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WPAC, Indian Ocean, and Southern Hemisphere Tropical Cyclones


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On 10/18/2026 at 1:40 AM, WxWatcher007 said:

Narrowly escaped a big time hit.

Sometimes the tightness/RI *saves* and area from a bigger hit.  Melissa over Jamaica was similar.  If Melissa had been a long-tracked Cabo Verde, it would have been much larger when striking Jamaica.  Sure, it may not have been as intense since multiple ERCs would preclude its sub-900 mb intensity, but a large Cat 4 impacting the entire island vs. only 1/3 of the islands getting Cat 3+ winds is not the same.  Also, the wrn 1/3 of Jamaica is significantly less populated, and one of its main airports went largely unscathed, and the other being on the N coast on the wrn side of the island had sig less impact than if it were on the S side.

Compare to Gilbert at Cat 4 in Sep 1988 that cut down the full length of the Jamaica E-W w/ the entire land devastated.  80% of home lost their roofs.

At least w/ Melissa on about 1/2 of the island, one could find relief, relatively speaking, short-term before full aid arrived and was distributed.

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Incredible model battle here. I have a feeling I know which one is going to win 
Screenshot_20261019-121922_Chrome.thumb.png.1e377b8bed3c6a96b3fc43a3a03718c7.png

I am assuming that the AI models use historical tracks and climatology more so then the physics models. From my understanding of AI. Not your typical analogs but everything that’s possible. If anyone has more information on the subject I would love to hear it.


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27 minutes ago, LongBeachSurfFreak said:


I am assuming that the AI models use historical tracks and climatology more so then the physics models. From my understanding of AI. Not your typical analogs but everything that’s possible. If anyone has more information on the subject I would love to hear it.


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Yeah it's mostly machine learning data correlations and such as far as I've heard, but I'm not sure that the specifics are really well understood by the weather community anyway. Actually there was a little segment about the deep mind and Isiais on NPR this morning and they were saying how mostly the project is worked on by non-meteorologist, not even programers with meteorology experience,  mostly neural network engineers

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2 hours ago, olafminesaw said:

Yeah it's mostly machine learning data correlations and such as far as I've heard, but I'm not sure that the specifics are really well understood by the weather community anyway. Actually there was a little segment about the deep mind and Isiais on NPR this morning and they were saying how mostly the project is worked on by non-meteorologist, not even programers with meteorology experience,  mostly neural network engineers

People are now talking about how Google Deep-mind ensembles beat the other ones on Hurricane Isaias.

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On 10/19/2026 at 2:20 PM, LongBeachSurfFreak said:


I am assuming that the AI models use historical tracks and climatology more so then the physics models. From my understanding of AI. Not your typical analogs but everything that’s possible. If anyone has more information on the subject I would love to hear it.


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Huge fail for AI models. Champi just made landfall on Taiwan 

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On 10/23/2026 at 1:32 PM, NorthHillsWx said:

Huge fail for AI models. Champi just made landfall on Taiwan 

Yes, so this is a lesson about AI models.  They are not infallible and can be off quite a bit.  Just I see some putting them up on pedestal and worshiping as to having the final say.

And look at what a physics-bases model (ECMWF ENS) did.  It even caught the odd motion from terrain influences so common for when a TY approaches Tawain!
 

ecmens.png

Champi_2026_path.png

Screenshot_20261019-121922_Chrome.png.81f4a1b2e2836f2e80950d61676a7ba6.png

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On 10/20/2026 at 3:40 PM, LongBeachSurfFreak said:

Based on old recon finding lots of sub 900 storms in that basin and this having that very high end look it likely was below 890.


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Odds are high that there have been at least 6 other STYs stronger than Tip.  This short paper from 2004 notes 2, and there have been several since (Haiyan comes to mind).

 

stronger.pdf

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On 10/20/2026 at 1:24 PM, WxWatcher007 said:

 

That structure is likely an artifact from precip in this case.  Other analyses showed Champi had completed an ERC earlier and there was no concentric eyewalls or more than one wind max.

 

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On 10/19/2026 at 5:50 PM, Chinook said:

People are now talking about how Google Deep-mind ensembles beat the other ones on Hurricane Isaias.

I find this a bit misleading in the larger pix.  Of course a newer model is going to be pushed and pointed out more often -- b/c it is new!  Makes for better news stories.  Also, beware of cherry-picking.  For any given fcst, you often can find a model (AI or otherwise) that did much better than the others.  Par for the course. Just is it pointed out a lot in the MSM or not? 

It's like the ECMWF praise that was big after Sandy.  Sure, it nailed far out a mega event, but the ECMWF overall skill perception was somewhat tainted by this very fact.  It did not mean that the ECMWF was the GOAT and panacea for all out fcst woes going forward!  I recall ppl using the ECMWF blindly for fcst solutions b/c it was the ECMWF!  That's not good fcst science.

One needs to see stats for all fcsts for the model, not just one that happens to get a lot of attention and had high sensible wx impact! 

That does not mean GDMI is useless or should not be praised, but let's not get carried away.

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On 10/19/2026 at 2:54 PM, olafminesaw said:

Yeah it's mostly machine learning data correlations and such as far as I've heard, but I'm not sure that the specifics are really well understood by the weather community anyway. Actually there was a little segment about the deep mind and Isiais on NPR this morning and they were saying how mostly the project is worked on by non-meteorologist, not even programers with meteorology experience,  mostly neural network engineers

"were saying how mostly the project is worked on by non-meteorologist, not even programmers with meteorology experience,  mostly neural network engineers."
 

That's a big caveat, and something that I think is lost in the translation often.

Example in the general pix.

One can educate themselves about the wx and claim, "I've observed and experienced all sorts of wx all my life!"  But that does not make one an expert on the subject by default.  It would be like saying, "I've known and driven cars most of my life!" but that does not make one an expert on cars!

Knowing is not enough.  You need to know how to *apply* the knowledge w/ insight and wisdom only acquired by deep interest and study of a given subject.

Having a formal education is key b/c if provides a lot of the basics and shows how other sciences connect well w/ meteorology, as an example.  You then take that formal education, and then apply it in job in the field, gaining experience and countless tidbits over time that round out thing nicely.  You also learn all the slang/jargon and the little nuances/idiosyncrasies.  The fine details count!  Stuff that you can often use to call out posers/hustlers/AI-generated content from the real deal! :)

Don't get me wrong, to be a wx expert, you do not need a full 4-year degree.  The Internet changes all that and if you put your mind to it and have the passion, you can get all the info ever needed from the web!  Plus, forums like this you get the details and nuances that round things out nicely, as I mentioned earlier.

So circling around back to the main topic at hand, when people that are not experts in a given field do work on products/services for the same field, there are caveats, as I said.  Programming a model but not understanding exactly what the model does for practical application is non-trivial, and can lead to things said about the model from such programmers not in-line w/ the fcst meteorology and its limits!  They can get idealistic w/o regard to practically. AI models are still bound by fcst chaos theory and in their case, the number of historical cases they drawn upon, so that becomes an issue when you get rare or outlier events.


"but I'm not sure that the specifics are really well understood by the weather community anyway."

Good point.  I think most only understand the basics from a bird's eye view., including myself.  It is a learning curve, as w/ all new tech.  Unfortunately, some put the wagon in front of the horse, getting way ahead of themselves simply for content (hype) or shill purposes <_<

AI as a concept is has long been ingrained in out culture from movies like The Terminator and The Matrix, making us wary, cautions, and fearful, but that's only Hollywood!  That does not mean it is reality or how things actually work!
 

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