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CAMELOT CLIMATE INDEX
In this1960's musical King Arthur professes that Camelot has a perfect climate all the
year; and by royal decree at that! But actually an "ideal" climate is extremely subjective, with one person's idea of perfection being met with disdain by others. Some individuals may want warm beach weather all year round, while four distinct seasons are most desirable for others. What follows is just one person's (the author's) idea that an ideal climate is sunny and relatively mild with few extremes in temperature, humidity or precipitation.
Dataset
Data for this project was extracted from the
Comparative Climatic Data for the United States (NCDC, 1998). Nine different weather elements were used in constructing
an index of ideal weather; a Camelot Climate Index (CCI). These parameters
are: maximum temperatures, minimum temperatures, mean number of days with
minimum temperatures less than 32º F, mean number of days with maximum
temperatures greater than 90º F, mean annual rainfall, mean number of days with
precipitation, mean annual snowfall, average percent of sunshine, and average relative humidity.
The average
monthly maximum temperature and corresponding afternoon relative humidity
were combined to derive a monthly temperature humidity index (THI) which
is a measure of "discomfort".
The initial Comparative Climatic Data (CCC) analyzed
has approximately 300 locations from all fifty states, Puerto Rico and
10 Pacific island stations. This represents ninety major urban
areas by stations within thirty miles. Unfortunately, not all of the
meteorological parameters are available for all of the stations. In
particular, some stations do not regularly record relative humidity and sunshine. The most limiting factor was the lack of data for the
percent of possible sunshine data, with only 158 stations having all the
requisite parameters.
Maximum Temperature
Because of the strong relationship between
discomfort due to high temperature and high humidity, it was decided
combine these factors using the temperature-humidity index (THI) using THI
= Td-(0.55-0.55RH)(Td-58) where Td is the dry bulb temperature and RH is
the relative humidity. A THI value was calculated for each month using the
average maximum temperature and the average afternoon relative humidity.
If the monthly THI was less than 75 degrees, then a value of 0 was
assigned to the month. This threshold was chosen because statistically at
least half of people feel uncomfortable with a THI greater than 75. If the
THI was between 75 and 85 it was given a value of 1, between 85 and 95 a
2, between 95 and 105 a 3 and if it was 105 or greater a value of 4 was
assigned to that month. The assigned monthly values were then summed to
get an annual Temperature Humidity Index (THI). Additionally, the average
annual number of days when the maximum was above 90 degrees (MAX90).
Minimum Temperature
The average monthly minimum
temperatures were handled similarly. If the monthly minimum (MIN) was
greater than 45 degrees, then a value of 0 was assigned to the month. If
the monthly minimum was between 45 and 35 it was given a 1, between 35 and
25 a 2, between 25 and 15 a 3 and if it was 15 or less a value of 4 was
assigned to that month. The assigned monthly values were then summed to
get an annual minimum value (MIN). Data was also used for the average
annual number of days when the minimum was below 32 degrees (MIN32).
Precipitation Data
The precipitation data set
included several types of data. The annual average rainfall (RAIN) for
each station was the primary data. Also the average number of days when
measurable rain (i.e., greater than or equal to .01 inch) fell was used
(RAIN01). The final precipitation parameter was the annual average
snowfall expressed inches (SNOW).
Sunshine Data
The final data subset used was the
annual percentage of possible sunshine (SUN). These values are derived by
calculating the total time that sunshine reaches the station as a
percentage of the maximum amount of possible from sunrise to sunset.
Index Calculation
Equal weight was given
to each of the data categories in calculating the Camelot Climate Index (CCI).
Thus the combination of maximum temperature, minimum temperature,
precipitation and sunshine each account for 25% of the total index. Within
the maximum temperature category, The THI was weighted as 10% and MAX90 as
15% of the total CCI. The minimum temperature group was weighted with
MIN32 as 20% and MIN as 5%. In the precipitation group RAIN was weighted
as 15%, RAIN32 as 5% and SNOW as 5%.
The maximum value for each category was used to
set the "best possible" for that category. This was divided by
the weight given that parameter to derive an overall rating factor. For
example: the highest value of MAX90 was 170 days (at Yuma, AZ), and MAX90
has a weight of 15%, giving a weighting factor of 11.3, which is expressed
as MAX90wf. The only exception to this was with the sunshine data, where
the "ideal" condition would be 100% sunshine, thus the percent
of sunshine received is subtracted from 100 initially, then divided by the
given weight.
The total CCI for each station was derived by subtracting the weighted value for each parameter from a value of 100, which would be "perfect" weather. It takes the form:
CCI=100-(THI/THIwf)-(MAX90/MAX90wf)-(MIN/MINwf)-
(MIN32/MIN32wf)-(RAIN/RAINwf)-(RAIN01/RAIN01wf)-
(SNOW/SNOWwf)-((100-SUN)/SUNwf)
CCI Map
The accompanying iso-analysis (isocams?) is rudimentary at best given the
limited dataset. Especially given the fact that no effort was made
to draw for topography and there are undoubtedly areas, mostly in and
around mountainous terrain, where an intermediate point would vary greatly
from the rather crude analysis.
Conclusions
A project like this sort is by nature extremely subjective, as everyone's "ideal" climate is different. Even by quantifying a
relatively small climatic dataset, numerous subjective choices had to be
made, including which climatic factors to choose and how much weight to give to each.
And remember the primary assumptions for this
study are based upon a 1960's musical about a mythical land with a mild dry
"perfect" climate as expressed by mythical King Arthur! Consequently,
cities with dry, mild and temperate climates ended up at the top of the Camelot Climate Indices.
If a similar project was done by a storm chaser it might be called the Oz
Climate Index with a bias towards the number of thunderstorm days, hail
and tornadoes.
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