Global Hurricane Hours

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Climate alarmists claim that hurricane frequency is increasing. I have already dealt with this for the Atlantic, here. There was no trend. Today, I will analyze global data.

NOAA sponsors the largest collection of historic hurricane data: IBTrACS (International Best Track Archive for Climate Stewardship). There is 13,545 storms archived going back to 1842, from a total of 14 agencies. My focus will only be on hurricanes, that is: storms that at some time achieved wind speeds at or above 64 knots. Also known as Category 1 or greater.

It is definitely true that the number of detected hurricanes has increased. This is due to better sensing technology, such as aboard: radiosondes (1930s), regular transoceanic air travel (1940s), satellites (late 1960s). Due to limitations of older data, it doesn’t make any sense to consider global data before 1950.

Aside from detection, there is also a matter of how one counts the frequency of hurricanes. Does it make sense to count a 6-hour Category 3 storm the same as a 42-hour Category 3 storm?

Both classified as Category 3, but Cat3 status unequal in time.

No, it’s doesn’t make sense. Such a thing would be misleading. But that is what climate alarmists do.

A better thing to do would be to count hours spend in certain wind speed categories:

And this is exactly what I did. Here are the results:

10yr CMA means 10-Year Centered Moving Average.

What does the best hurricane data in the world show?

Category 5 has decreased

Category 4 is cyclic/no-trend

Category 3 had increased, but dropping last 25 years

Category 2 had increased, but dropping last 25 years

Category 1 is cyclic/no-trend

Now we go to category hybrids:

Category 1 & 2
Categories 3,4 & 5

Yeah, pretty much cyclic/no trend.

Category 1,2,3,4,5 is the first image in this post. No trend!

Here are stats for top 10 years for each of our categories:

Cat 1 Hours
1972 :  5187
1992 :  4533
1997 :  4230
1996 :  4149
1990 :  4059
1971 :  4047
1964 :  3924
1968 :  3711
1989 :  3564
1986 :  3402
Cat 2 Hours
1992 :  2670
1997 :  2289
2015 :  2184
2004 :  2082
1972 :  2022
2005 :  1863
1994 :  1833
1996 :  1818
2018 :  1800
1991 :  1743
Cat 3 Hours
2015 :  1752
1997 :  1692
2018 :  1686
1992 :  1650
2004 :  1569
2005 :  1434
1972 :  1416
2019 :  1359
1994 :  1344
1991 :  1311
Cat 4 Hours
2004 :  1194
2015 :  1122
2018 :  1086
1997 :  1032
1992 :  1014
2005 :   978
1996 :   966
1994 :   909
2003 :   894
2014 :   885
Cat 5 Hours
1959 :   528
1997 :   462
1958 :   408
1957 :   330
2018 :   324
1961 :   312
1965 :   291
1954 :   267
2004 :   234
1962 :   228
Cat 1+2 Hours
1972 :  6201
1992 :  5739
1997 :  5412
1996 :  5031
1990 :  4842
1971 :  4767
1964 :  4599
2015 :  4542
1968 :  4359
1994 :  4320
Cat 3+4+5 Hours
1997 :  2466
2004 :  2379
1992 :  2295
2015 :  2268
2018 :  2187
2005 :  2007
1994 :  1866
1961 :  1842
1996 :  1797
2003 :  1710
Cat 1+2+3+4+5 Hours
1972 :  6606
1992 :  6489
1997 :  6123
2015 :  5799
1990 :  5613
1996 :  5544
1971 :  5424
1964 :  5373
2018 :  5346
2004 :  5307

Climate alarmists claim that greenhouse gases create more energy for hurricanes. Well, where is that extra energy for hurricanes?

Take care, -Zoe

Update

I added more categories.

Storm hours over 20 knots (23mph, 37 km/h)

There is a decrease in hours spend above 20 knots in the satellite era.

Storm hours over 5 knots (5.8 mph, 9.3 km/h)

Also a decrease in hours spend above 5 knots. This is not even a breeze.

So, I ask again: where is the extra storm energy from carbon dioxide?

Code

# Zoe Phin, 2021/06/16
# File: ibtracs.sh
# Run: source ibtracs.sh; require; download; hurrs; stats; plotall

require() { sudo apt-get install nco gnuplot; }

download() { wget -c "https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/netcdf/IBTrACS.ALL.v04r00.nc"; }

view="ncks IBTrACS.ALL.v04r00.nc --trd -HC -v" 

filter() {	# Speed Filter: $1 - Min, $2 - Max
    tr -d ' ' | tr _ 0 | awk -vm=$1 -vM=$2 -F '[]=[]' '$8>=m && $8<=M {print $2"."$4}'
}

winds() { # All data sources
    $view bom_wind        | filter $1 $2 > .w01
    $view cma_wind        | filter $1 $2 > .w02
    $view ds824_wind      | filter $1 $2 > .w03
    $view hko_wind        | filter $1 $2 > .w04
    $view mlc_wind        | filter $1 $2 > .w05
    $view nadi_wind       | filter $1 $2 > .w06
    $view neumann_wind    | filter $1 $2 > .w07
    $view newdelhi_wind   | filter $1 $2 > .w08
    $view reunion_wind    | filter $1 $2 > .w09
    $view td9635_wind     | filter $1 $2 > .w10
    $view tokyo_wind      | filter $1 $2 > .w11
    $view usa_wind        | filter $1 $2 > .w12
    $view wellington_wind | filter $1 $2 > .w13
    $view wmo_wind		  | filter $1 $2 > .w14
}

yhours() { 
    winds $1 $2
    sort -nu .w* | awk -F. '{print $1}' | uniq -c | awk '{print $2" "$1}' > storm.obs
    sed -f storm.year storm.obs | awk '{Y[$1]+=$2} END { for (y in Y) print y" "Y[y]*3}'
}

hurrs() {
    $view season | tr -d ' ' | awk -F '[]=[]' '{print "s/^"$4" / "$6" /"}' | sed \$d > storm.year
    yhours 137 999 > cat5.hours
    yhours 113 136 > cat4.hours
    yhours  96 112 > cat3.hours
    yhours  83  95 > cat2.hours
    yhours  64  82 > cat1.hours

    yhours  64  96 > cat12.hours
    yhours  96 999 > cat345.hours
    yhours  64 999 > cat12345.hours
}

stats() {
    for c in 1 2 3 4 5 12 345 12345 ; do
        f="cat$c.hours"
        sort -rnk2.1 $f | awk -vc=$c 'BEGIN{ 
            print "Cat "c" Hours" } 
    NR<11 { printf "%s : %5s\n",$1,$2 }' 
    done
}

cma() {
    cut -c6- | tr '\n' ' ' | awk -vp=$1 '{
    for (i=0;i<p/2;i++) print ""
    for (i=p/2;i<=NF-p/2;i++) { s=0
        for (j=i-p/2; j<=i+p/2; j++) s+=$j/(p+1)
        printf "%.4f\n", s
    }}'
}

plot() {
    awk '$1>=1950{print $0}' $1 | tee .dat | cma 10 > .cma
    paste -d ' ' .dat .cma > .plt
    c=$(echo -n $1 | tr -cd 12345)
    echo "set term png size 740,480; set mytics 5
    set key outside top left horizontal
    set grid; set xrange [1950:2020]
    plot '.plt' u 1:2 t 'Category $c Hours' w l lt 3 lw 2,'' u 1:3 t '10yr CMA' w l lt 6 lw 4
    " | gnuplot > c$c.png
}

plotall() { for c in 1 2 3 4 5 12 345 12345 ; do plot "cat$c.hours"; done; }

Published by Zoe Phin

https://phzoe.com

13 thoughts on “Global Hurricane Hours

  1. Interesting way to analyze the data, Zoe. Gotta love someone like you who actually goes to the sources, does the hard yards to analyze the data, and presents it in a clear explanatory manner.

    Best to you and yours,

    w.

    Liked by 1 person

  2. Thank you Zoe, very interesting, and very well done. I will share your analysis on my Linkedin network…as I also did with many others.

    Liked by 1 person

  3. Excellent and logical analysis. I’ve thought exactly among those lines while asking why don’t they categorize solar flares using the same approach. What creates more disruption to the ionosphere, an hour long M-class solar flare, or a 2 minute long X-class flare? Given the increased amount of attention that’s being paid to the sun (insofar as how it impacts the climate) this approach should be of interest. It’s the sun! It’s NOT CO2.

    Liked by 1 person

  4. Excluding pre-satellite data before the 1970s, what I see in the charts are random variations.
    Non-linear data, with no reason to calculate a linear trend line.
    I see no correlation with CO2 levels, or the global average temperature.

    On the other hand, hurricanes are bad news.
    CO2 is the cause of all bad news in the world !
    The world will end in 10 years from climate change.
    Because the science is settled, with a 102% confidence level.
    (95% confidence is for losers)

    Liked by 1 person

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