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Wintry January 23rd-27th 2026

Seems like it would be really hard for models to trend stronger with the SE ridge here. You have a perfectly placed block with a polar vortex trapped directly in between it and the southern Atlantic coast. If anything, I’m shocked there can be as much ridging as what’s being shown?

Doesn’t this usually trend towards less ridging, when you have the the players lined up like this?
I would just say that even if you have the good players on the field - retrograding Greenland block, low anomaly underneath, etc, it still comes down to the specific details on where those features are located, how they evolve, and how they interact with the storm wave moving in from the SW. A lot of the good storms that hit the Mid-Atlantic and the Northeast also have these features of course, there're just a little farther north when those areas get hit good compared to what we require
 
How reliable is the google model? It have a good track record?
Statistically, it is the best model around. That is based on validation that spans multiple years (2022 to present).

It was stellar during hurricane season (even, surprisingly, with cyclone intensity!).

How well it does with specific situations is TBD. It is experimental and not publicly available.
 
really interesting that this ai model displayed the exact same lurch upward in qpf the ai ero ens showed over the last two model cycles
They initialize with the same initial conditions, have the same training data (ERA5 since 1980), and very similar model architectures (CRPS-optimized GNN-Transformer ensemble, difference is AIFS-ENS injects high dimensional noise across the model while WeatherNext2 injects low dimensional noise across the model.. WxNext2 noise injection is the better method in testing)
 
They initialize with the same initial conditions, have the same training data (ERA5 since 1980), and very similar model architectures (CRPS-optimized GNN-Transformer ensemble, difference is AIFS-ENS injects high dimensional noise across the model while WeatherNext2 injects low dimensional noise across the model.. WxNext2 noise injection is the better method in testing)
ok makes total sense. really enjoyed picking your brain about these things last few days
 
Valid question IMHO. Assuming 10:1 ratios (as shown) - given the cold projected, is 15:1 or even 20:1 possible? My guess is its too early to know?
From one of my friends in AmericanWX:

"There can be periods of high ratio snow most commonly on the backside or an upper level low pass but the front side is usually 10:1 or less. It's humid here and that matters. Getting pristine snow growth for long durations is also hard for multiple reasons. We always get periods of plates and mangled flakes even when it's a clean all snow event.
IMO, sticking to the basic 10:1 ratio when the ground and column is cold is the most accurate."
 
Valid question IMHO. Assuming 10:1 ratios (as shown) - given the cold projected, is 15:1 or even 20:1 possible? My guess is its too early to know?
I pressed a random point in North Carolina on OpenSnow (company I work for) and it has ratios around 14:1 to 15:1. We have a very strong algorithm for calculating snow ratio. 1768791395654.png
 
ok makes total sense. really enjoyed picking your brain about these things last few days
Ask away. Love explaining these things as many/most meteorologists don't understand them well. I spoke at a meteorologist conference last week in Tahoe about AI-weather and speaking at one this week in Colorado. All about these models (and more!).
 
WFO gonna really start discussing this tomorrow, don't recall anytime recent memory of the NBM spitting out these numbers at this lead time. Just shows you the model consensus of a strong winter storm signal
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Ask away. Love explaining these things as many/most meteorologists don't understand them well. I spoke at a meteorologist conference last week in Tahoe about AI-weather and speaking at one this week in Colorado. All about these models (and more!).
Maybe SouthernWx needs a Youtube video, question and answer session with you about AI-weather.
 
Ask away. Love explaining these things as many/most meteorologists don't understand them well. I spoke at a meteorologist conference last week in Tahoe about AI-weather and speaking at one this week in Colorado. All about these models (and more!).
Is that freezing rain map you made a stock qpf below 32? Or is it trying to factor in temps/rain rates/runoff/etc?
 
Is that freezing rain map you made a stock qpf below 32? Or is it trying to factor in temps/rain rates/runoff/etc?
QPF below 32F from period start to finish. weathernext2 (and other models) provide QPF total over 6 hour windows but temp at the times themselves, so if the ptype is ZR at start and end, it is counted. I reduce the ratio from 1:1 when either the start is above freezing or end is above freezing. And keep in mind, weathernext is an ensemble of 64 members, so we do this for each member and plot the mean.
 
Ask away. Love explaining these things as many/most meteorologists don't understand them well. I spoke at a meteorologist conference last week in Tahoe about AI-weather and speaking at one this week in Colorado. All about these models (and more!).
there any good articles, journals, reading material you can recommend about this stuff?
 
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