed climatology of tornadoes in Alabama during the years of 1950-1999. Tornadoes were plotted and analyzed according to spatial distribution in proving that a “Dixie Alley” exists. A complete temporal and spatial climatology was done for all and significant tornadoes. From this analysis, two distinct tornado regions were found in North and Central Alabama. Explanations were given regarding to why these two tornado alleys existed as far as terrain, population, and land use.

In this study, it was found that the tornadoes from 1950-1999 in Alabama do show a clustered pattern. This clustering pattern is closely related to population as most of the tornado clusters are centered around a metropolitan area. However, it was found that elevation of a city does not effect tornado distribution but rather the possibility that the terrain surrounding the city may have an influence on tornadoes.

The most significant finding was that there is a tornado alley in North and Central

Alabama consisting of two smaller and distinct alleys.

With little to no previous studies conducted on Dixie Alley, I feel that my research has filled some gaps where information is lacking. When one thinks of a

79 80

tornado alley, most think of the Great Plans in the Central United States. Not many think of the Southeastern United States as a tornado prone area, however those who inhabit these states will argue. My purpose for this research is to help save lives.

This coincides with the main goal of the National Weather Service in their efforts to save lives and property. If this study can help in further educating forecasters to the local effects of tornadoes in Alabama, then I feel I have done my part as a meteorologist in helping to prevent the loss of life.

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