The analysis is easy: copy and paste the data from that link into a new text file, then write a python script that goes through it and counts the number of Cat 1, 2, 3, 4 & 5 hurricanes that make landfall per year (the "Highest Saffir-Simpson U.S. Category" column), and then make the plots: I used gnuplot. You can then do fits to the data if you'd like, but the flat trend lines over the last 175 years are obvious.
I encourage you to not trust me and to do it yourself, but I'm also happy to share my script, let me know.
As far as the hurricane trajectory trend lines go, they are clearly highly stochastic: check out e.g. both the spaghetti plot predictions for various storms from previous years, and ask google for a map of where they grow (grew...) oranges in Florida.
https://www.aoml.noaa.gov/hrd/hurdat/All_U.S._Hurricanes.htm...
The analysis is easy: copy and paste the data from that link into a new text file, then write a python script that goes through it and counts the number of Cat 1, 2, 3, 4 & 5 hurricanes that make landfall per year (the "Highest Saffir-Simpson U.S. Category" column), and then make the plots: I used gnuplot. You can then do fits to the data if you'd like, but the flat trend lines over the last 175 years are obvious.
I encourage you to not trust me and to do it yourself, but I'm also happy to share my script, let me know.
As far as the hurricane trajectory trend lines go, they are clearly highly stochastic: check out e.g. both the spaghetti plot predictions for various storms from previous years, and ask google for a map of where they grow (grew...) oranges in Florida.