Early open access of the new paper is here.

Abstract

Every year, tropical hurricanes affect North and Central American wildlife and people. The ability to forecast hurricanes is essential in order to minimize the risks and vulnerabilities in North and Central America. Machine learning is a newly tool that has been applied to make predictions about different phenomena. We present an original framework utilizing Machine Learning with the purpose of developing models that give insights into the complex relationship between the land–atmosphere–ocean system and tropical hurricanes. We study the activity variations in each Atlantic hurricane category as tabulated and classified by NOAA from 1950 to 2021. By applying wavelet analysis, we find that category 2–4 hurricanes formed during the positive phase of the quasi-quinquennial oscillation. In addition, our wavelet analyses show that super Atlantic hurricanes of category 5 strength were formed only during the positive phase of the decadal oscillation. The patterns obtained for each Atlantic hurricane category, clustered historical hurricane records in high and null tropical hurricane activity seasons. Using the observational patterns obtained by wavelet analysis, we created a long-term probabilistic Bayesian Machine Learning forecast for each of the Atlantic hurricane categories. Our results imply that if all such natural activity patterns and the tendencies for Atlantic hurricanes continue and persist, the next groups of hurricanes over the Atlantic basin will begin between 2023 ± 1 and 2025 ± 1, 2023 ± 1 and 2025 ± 1, 2025 ± 1 and 2028 ± 1, 2026 ± 2 and 2031 ± 3, for hurricane strength categories 2 to 5, respectively. Our results further point out that in the case of the super hurricanes of the Atlantic of category 5, they develop in five geographic areas with hot deep waters that are rather very well defined: (I) the east coast of the United States, (II) the Northeast of Mexico, (III) the Caribbean Sea, (IV) the Central American coast, and (V) the north of the Greater Antilles.

Here is the email from Dr. Willie Soon about the paper.

Dear Friends and Colleagues,

My good friend and able colleague Victor Velasco Herrera and I
are happy to alert you to this new paper:

https://www.mdpi.com/2073-4433/13/5/707

Predicting Atlantic Hurricanes Using Machine Learning

Our new way of looking and analyzing the Atlantic Hurricane data permits us
to offer some interesting preview of the future activity:

“Our forecast is that no category 5 hurricanes will be formed in the Atlantic until the next
active decadal oscillation phase around 2026±2 to 2031±3. For category 4 hurricanes,
there could still be at least one event between 2022 and 2023.
The next active category 4 hurricanes will begin in 2025±1 and end 2028±1.
For category 3, it is expected that there could still be one event in 2022
while the next activity phase will be around 2023±1 and 2025±1 where
one can expect 1 to 4 category 3 Atlantic hurricanes.
Finally, for category 2 hurricanes, our Bayesian Machine Learning model
hindcasted correctly the highly active episodes around the 1990s and early 21st century
(clusters X-XIII shown in Figure 8). The next active phase of category 2 hurricanes will
be around 2023±1 and 2025±1.”

Obviously, our unusual way of looking at the data almost deemed our paper unpublishable
and hence rejected by the referees. Luckily we were able to explain and prevail with the
referees of this journal. Of course there are obvious limitations in our works in that we
cannot offer a forecast of the intensity nor the track of any future hurricanes.

Please help spread this paper widely if you find the work meaningful.

Cordially,

Victor and Willie (and all co-authors)

https://www.ceres-science.com/