Atlantic TS Epsilon Is About a 1 in 80,000 Year Event!!!!!

Tropical storm Epsilon, number 29, is now active in the Atlantic:

http://www.wxforecasts.com/ameriwx/a...age&hwvmetric=

http://www.wxforecasts.com/ameriwx/a...ems&hwvmetric=

If one controls for the increasing capability to observe
tropical storms over the last 126 Atlantic hurricane seasons,
this year's 29 Atlantic storms are about a 1 in 80,000 event!

If one ignores the 4 storms per year per century growth in
counted storms, this year's 29 storms is a 1 in 1,700,000
event. I won't use this, because it is misleading. I'll
leave the lying and exaggeration to the Carbon industry
lobbyists. They are the experts at that.

Storms
[1] 11 7 6 4 4 8 12 19 9 9 4 11 9 12 7
[16] 6 7 6 11 9 7 12 5 10 5 5 11 5 10 11
[31] 5 6 7 6 1 5 14 3 5 3 4 6 4 7 8
[46] 2 11 7 6 3 2 9 11 21 11 6 16 9 8 5
[61] 8 6 10 10 11 11 6 9 9 13 13 10 7 14 11
[76] 12 8 8 10 11 7 11 5 9 12 6 11 8 8 15
[91] 10 13 7 8 11 9 10 6 12 9 11 12 6 4 13
[106] 11 6 7 12 11 14 8 7 8 7 19 13 8 14 12
[121] 15 15 12 16 15 29

The data are from the NOAA web page given at this URL:
http://www.aoml.noaa.gov/hrd/hurdat/...arandStorm.htm
Currently, these data are preliminary and dated Friday,
July 15, 2005 9:00PM.

sort(Storms)
[1] 1 2 2 3 3 3 4 4 4 4 4 4 5 5 5
[16] 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6
[31] 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7
[46] 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8
[61] 9 9 9 9 9 9 9 9 9 9 9 10 10 10 10
[76] 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11
[91] 11 11 11 11 11 11 11 12 12 12 12 12 12 12 12
[106] 12 12 13 13 13 13 13 14 14 14 14 15 15 15 15
[121] 16 16 19 19 21 29

Storms[126]
[1] 29
mean(Storms)
[1] 9.142857
sd(Storms)
[1] 4.086493

fitted.model

Call:
lm(formula = Storms ~ Year, data = aframe)

Coefficients:
(Intercept) Year
-70.80045 0.04115


residuals(fitted.model)[126]
126
17.28496

residuals(fitted.model)[126]/sd(Storms)
126
4.229779

pnorm(residuals(fitted.model)[126]/sd(Storms))
126
0.9999883

# Yes, except for this year's extreme and a few others,
# the residuals of the "Storms" data are normally
# distributed. A normal quartile-quartile plot shows a
# straight line, confirming this.

1/(1-pnorm(residuals(fitted.model)[126]/sd(Storms)))
126
85498.98
# Which rounds to 1 in 80,000.

# NOTE:
# to remove any bias due to increasing capability to observe
# hurricanes, I have used the current value of the trendline
# and not the mean as a baseline. Thus, the call to the
# residual here. Had I used the mean as a base line the
# result would have been 1 in 1,700,000. I'll leave the
# lying and exaggeration to the fossil fools.

pnorm((Storms[126]-mean(Storms))/sd(Storms))
[1] 0.9999994
1/(1-pnorm((Storms[126]-mean(Storms))/sd(Storms)))
[1] 1697027