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Project Title: The Lake Michigan Effect: Possible Relationship Between Lake Michigan Ice
Cover and Stronger Storms in the Spring.
Authors: Trevor Bengtsson and Eddie Snyder
METR 4922: Senior Seminar (Capstone) II
Mentors: Dr. Jeff Kimpel (former director of NSSL) and Virginia Silvis (Ph.D. Student, Dept. of
Geography & Environmental Sustainability)
Certification of Mentors Approval: Dr. Jeff Kimpel and Virginia Silvis
Date: 1 May 2015

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Abstract
It is well known that Lake Michigan can have an effect on the lakeshore communities’
weather. It appears that the lake’s temperature drives the affect the lake has on the surrounding
environment. It is the goal of this paper to determine if a relationship exists between Lake
Michigan ice cover extent and the frequency and timing of severe storms in the Lower Michigan
Peninsula that followed in the spring/summer months. Ice cover data was used to determine four
years of interest: two extreme ice cover extent years and two years with minimum ice cover
extent. Tornado, thunderstorm wind, and hail storm reports for the four years were obtained and
filtered using radar imagery. Descriptive statistical techniques were used on the raw storm
reports data set, which showed no significant change in frequency of storm reports for the four
years of interest. Displaying the storm reports by month suggests that the ice cover extent may
lead to an early /delay of the severe weather season. However, this relationship is more in line
with the lake’s temperature, which is indirectly influenced by ice cover extent. Understanding
theses relationships can have an impact on operational forecasters and to the general public,
which warrants further research.
1. Introduction
Lake Michigan is a well-known fishing and vacation destination in the Upper Midwest. It
is also heavily traveled by cargo ships that provide goods and services to cities on the lakeshore.
Lake Michigan is one of the five fresh water Great Lakes in the United States (Fig 1), but its
latitudinal orientation is what makes it interesting for this study. This is because weather patterns
generally travel over the lake into the lower peninsula of Michigan due to the predominant
westerly flow pattern. Therefore, these weather patterns can be influenced by the lake.

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The presence of Lake Michigan affects the surrounding environment in many ways.
Cities around Lake Michigan experience warmer temperatures in the winter compared to cities
farther away from the lake. This is because the lake
waters are warmer than the surface temperatures. The
opposite affect occurs in summer where cities around
Lake Michigan are cooler than inland cities. A study
done by Notaro et al. (2012) showed that the thermal
inertia of the Great Lakes affects the precipitation
and temperature for the surrounding areas year-round by removing the Great Lakes from a
climate model. Lake Michigan also influences the precipitation in this region as well. The
majority of Western Michigan’s winter precipitation comes from lake-induced snowstorms
(Eichenlaub 1970).
Not only does Lake Michigan influence the precipitation and temperatures in this region,
it can also affect storm evolution and development. The lake itself can suppress thunderstorm
evolution in the summer months when the lake is colder than the environment (Eichenlaub
1970). Lyons (1966) also showed that thunderstorms weaken as they encounter the eastern
shores of Lake Michigan due to entrainment of lake cooled air. In the summer months, Lake
Michigan can create lake breezes, which occur due to differential heating between the lake
waters and the land surface. Lake breezes can occur on either side of the lake. Thunderstorms
may interact with these lake breezes, which can intensify the thunderstorm itself (Lyons 1966).
This intensification is due to convergence that is present at the lake breeze front (Moroz 1967).
Thus, Lake Michigan plays a crucial role on the surrounding environment and has an
impact of the weather that occurs. Most of the mechanisms that create these Lake Michigan
Fig 1. Great Lakes image from GLERL.

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affects are due to the lake temperatures. One of the main factors that contribute to the lake
temperatures is the amount of ice cover during the winter months. A large ice cover extent on the
lake can result in ice melt lasting into the spring months, which allows the lake temperatures to
be cooler than years with less ice coverage. The orientation of Lake Michigan allows it to
influence many storm systems as they propagate eastward. A study conducted by Steiger et al.
(2009) noted that the greatest frequency for cool season thunderstorms occurred in fall when the
lake water was at its warmest.
Currently, there are no published papers that have attempted to make a connection
between lake temperatures in winter to spring/summer months’ thunderstorms. Determining a
connection between these two different seasonal phenomena is a hard task since both features
occur over such a large time scale. The purpose of this paper is to see if there is a relationship
between the extent of ice cover experienced during the winter months and the frequency and
timing of severe storms in the following spring and summer months. For this paper, our focus
will be on Lake Michigan and the lower peninsula of Michigan.
2. Details and Methods
The winter months data was obtained from Great Lakes Environmental Research
Laboratory (GLERL). GLERL takes gridded satellite imagery from the National Ice Center or
Canadian Ice Service and computes daily and weekly averages of different weather variables.
The data that was used in this paper was Lake Michigan’s daily lake temperatures and the daily
lake ice concentration, which only extended back to 1992. Archived radar imagery for this region
extended back to 1995 after the installation of NEXRAD radars. Because of these data
limitations, we had to focus our research between the years of 1995 to 2014.

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Using Fig 2, we selected four years that experienced an extrema in ice cover from 1995
to 2014. The years 1996 and 2014 were selected because both experienced maxima in ice cover.
Conversely, 1998 and 2002 were selected because both experienced minima in ice cover. From
there, we gathered the daily averaged ice coverage data and the averaged surface lake
temperature for the four winters. This data was then plotted using R Console to show the daily
averages for each year segment as well as the averages for each piece of the data respectively. R
Console is an open source computer program used to make charts, graphs, and other statistics.
The averages were taken based on GLERL readily available data averages which were between
the years 1992 to 2014.
After the plots had been created, the main things that we were looking for were below
average temperatures for the four selected years, as well as the amount and duration of the ice
coverage. From this, we then attempted to find a relationship between the winter data and the
spring/summer data to find any sort of trend in the four years, as our premise stated.
The winter data that was selected by extrema in ice coverage did in fact show that in the
years with high ice coverage resulted in below average lake surface temperatures. Conversely,
the years with low ice coverage showed lake temperatures were above average in temperature.
This is what was expected based on the heat of fusion principle, meaning more ice will lead to
Fig 2. Annual Maximum Ice Cover for Lake Michigan from National Weather Service.

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colder temperatures, and vice versa. We noted that especially for our years of focus, the winter of
2013 to 2014, the temperature remained below average until September.
The spring/summer months data was obtained from the Storm Events Database, which is
part of the National Climatic Data Center (NCDC). These storm events are a compile of storm
reports that are received by the National Weather Service. The reports that were chosen are the
following: Tornado, Thunderstorm Wind, and Hail. These types of reports were chosen because
they were the only three that extended far back enough for our research purposes From herein the
three storm reports will be referred to as TWH.
To match the winter months data, the TWH storm reports from 1992 to 2014 was
exported from the database. This data set will be considered raw TWH storm reports. The raw
data set will include storm reports from Upper Michigan as well. Standard descriptive statistics
were calculated using this data set.
The raw storm reports can contain multiple reports from one storm. It became apparent
that the storm reports would need to be filtered in order to obtain a data set that can best
represent each individual storm. This was achieved by using University Corporation for
Atmospheric Research (UCAR) image archive website and NCDC’s Climate Data Online (CDO)
radar mapping tool to determine if a storm cell produced multiple reports. UCAR image archive
contains thirty-minutes interval of NEXRAD data. The CDO radar mapping tool presents
NEXRAD data in five minute intervals. The start of the filtering process began by removing the
counties located in Upper Michigan. This was done by searching for the counties and flagging
them with a unique color using one of the spreadsheet’s search tools. The website was set to the
time just before the storm report occurred. Then the radar imagery was used as a guide to find
the county the storm report occurred in on a Michigan county map. Once the storm cell was

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identified, move the radar imagery forward one time interval at a time, to see if that cell
produced other reports by looking at the TWH data set. The ways of classifying thunderstorms
are the following:
1. If a thunderstorm cell continually appears in the radar imagery then it requires one
TWH report (if applicable).
2. If a thunderstorm cell disappears from the radar imagery and reappears as a new
storm, then it is considered two independent thunderstorm cells, which means it can
have up to two TWH reports (if applicable).
3. Once two or more thunderstorms merged into a mesoscale convective system, it
would be considered two or more thunderstorms. This means that there can be two or
more TWH reports (if applicable) for this convective event.
4. Tornado storm reports were not filtered out because they occur at a shorter time scale.
Further classification of these four years was done in a simple spreadsheet program. They
were classified by cold and mild years, and by months.
3. Results and Discussion
The winter data was not used as much for results as it was used as a tool to find the
extreme periods of ice cover during 1995 to 2014. Both 2014 and 1996 were above average in
ice concentration and, 2002 and 1998 were below average (Fig 2). The plotted results for the four
selected years lake temperatures and 1992 to 2014 average lake temperatures can be found in Fig
3 (on the next page). Another result that made sense were the years with above average ice
concentration also had below average lake surface temperatures much into the following spring
and summer months (Fig 3). We used this data as guidance to determine which of the years to

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look at and, to select a few years that were extremely warmer or cooler in lake temperature than
average.
Fig 3. Average lake surface temperature of Lake Michigan plotted against the average from 1992 to 2014.
0 100 200 300
0510152025
Day of Year
Avg.SurfaceTemp.(*C)
Legend
2001
2002
1992-2014 Average
0 100 200 300
0510152025
Day of Year
Avg.SurfaceTemp.(*C)
Legend
2014
2013
1992-2014 Average
0 100 200 300
0510152025
Day of Year
Avg.SurfaceTemp.(*C)
Legend
1995
1996
1992-2014 Average
0 100 200 300
0510152025
Day of Year
Avg.SurfaceTemp.(*C)
Legend
1997
1998
1992-2014 Average

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The raw TWH storm reports can be found in Table 1. The four years of interest have been
highlighted with a blue background representing a colder winter and a red background
representing a milder winter. As mentioned earlier, this data set contains storm reports that
occurred in Upper Michigan.
Table 1. Raw Storm Reports for 1992 to 2014 from NCDC.
Raw Storm Reports
Year Hail Thunderstorm Wind Tornado Totals
1992 38 167 21 226
1993 48 85 4 137
1994 147 153 11 311
1995 63 246 11 320
1996 147 165 13 325
1997 138 285 19 442
1998 209 514 24 747
1999 99 344 13 456
2000 245 226 6 477
2001 163 322 42 527
2002 180 396 13 589
2003 267 359 14 640
2004 272 371 24 667
2005 135 404 5 544
2006 363 388 10 761
2007 279 602 27 908
2008 342 445 16 803
2009 63 144 3 210
2010 107 334 27 468
2011 261 457 18 736
2012 250 257 6 513
2013 133 395 14 542
2014 172 380 14 566
Descriptive statistical techniques were used on the raw storm reports data set. The
average and standard deviation of this data set were calculated and results can be found in Table
2 (on the next page). Comparing these results to the four years selected show that majority of the

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categories fall within one standard deviation. There are a few exceptions. The 1996 thunderstorm
wind reports are two standard deviations below the mean. Conversely, the 1998 thunderstorm
wind reports are two standard deviations above the mean. The total storm reports for 1998 were
two standard deviations above the mean.
Looking at this data, the raw reports in Table 1 by type and year and comparing the years
of 1996, 1998, 2002, and 2014 to the standard deviation row in Table 2, it can be seen that even
though these years had an above average severe storm occurrence, the deviation from the mean
was still at or below one standard deviation away from the mean. Specifically the years of 2002
and 2014 fall within less than half a standard deviation from the mean. This lack of significant
change or outlier points in the data suggests that there is no relationship in years with an extreme
high or low ice coverage extent and the amount of severe weather occurrences during the
following storm season.
However, this result was obtained with limited factors and limited data. Possibly if more
factors involved with the changing conditions of Lake Michigan could be taken into account,
there would be a possibility of a relationship in the changing conditions through other factors.
Factors that we would have liked to examining but we did not have the time or resources were
the upper air maps, synoptic patterns, snowmelt, effects of some of the other Great Lakes, etc.
These initial results show there is no significant relationship between years of extreme, or lack
thereof, ice cover with total number of severe weather storm reports.
Table 2. Statistic results from Table 1: Raw Storm Reports.
Hail Thunderstorm Wind Tornado Total
Average 179.17 323.43 15.43 518.04
Standard Deviation 90.28 125.78 8.93 197.43

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Further analysis on the selected four years was done using the methods laid out in the
previous section. This was done because the raw data set contains multiple reports of the same
storm, it also contains reports from Upper Michigan. The results from the radar analysis for the
four years were placed in Table 3. This table contains the storm reports by type and by
individual years. It also contains a grand total of the two different cases which is highlighted
with a colored background. From this table, there is a little variation in this data set. For the
colder winter case, one can see that there are little variations between the two years and the total
storm reports are very close to each other. For the milder winter case, the numbers were not as
similar as the previous case. 1998 had more tornado and hail reports than in 2002. This caused
1998 to have more total storm reports than 2002. Comparing the two cases, most of the storm
reports are very similar. The colder winter case experienced a little more hail reports than its
counterpart. The milder winter case had more thunderstorm wind reports and slightly more
tornado reports, which contributed to this case having more storm reports overall than the colder
winter case.
Additional classification was done on the radar analyzed storm report data set. The total
storm reports for each case were classified by months. The results were plotted into Figure 4 (on
the next page). There are a few takeaways from this figure. It appears that the storm reports start
earlier in the colder winter case compared to the milder case with the exception of July. The
milder winter starts later and lasts longer than the colder winter case. One can see that the
Table 3. Radar Analyzed storm reports for 1996, 1998, 2002, and 2014.
1996 2014 Total 1998 2002 Total
Hail 61 56 117 64 38 102
Thunderstorm Wind 45 55 100 70 75 145
Tornado 13 12 25 23 9 32
Totals 119 123 249 157 122 279

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0
10
20
30
40
50
60
70
80
StormReports by Month
Cold Winter
Mild Winter
maximum in storm reports for the milder winter case occurred near the beginning of the year in
June. The maximum in storm reports for the colder winter case begins later in July.
To explain why there is a shift of the storm reports for the cold winter cases towards the
beginning of the calendar year could be a result of the lake temperatures. Below average lake
temperatures in the early spring and warm land temperatures can led to a thermal gradient. This
gradient can led to pressure perturbations that can form a lake breeze. Lake breezes can enhance
convection and even initiate storm development (Lyons 1966 and King et al. 2003). The lake
temperatures have a similar affect for the milder cases as well. Above normal lake temperatures
during October to December are favorable for thunderstorms (Steiger et al. 2009). This is
because relatively warmer lake water than the surface can help destabilize the atmosphere
Fig 4. Analyzed Storm Reports for the two cases by months.

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(Steiger et al. 2009). Also, air masses around these months tend have more moisture content,
which favors storms to occur in these months than in later winter months (Steiger et al. 2009).
Knowing a possible shift in storm distribution could play a major role in educating the public on
when to prepare for severe weather.
4. Conclusions and Summary
In conclusion to our research, we found that the ice coverage of Lake Michigan in the
winter months, and the temperature of the lake following the extrema in ice coverage play no
significant role in changing the overall occurrence of severe storms in the following spring and
summers months (Table 1 and Table 2). The storm reports and severe weather followed an
average trend for the years selected. However, it can be seen in our four years of analysis that
there might be a shift in the timing of severe storms through the spring and summer months, with
the exception of July for the cold winter case (Fig 4). This shift could be better explained by the
lake temperatures rather than the ice coverage.
Through this research we found no relationship between ice cover extent and frequency
of storms. However, if more parameters could be factored into the analysis, a relationship may be
found. Parameters such as upper air maps, snowmelt, and synoptic scale patterns could be
considered in further research into the matter. The most interesting result is the possible shift in
the timing of the severe weather occurrences. This possible relationship warrants more research
with a focus on lake temperatures rather than ice coverage extent. Ice coverage can indirectly
affect lake temperatures but the influence it has on the storm season might not be reasonable.
Understanding this relationship could improve public awareness, as severe weather in Michigan
is not as common of an occurrence as it is in the Great Plains, so the impacts to the Michigan

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residents become greater. Therefore, if more years could have been analyzed it might have
yielded the same results and create a better weather prepared society in the Great Lakes region.

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References
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Michigan. Bull. Amer. Meteor. Soc., 51, 403-412.
Great Lakes Environmental Research Laboratory, cited 2015: About Our Great Lakes:
Introduction. [Available online at http://www.glerl.noaa.gov/pr/ourlakes/.]
Great Lakes Environmental Research Laboratory, cited 2015: Great Lakes Statistics.
[Available online at http://coastwatch.glerl.noaa.gov/statistic/statistic.html]
King, P. W., M. J. Leduc, D. M. Sills, N. R. Donaldson, D. R. Hudak, P. Joe, and B. P. Murphy,
2003: Lake breezes in southern Ontario and their relation to tornado climatology. Wea.
forecasting, 18, 795-807.
Lyons, W. A., 1966: Some effects of Lake Michigan upon squall lines and summertime
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Moroz, W. J., 1967: A lake breeze on the eastern shore of Lake Michigan: observations and
model. J. Atmos. Sci., 24, 337-355.
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of the Laurentian Great Lakes on Regional Climate*. J. Climate, 26, 789-804.
Steiger, S. M., R. Hamilton, J. Keeler, and R. E. Orville, 2009: Lake-effect thunderstorms in
the lower Great Lakes. J. Appl. Meteor. Climatol., 48, 889-902.