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About the hurricane data:

    A tropical cyclone is a low-pressure system over tropical or sub-tropical waters with thunderstorm activity and surface wind circulation.  Tropical cyclones with maximum sustained surface winds of less than 17 m/s (39 mph) are called "tropical depressions".  Once the tropical cyclone reaches winds of at least 17 m/s they are typically called a "tropical storm" and assigned a name.  If winds reach 33 m/s (74 mph), then they are called: a "hurricane" or "typhoon", depending on the geographic location.  For the Atlantic basin, USA meteorologists use a scale of 1 (low) to 5 (high) to rate the intensity of hurricanes.

       A hurricane generates an incredible amount of kinetic or wind energy.  With a radius of 60 km and an average wind speed of 40m/3, a hurricane can produce 1.5 x 1012 Watts of energy -- equivalent to about half the worldwide electrical generating capacity!  Hurricane wind energy of this magnitude can cause enormous property damage directly as a result of the wind, or indirectly from ocean waves, tornadoes, flooding, and storm surge.  Human suffering and loss of life is also caused by wind energy (direct and indirect) but often is the result of outbreaks of disease long after the hurricane has passed.  The 1926 hurricane in Southeast Florida and Alabama has been estimated to be the most costly in US history -- $83,814,000,000 in today's dollars, while the 1900 hurricane that hit Galveston, TX was the deadliest -- over 8000 people killed.

    There is considerable debate over the extent to which the severity and frequency of hurricanes will change with global warming because the formation of tropical cyclones depends on a number of ocean and atmospheric variables, including sea surface temperature and air moisture.  But with increased sea levels associated with global warming (coupled with the continued worldwide migration of people to coastal cities) there is growing concern that hurricanes will cause more property damage and loss of life in the future.  An interesting activity for the student would be to examine these data and look for trends in hurricane frequency over two climate warming periods, say 1880-1940 and 1940-2000.

    These hurricane data were pulled from the Atlantic Tracks File compiled by the US National Hurricane Center.  This file consists of loads of data for major storm events from 1851 to 2000.  Tropical depression and tropical storm data were omitted from the file, as were all other storm statistics.  Each year the hurricane season begins June 1, and all hurricane dates are converted to "weeks of season." The weeks were then binned in 2-week intervals beginning June 1-14 (weeks 1 and 2) and ending in early January (weeks 31 and 32). Hurricanes occurring after week 32 (four of them) were considered outliers, and left off the histogram. We see from the graph that there is a major hurricane season that begins to form in early August (week 9), peaks during late August and early September (weeks 13 and 14), and substantially diminishes by late October (weeks 21 and 22).

     Note that the graph is nearly symmetric and bell-shaped; a student will find it interesting to approximate the graph with a normal distribution.  What is the mean and standard deviation for the data represented in the graph, and how can these numbers be interpreted?  Can the mean and standard deviation be approximated from the graph?  How would effects of global warming (as mentioned above) change the shape of the graph, or would the shape remain the same?  At the National Hurricane Center's website, there is also available what is called the Eastern North Pacific Tracks File.  For the student who has some skill in managing larger data sets (Excel may work), analyzing the hurricane season of the Pacific would make an excellent student project.

Variables:

hurricane

date

week_of_season

Link To Google Sheets:

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License Type:

References/Notes/Attributions:

Source:  US National Hurricane Center   http://www.nhc.noaa.gov/

https://seattlecentral.edu/qelp/sets/070/070.html

R Dataset Upload:

Use the following R code to directly access this dataset in R.

d <- read.csv("https://www.key2stats.com/US_Atlantic_Hurricanes__1851-2000_1700.csv")

R Coding Interface:


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