DT appreciates yall tracking his storm for him and congrats to BOS ! They will get 2 feet from this weekends bomb
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Misc General Banter Thread
- Thread starter Rain Cold
- Start date
GeorgiaGirl
Member
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ducketta27
Member
I really hope I am wrong, it does kind of look like this one is slowly slipping away from the south... based on the trends today... maybe that changes! Lets start good trends tonight!
I'm here, name change but they keep summoning metwannabe lol. If we get 2' of snow I'll change my name to whatever they want and never look back. Glad to see you back on here
packfan98
Moderator
And now we wait for the New King to report in. I hate that the Google WeatherNext model is more readily available.
Yeah chances are.. for our climo, we do not see as lush a setup as we've had the past week or so. That was our chance.
There's still time for a gulf low to pop off before next weekend, but I'm whistling past the graveyard
PvilleWeather
Member
Yep. This hobby is beginning to really suck! Us in Alabama continue to find new ways to suck regarding winter weather! Especially us here in Central Alabama. I’ve had storms miss me in literally all directions the past couple years. Somebody around here must have pissed off the weather Gods…hopefully not me
GeorgiaGirl
Member
Sctvman
Member
It's Monday
freebird581bd
Member
I NEED ABOUT 3 WEEKS OF NICER WEATHER. WE ARE BUILDING A NEW FIRE STATION AND CANT EVEN GET THE FOOTERS POURED. IF WE CAN GET THEM AND THE PAD DOWN THEN WE WILL BE GOOD
cae will have ptype issues or no moisture at all; bank it
This was the boost I needed before bedtime. Thank you kylo
DustinWx
Moderator
He was a great generator sales man
I almost bit the bullet on stormvista just so I could see it myself lol. Seeing that Euro run bout to make me do stupid things
I count a 2” event as significant here in Atlanta. That’s more than enough to make everything white, make snow angels, sled down the hill in the backyard, throw some snowballs, and maybe make a small snowman. That’s really all I want out of a winter storm. Anything extra is just a cherry on top.
BrickTamland
Member
PvilleWeather
Member
Does anyone have any old school DGEX maps for us here in Bama? These other models suck 
Nice thing about SV is you can do month to month. I keep it for the winter. And the models load fast and I like the gif feature.
Claycochaser
Member
Sooo...about that. Don't be mad...but @Arcc and I may or may not have made this, erm...deal...back in 2017. I mean..it seemed perfectly harmless at the time. We really wanted a big snow, bruh. Had to make moves...strike while the iron was hot. You know how it goes, right? No hard feelings? Really sorry that it cost us our next 5 first round draft pick snowstorms. Oh and the Pandem...I mean. No. Just the snowstorms. Cheers!Yep. This hobby is beginning to really suck! Us in Alabama continue to find new ways to suck regarding winter weather! Especially us here in Central Alabama. I’ve had storms miss me in literally all directions the past couple years. Somebody around here must have pissed off the weather Gods…hopefully not me
Also, Saban had to retire. That one hurt.
I got that Andy Griffith reference, thank you.
Chat gives me that copium
So who or where do you keep getting these images without giving credit to the source?
BrickTamland
Member
I was just replying to the image someone posted in the storm thread. Is that not allowed? Are we supposed to say where we get the images from the model runs when we post them in the storm thread?
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lizajane
Member
It's just telling you what you want to hear.
Well don’t ask it about Wilmington lol yikes
I could only assume that Google is receiving an incredibly large increase in requests to get access to their WeatherNext 2.0 model data. We should start seeing all kinds of new plot styles soon for it.
Iceagewhereartthou
Member
If we're being honest, we all want the most snow possible for our own backyards and, human nature, we look at what others have and want it.
The patterns have moved around over the decades. I think 60s through 90s we're better for Piedmont/ mountain areas. Seems like that 2000 Crusher storm rang in the new millennia and a new pattern where I-77 and east is the place to be.
For a 30 year stretch GSP got double digits about every other year, now it hasn't happened once in 33 years. GSP has only had 3 snows over 6 inches since 1993; Feb 2004 (and a few miles west of there was just a couple inches), Jan 2011, and Jan 2022. Everything else has been somewhere else; either west of the Apps, I-77 and East, or Coastals.
Maybe things just move in longer patterns and we just have to take what we can get. Counting this past weekends system, GSP, AVL, and CLT have had a combined snow total of 7 inches in 4 years. Parts of the SC coast got more than that last Jan (not to mention FL panhandle). We all want more snow and it's hard to watch "everybody else" get more than our own backyards but living in this part of the country maybe that's how it is. Yeah, we think of some areas as "supposed to get more than," but maybe that's not what always works. I don't begrudge anyone else snowfall but I certainly would like to get more than I'm getting now. I guess most of you can identify with that sentiment.
The patterns have moved around over the decades. I think 60s through 90s we're better for Piedmont/ mountain areas. Seems like that 2000 Crusher storm rang in the new millennia and a new pattern where I-77 and east is the place to be.
For a 30 year stretch GSP got double digits about every other year, now it hasn't happened once in 33 years. GSP has only had 3 snows over 6 inches since 1993; Feb 2004 (and a few miles west of there was just a couple inches), Jan 2011, and Jan 2022. Everything else has been somewhere else; either west of the Apps, I-77 and East, or Coastals.
Maybe things just move in longer patterns and we just have to take what we can get. Counting this past weekends system, GSP, AVL, and CLT have had a combined snow total of 7 inches in 4 years. Parts of the SC coast got more than that last Jan (not to mention FL panhandle). We all want more snow and it's hard to watch "everybody else" get more than our own backyards but living in this part of the country maybe that's how it is. Yeah, we think of some areas as "supposed to get more than," but maybe that's not what always works. I don't begrudge anyone else snowfall but I certainly would like to get more than I'm getting now. I guess most of you can identify with that sentiment.
blueheronNC
Member
My chatGPT copium tonight on the old v. new king of the models:
1) Noise inside the model (AIFS-Ens style)
What happens mechanically
In AIFS-Ens-style systems:
• You have one learned deterministic mapping
• Randomness is injected:
o into hidden layers
o or into intermediate representations
o or as stochastic perturbations during rollout
Each ensemble member is:
the same function evaluated with different internal randomness
Think:
same physics surrogate + different random nudges
What this implies conceptually
• All ensemble members share the same inductive biases
• Differences emerge locally and propagate forward
• Spread grows because noise is amplified by dynamics
This is close in spirit to traditional NWP:
• perturb initial conditions
• let chaos do the rest
Strengths
• Strong mean skill
• Good calibration near the bulk of the distribution
• Ensemble members remain physically coherent
Limitation (subtle but important)
The ensemble is still confined to:
one learned “belief” about how the atmosphere evolves
So:
• If the model’s dynamics are slightly wrong
• Or if uncertainty should be structural (e.g., different plausible evolutions)
…the ensemble can become under-dispersed, especially in the tails.
2) Function-space noise (WeatherNext / FGN style)
This is a deeper shift.
What changes fundamentally
Instead of learning one function, the model learns a distribution over functions:
Each ensemble member is:
a different sampled forecasting rule, not just a noisy execution of one rule
Concretely:
• Noise is injected into latent variables that parameterize the function itself
• These latents control:
o flow strength
o wave propagation
o teleconnection patterns
o error growth modes
So two ensemble members may:
• disagree on how strongly a ridge amplifies
• or how fast a trough propagates
—not just on small perturbations around a single trajectory.
A useful mental model
Think of drawing different plausible “atmospheres”:
• One atmosphere where:
o baroclinic growth is slightly stronger
• Another where:
o wave breaking occurs earlier
• Another where:
o tropical–extratropical coupling is weaker
Each is internally consistent—but different.
That’s function-space diversity.
Why this matters for probabilistic skill
Tail behavior
Function-space noise naturally produces:
• fatter, more realistic tails
• rare-but-possible scenarios
• alternative synoptic evolutions
CRPS rewards this if those scenarios occur with the right frequency.
This is why people describe WeatherNext-style models as:
“better dispersed” rather than “more random”
Joint structure
Because the randomness controls the function, not individual grid points:
• Spatial coherence is preserved
• Large-scale patterns vary meaningfully
• Extremes are physically plausible
This matters hugely for:
• storm tracks
• blocking regimes
• tropical cyclone paths
Why both optimize CRPS, but one benefits more
CRPS punishes two failure modes:
Too narrow - Big penalty when reality falls outside
Too wide - Constant penalty for hedging
Function-space noise gives the model a richer way to adjust spread:
• widen uncertainty only where dynamics are ambiguous
• remain sharp where predictability is high
Layer noise has fewer degrees of freedom to do this.
That’s the practical reason WeatherNext-style models edge ahead.
Analogy that tends to click
Orchestra analogy
• Layer noise:
One orchestra, same sheet music, musicians wiggle timing slightly.
• Function-space noise:
Same score idea, but different conductors interpreting it:
o tempo changes
o emphasis shifts
o phrasing differs
Both produce music.
Only the second produces meaningfully different interpretations.
1) Noise inside the model (AIFS-Ens style)
What happens mechanically
In AIFS-Ens-style systems:
• You have one learned deterministic mapping
• Randomness is injected:
o into hidden layers
o or into intermediate representations
o or as stochastic perturbations during rollout
Each ensemble member is:
the same function evaluated with different internal randomness
Think:
same physics surrogate + different random nudges
What this implies conceptually
• All ensemble members share the same inductive biases
• Differences emerge locally and propagate forward
• Spread grows because noise is amplified by dynamics
This is close in spirit to traditional NWP:
• perturb initial conditions
• let chaos do the rest
Strengths
• Strong mean skill
• Good calibration near the bulk of the distribution
• Ensemble members remain physically coherent
Limitation (subtle but important)
The ensemble is still confined to:
one learned “belief” about how the atmosphere evolves
So:
• If the model’s dynamics are slightly wrong
• Or if uncertainty should be structural (e.g., different plausible evolutions)
…the ensemble can become under-dispersed, especially in the tails.
2) Function-space noise (WeatherNext / FGN style)
This is a deeper shift.
What changes fundamentally
Instead of learning one function, the model learns a distribution over functions:
Each ensemble member is:
a different sampled forecasting rule, not just a noisy execution of one rule
Concretely:
• Noise is injected into latent variables that parameterize the function itself
• These latents control:
o flow strength
o wave propagation
o teleconnection patterns
o error growth modes
So two ensemble members may:
• disagree on how strongly a ridge amplifies
• or how fast a trough propagates
—not just on small perturbations around a single trajectory.
A useful mental model
Think of drawing different plausible “atmospheres”:
• One atmosphere where:
o baroclinic growth is slightly stronger
• Another where:
o wave breaking occurs earlier
• Another where:
o tropical–extratropical coupling is weaker
Each is internally consistent—but different.
That’s function-space diversity.
Why this matters for probabilistic skill
Tail behavior
Function-space noise naturally produces:
• fatter, more realistic tails
• rare-but-possible scenarios
• alternative synoptic evolutions
CRPS rewards this if those scenarios occur with the right frequency.
This is why people describe WeatherNext-style models as:
“better dispersed” rather than “more random”
Joint structure
Because the randomness controls the function, not individual grid points:
• Spatial coherence is preserved
• Large-scale patterns vary meaningfully
• Extremes are physically plausible
This matters hugely for:
• storm tracks
• blocking regimes
• tropical cyclone paths
Why both optimize CRPS, but one benefits more
CRPS punishes two failure modes:
Too narrow - Big penalty when reality falls outside
Too wide - Constant penalty for hedging
Function-space noise gives the model a richer way to adjust spread:
• widen uncertainty only where dynamics are ambiguous
• remain sharp where predictability is high
Layer noise has fewer degrees of freedom to do this.
That’s the practical reason WeatherNext-style models edge ahead.
Analogy that tends to click
Orchestra analogy
• Layer noise:
One orchestra, same sheet music, musicians wiggle timing slightly.
• Function-space noise:
Same score idea, but different conductors interpreting it:
o tempo changes
o emphasis shifts
o phrasing differs
Both produce music.
Only the second produces meaningfully different interpretations.
Welp..... don't fail me now
I don’t get the gripe with the weathermen. They don’t issue watches and warnings. If the NWS puts you under an ice storm warning and all of the data says “yeah this is about to be really bad” you have to forecast that way. You can’t forecast on a hunch that “hey yeah I think maybe probably the wedge is SO undermodeled that the freezing rain is maybe probably going to freeze a couple hundred feet above the treeline and turn back into ice crystals before reaching the surface. I know ball.”
Can’t say I agree, at least for the east side (Decatur)… these pics are from 2 separate events literally last year.
View attachment IMG_5836.jpeg
View attachment IMG_5988.jpeg
Edit: not sure why they are links now and not photos in the post.
Yeah. Last year’s 1st January storm was pretty good. Even with the change over to freezing rain. It stuck around for a long time. And it broke what did seem like a long drought for me.
2nd storm was meh (and doesn’t count with its cut off at i85).
I’m not feeling good about chances for this weekend. Hoping it pulls in more of the board. Especially the folks over in bama. Want everyone to get a good, classic snowstorm.