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Perceptions toward Severe Weather in the Geneva, New York
Region
By Eliza Orrick
ABSTRACT
People’s perceptions towards severe weather are often not clear. However, it is very
important to gain a more complete perspective, because the perception people have on weather
events impact risk-assessment and emergency preparedness. In the wake of the flash flood event
in Penn Yan, New York on 13 May 2014 this research was conducted to determine severe
weather perception in and around the neighboring town of Geneva, New York. Although the
Geneva participants followed the weather, understood emergency messages, and relied on the
media to inform them on severe weather, the participants generally did not have nor think about
an emergency plan of their own. In cases of unexpected weather, this lack of preparation is
dangerous because it places those individuals in a situation where harm could be done. While
this research is still in its infancy, it is important that more emphasis is placed on emergency
preparedness and planning.

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I. Introduction
People are commonly predisposed to only think of severe weather in terms of scientific
data. Although scientific data is important, the ultimate decision in emergency planning for
severe weather events is determined by an interdisciplinary approach. Practitioners, the
decision makers in emergency management, have to assess severe weather risks from not
only a scientific standpoint, but by incorporating how individuals’ and groups’ connection
with the disaster or prior knowledge will influence behavior. Only limited research has been
done to provide a complete analysis on how individuals will perceive and react to severe
weather. Several target studies have been undertaken that provide a rough and incomplete
framework. Although there have been commonalities between case study and survey results,
the research conducted in this report, along with scholars’ research selected in this report, are
not created to suggest causality but rather relationships. In light of the recent flash flood
beginning on 13 May 2014 in Penn Yan, New York, approximately 18 miles from Geneva,
New York, this study was conducted to see how prepared people are in the Geneva area for
not only floods but also other severe weather events (Figure 1). This paper provides
information about the perception of the weather by a sample of respondents in and around
Geneva, New York.
a. What Happened in Penn Yan
On the evening of Tuesday 13 May 2014 a low-pressure system moved east from South
Dakota toward Penn Yan, New York. At roughly around 2325UTC thunderstorm clusters
formed into a squall line and dumped 1 to 1.5 inches of rain per hour on Penn Yan in a span
of only a few hours causing massive property damage as streams overflowed and a flash

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flood roared through town (The Observer, 2014). Over the next three days, the storm
continued to dump rain over Penn Yan, and by Friday16 May 2014 the estimated
precipitation total was between 5 and 9 inches while the estimated property damage for both
residents and business was more than 3 million dollars (The Observer 2014). Although Penn
Yan has experienced flash floods in the past, such as the 1972 flash flood caused by
Hurricane Agnes, the flash flood that occurred in May was unexpected because the quick
succession of rain in a short time period. The 911 Center reported that between 13 May to 17
May, they received more than 2,000 calls (The Observer 2014). The town was lucky to
receive help from the surrounding area but many believe it will be years before the town fully
recovers.
The flash flood that occurred in Penn Yan was caused by a cluster of mesoscale
convective system (MCS), otherwise put as an organized region of shower and thunderstorms
(Figure 2). Specifically, this event followed the frontal flash flood paradigm as a stationary
synoptic-scale frontal boundary oriented west to east with winds running parallel to the front
was present (Maddox et al. 1979). Figure 3 indicates a rough outline of where the front was
located on 13-14 May. When looking at Figure 2, a clear region of convection with a west to
east orientation was located in this region.
Mesoscale convective systems often occur during nighttime hours and are triggered by a
series of characteristics. These characteristics include warm air advection, which can be
reinforced by a mid-troposphere trough, both high surface dew point temperatures and high
moisture content throughout the troposphere, and a nearly stationary thunderstorm outflow
boundary (Maddox et al. 1979). Unlike synoptic-scale storms, which form ahead of a strong
upper-level trough, mesoscale convective systems do not necessarily need a trough aloft

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(Houze 2004). During summer months when there is not a strong upper-level jet stream, lift
is generally caused by warm air advection. The amount of instability or the CAPE modulates
the potential for storms. However, in order for an environment to have a lot of CAPE there
needs to be moisture in the air and in order to sustain the storm, there needs to be a boundary
that acts to focus the convection (Houze 2004).
Identifying and understanding the scientific causes of Penn Yan’s catastrophe, as well as
other catastrophes around the world, is important but it is only the preliminary step when
addressing the appropriate steps toward recovery. The next segment will discuss the
importance of an interdisciplinary approach to severe weather management through analysis
of a variety of different research studies.
b. An Interdisciplinary Approach to how People Impact Science
Previously, two case studies from Denver and Fort Collins, Colorado were conducted to
understand how scientific information on flood risk would affect decision-making by
practitioners. In Denver, debate was centered on the Cherry Creek Dam; a dam, which had
little risk to overflow but if it did would lead to considerable damage (Morss et al. 2005). The
topic of the debate centered on whether the dam’s walls needed to be built higher. There was
heavy disagreement between scientists, state offices, federal offices, and the public on
whether using taxpayers’ dollars to pay for higher dam walls was a necessary safety
precaution. In this real-life scenario, the main question was the dam walls, but the focus
skewed from safety precautions to money. In the end, the high level of uncertainty caused by
a lack of consensus finalized the decision of inaction.
Meanwhile in Fort Collins, the major flood of 1997 spurred debate over the design
rainfall standard that would determine whether the town was at risk for another flood (Morss

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et al. 2005). Again there was uncertainty, as both scientists and local stakeholders disagreed
on the design rainfall and acceptable level of risk. Unlike in Denver, a panel was created to
combat the uncertainty caused by conflicting scientific opinions. Although multiple scientists
presented to the panel, the Fort Collins City Council determined which scientist was correct.
In this real-life scenario, the main focus was the design rainfall standard, but rather than
scientific information finalizing the decision, politics were the man driver due to conflicting
viewpoints.
In both case studies the final decision, or lack of, was determined not by scientific
information but by economics, societal uncertainty, and/or politics (Morss et al. 2005). With
economics, social uncertainty, and/or politics guiding the decision-making process, it is
evident that when emergency management is involved, science is not always the determining
factor.
Typically when assessing risk in emergency management, scientists have relied on
the end-to-end approach to evaluate the risks and provide a solution for a specific
catastrophe. The end-to-end approach is a feedback mechanism where scientists and
practitioners, otherwise known as the decision makers in emergency management, work
together to apply scientific research to real life situations (Morss et al. 2005) (Figure 4). The
original model of the end-to-end approach simply involved a feedback process between the
scientific research and the practitioners, however the model has since expanded to include an
integrated approach of multiple disciplines including climatology, meteorology, hydrology,
statistics, engineering, geography, and the political and social system (Morss et al. 2005)
(Figure 5). The integrated approach to end-to-end research is crucial in risk assessment for

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emergency management because it takes into consideration the different angles that other
disciplines have to offer while providing for a group consensus.
At the National Weather Center in Norman, Oklahoma, for instance, a study was
conducted concerning public knowledge of weather risks along with the ideal lead time for
tornado warning (Hoekstra et. al. 2011). Although, scientific research in the field provided
technology that was able to warn people two hours prior to an impending tornado, the special
certainty was not as accurate. As a result, people were predisposed to choose a shorter
warning time of 14 minutes since there was a greater guarantee of the tornado’s location.
Before asking participants on the desired length of lead warn time, the survey
required that the participants would answer a variety of questions, which corresponded to a
placing on Slovic’s risk factor map (Hoekstra et al. 2011). The risk factor map was invented
by a psychologist named Paul Slovic whose diagram is divided into four quadrants based on
the feelings associated with the risk (control or dread) and how much information was known
about the risk (known or unknown) (Hoekstra et al. 2011). The majority of people placed
“tornadoes” in the quadrant that defined them as relatively uncontrollable and unknown even
though some research had been done on them (Figure 6). Despite feeling that tornadoes are
uncontrollable and relatively unknown, people still did not want an increased lead-time for
them because when faced with a severe weather risk, as Slovic determined himself, more
information can often overwhelm people (Hoekstra et al. 2011). Although tornadoes are not
prevalent to New York there is much that is still relatively unknown and not necessarily
controllable in regards to severe weather risk. These unknowns could be discovered if other
disciplines get involved and preform studies.

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The previous reports in Colorado and Oklahoma demonstrate how people impact the
decision-making process. Uncertainty is a main factor where alternatives to scientific results
arise. However, before an analysis on how extreme weather influences people’s perceptions
it is first important to see how regular weather influences people.
A nationwide Internet survey was created in 2011 in which 1,461 users of forecast
information were asked a variety of questions to discover their sources, uses, and perceptions
on weather. The main idea of the survey was to analyze how a variety of factors including
weather exposure, forecast accuracy, weather variability, and socio-demographic factors
affected individuals’ attitudes and future behaviors. On an everyday basis, 72% of the
participants looked at the weather “out of curiosity” and 55% looked at the weather to
determine what to wear (Demuth et al. 2011). Weather was determined to be more important
when deciding a “leisure activity” involving travel, social, or outdoor activities rather then
for scheduled mandatory activities such as work or school (Demuth et al. 2011). In other
words, weather information is used most often for something that a person has control over
(such as a weekend plan or an outfit). People who lived in variable climates, however,
checked the weather often and routinely. Unlike forecasts for severe weather, people were
only slightly concerned with the accuracy of the weather prediction regardless of the climate
they lived in. Females were more prone to check the weather, indicating gender difference in
attitudes toward dressing and family roles (Demuth et al. 2011). For instance, motherly
instincts make females more aware of both themselves and their family dressing
appropriately for a variety of weather conditions. Final conclusions revealed that how people
use and perceive forecasts is interrelated and that there is a strong positive relationship
between weather use and perception (Demuth et al. 2011). That means that the more

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someone uses the weather, the more the weather forecast will impact their decisions and how
the person will perceive the weather.
The salience (importance) of weather, to a person is important information for
emergency management because salience will determine how closely people follow and pay
attention to weather forecasts. At the University of Georgia, 946 undergraduates were
interviewed to determine the relationship of weather salience to other weather related
attitudes, knowledge, and experience (Stewart 2012). Out of the 946 people interviewed, the
degree of weather salience was normally distributed with an equal number of people having
both a weak and strong degree of salience. Women had only a slightly higher mean score of
salience (99.96 out of 129) then men did (96.63ot of 130), but knowledge of weather and
experience of weather damage had more of an effect on the relationship of salience for men
(Stewart 2012). Both genders that experienced evacuation due to an extreme weather event
were found to be more weather salient. Concluding remarks suggested that for the university-
based participants: weather salience was related to knowledge and experience of extreme
weather events (Stewart, 2012). Forcing people into a severe weather situation may not be
the best strategy to increase weather salience, but giving people knowledge about weather is
crucial to increase the importance. This Stewart (2012) study introduces a new dimension on
weather perceptions and salience, besides the traditional approach that knowledge is
important. The study indicates that if the subject does not have experience with severe
weather and is not knowledgeable, weather will not seem important and this could be
dangerous in the case of an approaching severe weather event.
Knowledge on severe weather is vitally important and as indicated by a questionnaire
on an online data service provider called Amazon’s Mechanical Turk, there was a

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significantly strong relationship between the level of knowledge someone had about severe
weather and the presence of a severe weather phobia (Coleman et al. 2014). As indicated by
an interview of 81 candidates in 1996, 80% of phobics receive their phobia after a severe
weather event. Although phobics make up a small percentage of the population, it indicates
that after catastrophic severe weather events not everyone is debriefed properly.
The previous case studies provide a background on the main research topic of severe
weather perceptions. The first studies identified people’s impact on the decision-making
process by introducing the power of uncertainty. Although scientific data initially identified
the target problem area, uncertainty in the risk-assessment made economic, social, and
political variables finalize the decision. The next studies reinforced the main argument that
severe weather research is an interdisciplinary field. Practitioners use a complex web of
different parameters encompassing the political, social, and economic spheres when making
decisions. The final studies, introduced that the processes in which perceptions of everyday
weather are formed are related to uses and socio-demographic characteristics. However, in
the case of a severe weather event, perceptions change toward the weather, orienting people
around the degree of risk. Salience in severe weather is developed through both knowledge
and experience in severe weather, while weather phobia is related to lack of knowledge.
Therefore, although on a risk assessment level, scientific knowledge is not the only factor, on
an individual level scientific knowledge of extreme weather is important. This research
background will be applied to people in the Geneva, New York region by asking them to fill
out a survey to ascertain their perceptions towards severe weather.

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The following section will discuss the methodology of the research collected from the
survey in Geneva, New York. Section III will outline the results, and section IV will provide
a discussion and concluding remarks on the study.
II. Methodology
The research was motivated by the question, “Why in this age of advanced technology
where multiple media outlets warn about weather do people still get hurt during extreme
weather and natural disasters”. Specifically this research focused on whether it was a lack of
knowledge or preparedness.
The objective of the research was to create a preliminary understanding of local
perceptions toward extreme weather. With limited access and means to get the access to a
representative enough sample of the United States, a subsection of the population of Geneva,
New York was sampled.
Geneva is a small city in upstate New York with has a population of 13,210 people (City
Data 2012). The summer months are marked by humid days and cool nights with temperature
averages ranging from 60°F to 80°F (City Data 2012). Winters are marked by cold
temperatures ranging around 16°F to 31°F, and Geneva typically gets around 54 inches of
snow per year with most of it occurring during January. Geneva also gets around 33 inches of
rain each year, with the mean largest amount of precipitation occurring in June (Us Climate
Data 2014)(Figure 8).
A survey composed of 16 questions was drafted in order to determine how much locals
understood about extreme weather and how prepared they are if something was to happen.
The first 12 questions had 5 choices ranging from strongly agree to strongly disagree.
Questions 13 and 14 required that the participant complete choices A through E and fill out

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of the corresponding answer for each letter ranging from less to more. Question 15 also
required the participant to go through choices A through E and fill out the corresponding
answer ranging from no fear to severe fear. Question 16 required that the participant chose an
option ranging from much less to much more (Figure 8). This survey was distributed to 21
locations in and around Geneva, including but not limited to Joe’s Hots, Flower Pedal Café,
Water Street Café, Bagels and Cakes, Pinkie’s Bar, White Springs Vinery, etc. Surveys were
left out for people to fill out and participation was completely voluntary (Figure 9). Four of
the locations did not gain any data because the surveys were misplaced. In total 171 surveys
were completed over a span of 3 weeks. Surveys were left in the location for generally
around a week and checked every 2-3 days before they were moved to a different location.
The survey data was then inputted into SPSS, a statistical software program. The main
measures that were analyzed were frequencies, chi-square, significance level, and gamma.
Chi-square is an inferential statistical test used to analyze significant relationships between
two nominal or ordinal variables in a bivariate table. The significance level is a measure,
which reveals if the chi square or gamma statistic can be applied to the general population.
Chi-square tests were conducted to determine if the null hypothesizes could be rejected to
determine if there is a relationship between the variables. Rejecting the null hypothesis
means that there is a relationship while failure to reject the null hypothesis indicates there is a
no relationship. Gamma is a statistical measure used to indicate strength in a relationship. A
two-tailed test was performed and the validity of the test was determined by both the chi
square value and the significance level at a .05 alpha level. Since the significance level was
tested at an alpha of .05, that means that any data that has a significance greater than .05,

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cannot be applied to the general population. Chi-square is very dependent on sample size so a
significance level greater than .05 could be the result of a small sample.
A variety of charts were created to analyze the data to see if there was a relationship
among variables and a selected group were chosen for this report. The charts that were
chosen either portrayed a significant relationship between the variables being measured or
were surprising in their lack of relationship.
Although the results of this survey cannot be representative of Geneva, New York and
the surrounding area as a whole, the survey can begin to answer the question of how
knowledgeable people are about severe weather, how prepared, and what influences the
choices people make regarding severe weather.
III. Results
A. Sample Demographic information
Within the sample of 171 participants who participated in the survey, 37% were men,
60% were women and 3% classified themselves as neither men nor women (Figure 8). The
degree of education was divided into six categories with 3% educated at a middle school
level, 29% educated at a high school level, 24% educated at an associate level, 22% educated
at a bachelors level, 12% educated at a masters and 10% educated at a professional degree
level. The majority of participants were white (90%), but 1% was African American and 9%
were other ethnicities. The majority of the sample had kids (61%)(Figure 8). Age was
divided into six categories, with 17% between 18 and 27, 13% between 28 and 37, 14%
between 38 and 47, 17% between 48 and 57, 27% between 58 and 67, and 14% who were 68
and older. Employment status was divided into 6 categories with 55% employed for wages,
12% self-employed, 4% out of work, 1% in the military, 23% retired, and 5% unable to

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work. Marital status was divided into 3 categories with 41% single, 44% married, and 14%
other (whether that be divorced, widowed, etc.) (Figure 8).
B. High Frequency and Relationships
To account for the smaller sample size of 171 participants and to indicate the stronger
category, agree and strongly agree were condensed into one category of “Agree”, and
disagree and strongly disagree were condensed into one category of “Disagree” to determine
the result frequencies.
Participants were asked a variety of questions regarding their usage of weather forecasts,
along with questions that indicated their knowledge and preparedness in severe weather. In
general, most participants indicated that they agreed that they understood severe weather
(76.5%), followed severe weather (74.9%), and stayed up to date with severe weather
(78.9%). Yet a surprising amount of people did not have an emergency plan (46.4%) and a
vast majority did not think a great deal about severe weather (73.5%) (Figures 10A-E). Most
participants knew about the flash flood on 13 May in Penn Yan, NY (86.94%). Most of the
participants thought they lived in a high-risk area for winter storms (73.5%), but most people
did not think they lived in a high-risk area for floods (51%).
The study also sought to discover if participants paid attention to changes in their area
and if anyone in the study experienced intense fear or phobia toward any of the listed severe
weather events. Participants indicated that heavy rain storms (45%) and snowstorms (40.4%)
were becoming more common in the local area and that consequently there was more
problems in the local area concerning harm to crops (50.9%), floods (45%), and extreme cold
(49.1%). Nobody in the study experienced high amounts of fear toward any of the specified
severe weather events that would suggest a phobia. Participants indicated that they had little

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fear toward heavy snow, floods, hurricanes, tornadoes, and no fear about thunderstorms
(Figure 8). Most participants recorded that compared to the rest of the United States; Geneva,
NY receives less severe weather (54%). This indicates that participants do not perceive
themselves to be in more of a high-risk area for severe weather than other locations in the
US.
Participants were also asked, “if their friends and family impact their decisions about
severe whether more than media”, and, “if the media impacted their decision about severe
weather more than family”. Depending on how the question was worded (with media
mentioned first or family), yielded a different distribution between agree, neither, and
disagree. The two graphs illustrated in figure 11, reveal that when media was mentioned first,
there was more of a distinction in the most popular answer than when family was mentioned
first. On the first graph when media is mentioned first, 59.6% agreed, 26.3% were unsure,
and 14% disagreed that media impacted their decision more than family. On the second
graph when family was mentioned first, 30% agreed, 26.5% were unsure, and 43% disagreed
that family impacted their decision more than media. Since the frequency for unsure remains
relatively constant, this shows that depending on how the question was worded, some people
changed their answer.
In order to see if the relationship between weather usage and perceptions found in
Demuth’s study applied to the Geneva sample, two chi square tests were conducted to test the
relationship between following the weather and planning a day around the weather. If the
relationship was to hold true to Demuth’s study, there should be a positive relationship
(Demuth et al. 2011). As demonstrated by the bar graph on figure 12 there appears to be a
relationship(Figure 12). The blue line, which represents agreeing to plan one’s daily routine

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around weather, has the highest frequency under agreeing to follow severe weather, which
suggests a positive relationship. In order to reveal if there was a relationship and discover its
strength, two chi square and gamma tests were completed.
In the first chi square test the variables were not condensed (5 variables of strongly agree,
agree, neither, disagree, and strongly disagree) in order to accurately represent the data given
from the surveys. There was a positive chi square value of 42.371 with a significance level of
.000 and a gamma of .49. This indicates that the null hypothesis can be rejected and that a
moderately strong relationship existed. In the second chi square test the variables were
condensed (3 variables of agree, neither, and disagree). Although the chi-square value was
slightly lower (20.529), the significance of .001 reveals that a legitament relationship was
found. The gamma of .5 indicates that this relationship was moderately strong. In statistical
terms this indicates that the null hypothesis can be rejected and that a moderately strong
relationship existed. As the percentage of people agreeing to follow the weather increased,
the percentage of people planning their day around the weather increased and vice versa. As
revealed by the chi-square and gamma, Demuth’s relationship applies to the Geneva sample.
In order to understand this relationship further, gender and age were tested as control
variables to discover if they impacted the relationship. Gender did not affect the relationship
but age was conditional. A conditional variable means that the relationship is present for
some variables but not others. Two chi-square tests were again completed, but this time one
test had age condensed and the other did not. Depending on whether or nor age was
condensed changed the result of the test. When age was not condensed, the age group of 48-
57 year olds had the strongest relationship (Chi square of 16.174 with a significance of .013).
However, the people who were 38-47 and people older than 68 were distinguished by their

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strong lack of relationship, as indicated by their low chi-square and high significance values
(Figure 13). When the sample was condensed to gain a more accurate chi-square test, age
was categorized into three categories with 51 people falling into 18-37 year olds, 51 people
falling into 38-57 year olds, and 68 people falling into 58 years old and greater. When age
was condensed it was 18-37 year olds who had the strongest relationship than all the other
groups. The chi-square value was 11.737 with a significance of .019 and the gamma of .525
with a .008 significance value indicating a moderately strong relationship. The low
significance and high chi square also indicate that the null hypothesis can be rejected. Since
chi-square accuracy is dependent on the sample size, the second test is the more reliable
measure, which indicates that the relationship is strongest between following the weather and
preparing the day around the weather for 18-37 year olds.
Although a frequency test was already conducted to determine whether the media or
family impacted participant’s decisions about weather more, a chi-square test was conducted
to determine if education affected the relationship. As discussed earlier, there were two
questions (2 and 8) that analyzed whether media or family effected the decision more. Two
chi-square tests were conducted, one analyzed question 2 in terms of education (not
condensed) and one analyzed question 8 in terms of education (not condensed). The results
from question 2, “Friends/family impact my decisions about the weather more than media”,
was more statistically significant (chi-square of 44.186 at a significance of .001) and so for
the remaining of the relationships, question 2 was analyzed over question 8 (Figure 14).
Before the chi-squares were conducted, two bar charts were created to see if a visual
relationship was present (Figure 15). As indicated by the chart, the only categories that didn’t
increase as the data moved toward disagree were those with a middle school education and

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high school. It is important to note that the sample size in each category was small for
education, however, across the board when looking at the bar chart it is evident that past a
high school education there is a switch in response (agree to disagree) The chi-square
confirmed the visual representation on the chart (chi square= 44.186 with a significance of
.001). The chi square value indicated that the null hypothesis could be rejected and that there
was a relationship between education and family effecting weather decisions more. This
relationship was weak to moderately strong as indicated by the gamma of .334. That means
that in some cases participants with less education are more likely to say family effects their
decision about weather more than the media. The relationship was reinforced by the 47.1%
difference in those who picked disagree that family impacted their decision more than the
media between people with professional degree and middle school degree. Since the
relationship is moderately weak, there cannot be a direct claim that more education means all
participants will be more likely to have media impact their decision on the weather.
However, it is still important to note the switch in response between people with less than a
high school education and those with more. The switch in response after a certain degree of
education reveals that rather this relationship being linear, the amount of education could
affect the response only until a certain point. In this case, the pivotal education point being
less than a high school education or more.
A chi-square was next conducted to see if age affected the relationship of family
impacting their decisions more than the media. The age group of 18-27 years olds stood out
because 48.3% of the time they said agree that family impacts their decisions on severe
weather more than the media, while all other groups indicated disagree. However, although

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the chi-square value was significant (35.184 with a significance of .019), the data was not
large enough to specify the strength in the relationship.
The study then sought to discover if the participant’s perceived risk of where they lived
(whether they indicated high risk or low risk) effected how prepared people were. People
who live in a high-risk area for floods tend to be more prepared for the flood than those who
lived in a place that had a high risk of snowstorms. In this question, those who thought a
great deal about preparing for a severe weather event were considered prepared. As indicated
earlier only 19% of people indicated that they lived in an area that has a flood risk, yet those
people were more prepared than a vast majority of the 73.5% who perceived that they lived
in a high-risk area for snowstorms. There was a 44.7% difference in preparedness between
those who strongly agreed they lived in a high-risk area and those who strongly disagreed
they lived in a high-risk area for floods. When looking at the bar charts on figure 16,
although a strong relationship does not seem to be present, the second chart depicting
preparedness for flash floods reveals more obvious relationship than the first bar chart. The
chi-square indicated that although the relationship was weak, it was present for those who
lived in a high-risk area for flood (chi square value of 61.25 with a significance of .000 and a
gamma value of .239). Although the chi square value was not significant enough, there was a
22.3% difference between those who did not think they were prepared (disagree) but said
they lived in a high-risk area for snowstorms and those that did not.
Overall, most people said they strongly agree that they are prepared and strongly agree
they live in a flood plain (50%), but for snowstorms, most people said they they disagree that
they are prepared but agree that they are in a high-risk area for snowstorms (42.3%).

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The relationship between fear and living in a high-risk area for floods and snowstorms
was also tested but because of the small percentage of people who had fear, the data was not
large enough to produce a significant chi-square test. However, there was a suggestion that
those who live in a snow area are less afraid of snowstorms.
Besides studying risk-perception and fear, the study also sought to discover if those who
were more observant of increasing weather (survey question 13) conditions experienced fear.
As indicated earlier, heavy rain and snowstorms are perceived to be most prevalent in the
Geneva, New York area. There is a relationship between those who stated there had been an
increase in rain and those who have fear in floods. Although generally, there was little fear in
floods, there was a 19.3% difference for those who said there were more floods between
those with a severe fear and those with no fear. The chi square value of 18.6 with a
significance value of .029 indicates there is a relationship although, again because of the
small sample size of those who had fear, the strength of the relationship was not significant
enough to be certain.
The study also sought to discover if cognizance of increasing snow and rain conditions
(survey question 13) would increase a person’s level of perceived risk and, therefore, make
them more likely to have an emergency plan. A bar graph was created to indicate if there was
a visible relationship, between those who noticed an increase in rain and those who had an
emergency plan (Figure 17). As indicated by the graph, there did seem to be a relationship
and as a result a chi-square test was conducted. As indicated by a chi-square of 19.281 with a
significance of .082 and a gamma of .216 there was a weak relationship. This shows that
despite, being aware of increasing rain, participant’s level of perceived risk has not increased.
Other relationships were tested but did not have a statistical relationship. For example,

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knowledge of Penn Yan flood did not have an effect on the participant’s general fear of
floods. Having kids did not effect whether participants were impacted more by the media or
by family when making decisions about severe weather. Having an emergency plan and
knowing the difference between watch/warning was not affected by education. Finally a
questionable finding that was discovered was that people with kids who knew about Penn
Yan were less afraid of floods. However, in the case of the last finding there is probably an
intervening variable that affects that relationship.
IV. Conclusions
Originally when conducting this research study the aim was to decipher the perceptions
that people in Geneva, New York had about severe weather. As argued previously, severe
weather research and emergency planning are not limited to just scientific data but are also
reinforced by other disciplines, including sociology. That is not to devalue scientific data, for
as seen in previous examples, scientific knowledge is necessary to understand severe weather
events and the appropriate precautions. The questions that were asked on the survey
identified how prepared and knowledgeable people in the Geneva, New York area were on
severe weather.
Although results from this survey cannot represent causality and cannot be representative
of the whole population in Geneva, New York, the results can be used to reveal relationships
and add on the framework of severe weather perceptions. Most participants did pay close
attention to the weather and were aware of severe weather events occurring around them,
however, it was shocking that most people did not have an emergency plan nor had thought
about what to do when severe weather strikes. Although Geneva, New York, may not be a

21. 21
high-risk area for severe weather and natural disasters, it does receive a fair amount of harsh
winter storms.
The relationships that were analyzed in this study were encouraged by previous studies.
Demuth’s research on the linkage between weather usage and perceptions provided the focus
for the first two relationships analyzed (Demuth et al. 2011). Similar to Demuth’s research,
there was a linkage between following the weather and planning the routine around the
weather. The relationship was analyzed further, however, to see if certain variables affected
the relationship. Age was discovered to affect the relationship. Specifically, the youngest age
category 18-37 year olds were affected the most suggesting that maybe the relationship is
connected to youth and having more energy to perform activities outside.
Being interactive creatures who socially construct their world around interpersonal
relationships, it was hypothesized that family would impact people’s decisions on severe
weather more than the media (news, TV, internet). Results for the majority indicated
otherwise which suggests that when it comes to severe weather people put more of their faith
into the media to warn them and tell them what to do. Both the youngest age group and those
with an middle school and high school education, however, believed family had the greatest
impact. Both of these relationships toward family could be driven by the dependence on the
family. As people get older and form their own families, their initial dependence on the
family they grew up on begins to dissipate. Although, people still stay in contact with their
families and generally still remain close relationships, the initial dependence disappears when
people begin to start their own lives. Likewise, those with only a middle school or high
school education could be more dependent on their families than people with a higher degree

22. 22
of education. When severe weather hits, those who are younger and those who have less
education look to their family around them to guide their decisions.
Originally question 2 and question 8, which are both concerned with the media or family
impacting the weather more, were added as a check to make sure that people were taking the
survey seriously. It would not logical for instance to say both strongly agree “to family
impacting the weather more than the media” (Question 2) and “the media impacting the
weather more than family” (Question 8). Although media consistently impacted people’s
decisions on severe weather more than family, when the question was worded as
“family/friends impacting the decision more than the media” the difference between the
results were not as drastic than when “media” was placed first. This is particularly interesting
because one would expect that the two questions would mirror each other in terms of the
distribution of answers, for example those who answer strongly agree to one answer should
answer strongly disagree to the next answer. Yet something about placing media first made
people more distinct in their agreement. There is the possibility that people simply did not
understand the questions, however, it is more likely that the word “family” gives out a
connotation that makes people more attached and less likely to say that they disagree with
their family. It is also important to note that the change was in agree and disagree. The
percentage in the “don’t know column” remained unchanged between the two questions.
The study also sought to study the relationship between perceived risk, fear, awareness,
and preparedness. When studying these variables, the biggest problem was that the sample of
people who indicated fear and indicated living in a high-risk area for floods was not very
large. Since chi-square is impacted heavily by sample size, the chi-square results were not as
reliable as they could have been. There did seem to be, however, some type of relationship

23. 23
between fear of rainstorms and thinking about the weather. This relationship, was not linked
to preparedness, however, because even those who noticed an increase in rainstorms and
snowstorms were not prepared. Perhaps routine effects the amount of preparedness a person
has. Since upstate New York is known to get a lot of snow and rain people have incorporated
snow and rain preparations into their routine. Therefore when asked if they have an
emergency plan, they may indicate they don’t, because in their opinion they are just doing
what they normally do. Therefore there does seem to be a relationship between awareness,
perceived risk and fear but in this sample, it did not effect preparedness.
An unexpected result was that size of family did not impact emergency planning or fear.
Before conducting the survey, it was hypothesized that participants with children would be
more attentive to the weather and more likely to be prepared for extreme weather conditions.
It is possible, however, that kids do have an impact but in order to study it one would have to
create a longitudinal study analyzing people’s perceptions about weather before and after
having kids to see if there are any changes.
The study also aimed to discover if participants understood the difference between
“watch” and “warning” to gage on a small scale how knowledgeable the participants were on
extreme weather warning systems. Knowledge was not based on education, which is
reassuring since the majority of the participants indicated they knew the difference. Although
most people indicated that they were not prepared, the majority follow the weather, look to
media, and are knowledgeable on the warning system, which indicates that when participants
are warned ahead of time, they will be able to act accordingly.
Understanding the perceptions of severe weather is vital because it provides a
framework of action. From this data, it is evident that although people are generally

24. 24
knowledgeable about severe weather, more people need an emergency plan or strategy.
Although this cannot necessarily be proven to be true of the entire population of Geneva, NY
and definitely not the entire population of the US, being more prepared is never a bad
strategy. The biggest issue discovered from this research is that when unexpected severe
weather events hit, the majority of participants are not prepared to counteract that severe
weather event. This finding is not only true for the Geneva participants but is also suggested
by the mass destruction and confusion from the unexpected flash flood that occurred in Penn
Yan. There is no way to prevent formation or occurrence severe weather but if people are
more prepared and have back-up plans and strategies, there will be at least another obstacle
in the way of storm to prevent harm.
ACKNOWLEDGEMENTS
The author thanks Professor Nicholas Metz for supporting her research, helping her draft
multiple surveys and reports, and guiding her. The author also thanks Professor Freeman for
teaching and helping her figure out the right SPSS measures to use. The author thanks
Professor Monson for giving her background reading material and helping her with
demographic information on her survey. Finally, the author thanks all the stores who
participated and allowed her to leave her surveys and all the participants that made this
research possible.

25. 25
V. Appendix
Figure 1. Map of New York State depicting Geneva and Penn Yan
Figure 2. Radar showing thunderstorm clusters

26. 26
Figure 3. Radar Imaging for Penn Yan severe weather event with clear front running through
New York and influencing the Mesoscale convective system (region defined by blue box).
Figure 4. Scientists’ typical view of research and development to produce useful
information for society: “end-to-end” research, illustrated for the case of flood- risk
management. The connection from decision maker to research (represented by a dashed
line) is mentioned in some implementations of end-to-end research, but in others is left
implied or assumed.

27. 27
Figure 5. Revisedview of research to produce information that is useful in one or more
specific societal applications: “end-to-end-to-end” research, illustrated for the case of
flood-risk (specifically floodplain) management with diverse, interconnected decision
makers. The end-to-end-to-end approach explicitly recognizes the importance of
multidirectional communication; sustained interactions among researchers, application
developers, and multiple decision makers; and multiple iterations around the loop to
coproduce knowledge and tools. Integrated scientific research includes disciplinary and
interdisciplinary work in statistics, climatology, meteorology, hydrology, engineering,
geography, and the social sciences and humanities. The two ENDs in the figure represent
the two ENDs in end- to-end research (Fig. 1); end-to-end-to-end research signifies
iteration between these two ends.
Figure 6. Simplified version of Slovic’s (1987) risk factor map, depicting risk perception as a
function of the degree to which a hazard is ‘‘unknown’’ or ‘‘dreaded.’’ The lower left quadrant

28. 28
depicts those hazards that are clearly understood, common everyday risks (e.g., elevators). The
upper left depicts those that are less known and less risky (e.g., caffeine). The lower right depicts
those whose risks are more known and more dreaded (e.g., nuclear weapons). The upper right
depicts those that are not well known and more dreaded (e.g., tornadoes); people demand public
intervention for these types of hazards. The lighter region represents the hy-pothesized zone in
which the authors suspect tornadoes to fall.
Figure 7. Average Rainfall in Geneva, New York

29. 29
Figure 8. Most frequent Response for each Survey Question as indicated by blue circle

30. 30
Figure 9. List of locations where surveys were completed as well as the percentage of how many
surveys were completed at each location.

31. 31
Figure 10. Panels A-
A. Understanding Severe Weather
B Following Severe Weather
C Staying Up to Date with Severe Weather

32. 32
D. Part Having an Emergency Plan
Part E Thinking about Strategies Preparing for a Natural Disaster
Figure 11. Results for Question 8 “Does the media impact your decision on severe weather more
than family” and results from Question 2 “Do friends or family impact your decision on severe
weather more than the media” with a control variable of education. Despite being ultimately the
same question but revered, there is a different distribution among answers, the most noticeable
being depicted in the totals column and in the responses with people with middle school
education.

33. 33
Figure 12. Relationship between Question 1 (“I follow news about the weather” ) and Question 3
(“I plan my daily routine around the weather).
Figure 13. Chi Square test for Question 1 (“I follow news about the weather” ) and Question 3
(“I plan my daily routine around the weather) with control variable Age.
A. The chi square relationship does not indicate a relationship for 18-27 year olds
(significance=.120), 38-47 year olds (significance=.389), 58-67 year olds (significance =.134),
and 68 and greater (significance= .509) because the significance level is too high which means
there is no relationship. For 28-27 year olds (significance=.063) and 48-57 year olds
(significance=.013), however, there is a relationship because the significance value is high.
Chi-Square Tests
Age Value df Asymp. Sig. (2-
sided)
18-27
Pearson Chi-Square 14.074b
9 .120
Likelihood Ratio 15.361 9 .081
Linear-by-Linear Association 9.262 1 .002
0
20
40
60
80
100
Agree Niether Agree
nor Disagree
Disagree
Percentage
Follow Severe Weather (Question 1)
Relationship between following weather and planning
daily routine around weather
Agree
Niether Agree nor Disagree
disagree
Plan Daily Routine Aro
Weather (Question 2)

34. 34
N of Valid Cases 20
28-37
Pearson Chi-Square 11.938c
6 .063
Likelihood Ratio 12.781 6 .047
Linear-by-Linear Association .003 1 .956
N of Valid Cases 15
38-47
Pearson Chi-Square 4.125d
4 .389
Likelihood Ratio 5.062 4 .281
Linear-by-Linear Association 1.924 1 .165
N of Valid Cases 14
48-57
Pearson Chi-Square 16.174e
6 .013
Likelihood Ratio 16.395 6 .012
Linear-by-Linear Association 6.220 1 .013
N of Valid Cases 22
58-67
Pearson Chi-Square
13.697f
9 .134
Likelihood Ratio 15.023 9 .090
Linear-by-Linear Association 1.277 1 .259
N of Valid Cases 36
68 and greater
Pearson Chi-Square
1.351g
2 .509
Likelihood Ratio 1.911 2 .385
Linear-by-Linear Association 1.252 1 .263
N of Valid Cases 19
Total
Pearson Chi-Square
42.371a
9 .000
Likelihood Ratio 36.188 9 .000
Linear-by-Linear Association 21.755 1 .000
N of Valid Cases 126
a. 8 cells (50.0%) have expected countless than 5. The minimum expected countis .21.
b. 16 cells (100.0%) have expected count less than 5.The minimum expected countis .30.
c. 12 cells (100.0%) have expected count less than 5. The minimum expected count is .27.
d. 9 cells (100.0%) have expected count less than 5. The minimum expected countis .14.
e. 12 cells (100.0%) have expected count less than 5.The minimum expected countis .45.
f. 12 cells (75.0%) have expected count less than 5.The minimum expected countis .03.

35. 35
g. 5 cells (83.3%) have expected countless than 5. The minimum expected countis .63.
B The chi square relationship does indicate a relationship for 18-37 year olds
(significance=.109), but not for any of the categories ranging from 38-68 year olds because the
significance is too high.
Figure 14. Comparison of Chi Square and Gamma values between results from Question 2 “Do
friends or family impact your decision on severe weather more than the media” and for Question
8 “Does the media impact your decision on severe weather more than family” education. As
indicated by the highlighted section there was both a higher and more significant chi square
value and gamma for question 2.
Condensed vs Not Chi Square Gamma
Question 2 (Family
more than Media)
No-5 Variables 44.186
Significance=.001
.334
Yes- 3 variables 27.138
Sig=.001
.39
Question 8 (Media
more than Family)
No-5 variables 26.312
Sig=.156
.281
Yes-3 variables 16.3 sig=.091 -.217

36. 36
Figure 15. Education’s impact on agreeing or disagreeing to Question 2, “Do Family/Friends
impact your decision on severe weather events more than the media”. As indicated by the data,
those who have a middle school education is the only category that decreases as the chart moves
towards disagree.
Figure 16. Perception of Risk and Preparedness
A. Snow Storms and Degree of Preparedness
0
20
40
60
80
100
120
Agree Neither Agree
nor Disagree
Disagree
Percentages
Family Impacting Decision about Severe Weather more than Media
(Question 2)
Educations impact on Question 2
Middle School
High School
Associates
Bachelors
Masters
Professional Degree
Most
Recent
Degree of
0
10
20
30
40
50
60
Strongly
Agree
Agree Neither
Agree nor
Disagree
Disagree Strongly
Disagree
Percentages
Thought about Preparing for Natural Disaster(Question 7)
Preparedness and High-Risk Area
Strongly Agree
Agree
Neither Agree nor Disagree
Disagree
Strongly Disagree
Live in a high-risk
area forsnow

37. 37
B. Floods and Degree of Preparedness
Figure 17. More Rain more prepared
0
10
20
30
40
50
60
Strongly
Agree
Agree Neither
Agree nor
Disagree
Disagree Strongly
Disagree
Percentages
Thought about Preparing for Natural Disaster(Question 7)
Preparedness and High-Risk for Floods
Strongly Agree
Agree
Neither Agree nor Disagree
Disagree
Strongly Disagree
Live in a high-risk
area forFloods
0
5
10
15
20
25
30
35
40
Strongly
Agree
Agree Neither Agree
nor Disagree
Disagree Strongly
Disagree
Percentages
Have Emergency Plan (Question 6)
Emergency Plan and Beleiving Rain is Increasing Relationship
Less
The Same
More
Heavy
Rainstorms
occur___(Que
stion 13b)

38. 38
VI. References
Coleman et. al. 2014: Weathering the Storm: Revisiting Severe-Weather Phobia. Bull.
Amer. Meteor. Soc., 95, 1179–1183.
Demuth et. al., 2011: Exploring Variations in People’s Sources, Uses, and Perceptions of
Weather Forecasts. Climate Soc., 3, 177–192.
Hoekstra et. al, 2011: A Preliminary Look at the Social Perspective of Warn-on-
Forecast: Preferred Tornado Warning Lead Time and the General Public’s Perceptions of
Weather Risks. Wea. Climate Soc., 3, 128–140.
Houze, R. A., Jr. (2004), Mesoscale convective systems, Rev. Geophys., 42, RG4003,
Maddox, Chappell, and Hoxit, 1979: Synoptic and Meso-α Scale Aspects of Flash Flood
Events1. Bull. Amer. Meteor. Soc., 60, 115–123.
Morss et al., 2005: Flood Risk, Uncertainty, and Scientific Information for Decision
Making: Lessons from an Interdisciplinary Project. Bull. Amer. Meteor. Soc., 86, 1593–
1601.
Stewart, 2009: Minding the Weather. Bull. Amer. Meteor. Soc., 90, 1833–1841.
"Climate-Geneva New York." U.S. Climate Data. 2014. Web.
<http://www.usclimatedata.com/climate/geneva/new-york/united-states/usny0548>.
"Geneva, New York." City-Data. 2012.Web. <http://www.city-
data.com/city/Geneva-New-York.html#b>.
"Penn Yan Estimates Millions in Damage." The Observer: Review and
Express. Frontier, 2014. Web. 04 Dec. 2014. <http://www.observer-review.com/penn-
yan-estimates-millions-in-damage-cms-4304>