Confidence has increased enough to warrant winter storm watch
issuance for the entire CWA. Latest 12Z guidance coming into better
agreement on potent northern stream shortwave energy diving SW from
wrn Canada and the northern Plains into the plains and mid
Mississippi valley by Sunday morning, phasing with southern stream
coming out of the SW states and Mexico to carve out a deep closed
low aloft over the Mid Atlantic and induce rapid cyclogenesis off
the Mid Atlantic coast, with the surface low bombing out from from
1008 mb off the N Carolina coast Sunday morning to 970-975 mb near
38-39N/71W by Monday morning, then passing just outside the 40N/70W
benchmark Monday afternoon, GFS still more intense and closer to the
coast than the ECMWF, with its heaviest snow bands directly over the
area as opposed to just offshore. NAM and SREF have both trended
toward a heavier snowfall scenario as well, which has been a good
signal in past heavy snowfall events.
Snow should start Sunday morning, and may mix with rain at times
especially in the NYC metro area, NE NJ and western Long Island.
Then as precip intensity picks up later in the day Sunday p-type
should become all snow throughout. Heaviest snow looks to fall from
late day Sunday into Monday morning, then snow tapers off Monday
afternoon.
Greatest likelihood of seeing 6+ inches will be along the coast,
especially eastern Long Island where up to a foot of accumulation is
possible. Winds will also be strong Sunday night into Monday morning
especially along the coast as the sfc low deepens, with blowing and
drifting snow and some downed tree limbs as winds gust to at least
40-45 mph, and possible blizzard conditions in Suffolk, and near
blizzard conditions elsewhere along the coast including NYC. NAM/GFS
both signal potential for wind gusts to 60 mph late Sunday night
into Monday morning, though these winds can sometimes be overdone in
heavy snow events. If trends for heavy snow and strong winds
continue to increase and expand northward, the potential for
blizzard conditions could encompass all coastal areas.
A bit of scorn coming your way in the replies but it's not necessarily intuitive if you haven't thought about it. Some analogies that might help:
- If I play roulette in a casino today, I might win big, break even, lose a little or lose a lot. If I play roulette in a casino every day for a decade, I can be nearly certain I will lose a lot.
- Consider an ant walking on a rough stone road built up the side of a hill. If you look at the ant at any particular second, its body might be pointing up (head higher than tail) or down (vice versa) or level, depending on what particular angle of rock it's on at that time. But measured over minutes its likely to be at a greater altitude above sea level than where it started. Measured over the hours it takes to get from the bottom to the top, it's definitely higher.
- A random day of the year (pick from 1-365) in England might be sunny or rainy, but the chances of it being sunnier are higher if the day picked is in the summer.
The point is that there's a tremendous amount of noise in short-term measurements which tend to smooth out over longer term where trends are more clearly revealed. That's the counterpoint to your argument and the reason why climate prediction is not the same as weather forecasting. Going back to the casino analogy, climate prediction is looking at your bank balance over decades; weather forecasting is deciding how to bet on a particular poker hand.
(And finally, we actually kind of do mostly know what's going to happen tomorrow, but not a week out; that's not the point you're making though.)
Are you really equating daily weather predictions with meteorological science? That's like saying "they don't know what the next 3 coin flips are going to be but they know half of the next 10,000 will be tails"
That's not exactly true, it seems. Forecasts become less accurate the further out you go, unlike coin flips.
Weather forecasts are generally accurate about 90% of the time for a five-day forecast and around 80% for a seven-day forecast. Forecasts beyond ten days are only correct about half the time.
You can’t predict a coin flip because it is random. However, we have an accurate understanding of the random process producing coin flips and therefore, we can make accurate predictions about large quantities of flips.
Weather may or may not be random. It could be entirely deterministic for all we know. However, we lack the ability to fully model all the factors that contribute to weather and therefore our predictions are inaccurate.
Now let’s consider long term climate predications. Do you think these predictions are more like coin flips, where we have an extremely accurate model of the process, or more like weather, where unknown unknowns have outsized impact on accuracy?
That’s not to say climate change isn’t real, but your analogy doesn’t make sense.
All responses are so focused on exact predictions. We have high certainty that 50% of flips will be tails over long enough timespan. We don't know what any single flip will be. Climate science works the same way. But climate is not a coin, let's say it's a multisided die and it appears the sides are changing sizes as we compare data year over year.
Predictive models are not the same as historic data analysis and trend fitting.
Flipping coins: no predictive models, very definitive statistics
Weather: +/- 2 week predictive models, 100 years of measurements getting more definitive each year where trend are headed
Our models of weather are so accurate that literally trillions of dollars per year bank on them in the agriculture sector, the shipping sector, and everywhere else. Similarly, our models of climate change have been refined and refined, and now are essentially irrefutable.
> more like weather, where unknown unknowns have outsized impact on accuracy
"Unknown unknowns" aren't the reason weather forecasts are inaccurate.
Weather is path-dependent. Small changes to starting conditions or minor differences between modeled and actual conditions shortly after the simulation begins lead to large differences by the end of the simulation. Errors propagate and magnify.
Because energy in > energy out is a pretty simple non-chatoic thermodynamic equation with pretty limited variables and weather is one of the most complicated dynamic fractal systems imaginable. Why is this hard to understand? You might as well complain that they haven't described the exact curvature of the coast line of England at the nanometer level and yet they can avoid crashing ships into it with GPS.
And this contrasts with other fields - for example, in orbital mechanics, we're pretty darn certain where the Earth will be tomorrow. We're a bit less sure about 30 years, and increasingly uncertain after that.
Area Forecast Discussion National Weather Service New York NY 344 PM EST Fri Feb 20 2026 Issued by NWS Upton, NY
https://forecast.weather.gov/product.php?site=NWS&issuedby=O...
Confidence has increased enough to warrant winter storm watch issuance for the entire CWA. Latest 12Z guidance coming into better agreement on potent northern stream shortwave energy diving SW from wrn Canada and the northern Plains into the plains and mid Mississippi valley by Sunday morning, phasing with southern stream coming out of the SW states and Mexico to carve out a deep closed low aloft over the Mid Atlantic and induce rapid cyclogenesis off the Mid Atlantic coast, with the surface low bombing out from from 1008 mb off the N Carolina coast Sunday morning to 970-975 mb near 38-39N/71W by Monday morning, then passing just outside the 40N/70W benchmark Monday afternoon, GFS still more intense and closer to the coast than the ECMWF, with its heaviest snow bands directly over the area as opposed to just offshore. NAM and SREF have both trended toward a heavier snowfall scenario as well, which has been a good signal in past heavy snowfall events.
Snow should start Sunday morning, and may mix with rain at times especially in the NYC metro area, NE NJ and western Long Island. Then as precip intensity picks up later in the day Sunday p-type should become all snow throughout. Heaviest snow looks to fall from late day Sunday into Monday morning, then snow tapers off Monday afternoon.
Greatest likelihood of seeing 6+ inches will be along the coast, especially eastern Long Island where up to a foot of accumulation is possible. Winds will also be strong Sunday night into Monday morning especially along the coast as the sfc low deepens, with blowing and drifting snow and some downed tree limbs as winds gust to at least 40-45 mph, and possible blizzard conditions in Suffolk, and near blizzard conditions elsewhere along the coast including NYC. NAM/GFS both signal potential for wind gusts to 60 mph late Sunday night into Monday morning, though these winds can sometimes be overdone in heavy snow events. If trends for heavy snow and strong winds continue to increase and expand northward, the potential for blizzard conditions could encompass all coastal areas.
Beautiful new york
They don't know what's going to happen tomorrow but they know what's going to happen in 30 years.
A bit of scorn coming your way in the replies but it's not necessarily intuitive if you haven't thought about it. Some analogies that might help:
- If I play roulette in a casino today, I might win big, break even, lose a little or lose a lot. If I play roulette in a casino every day for a decade, I can be nearly certain I will lose a lot.
- Consider an ant walking on a rough stone road built up the side of a hill. If you look at the ant at any particular second, its body might be pointing up (head higher than tail) or down (vice versa) or level, depending on what particular angle of rock it's on at that time. But measured over minutes its likely to be at a greater altitude above sea level than where it started. Measured over the hours it takes to get from the bottom to the top, it's definitely higher.
- A random day of the year (pick from 1-365) in England might be sunny or rainy, but the chances of it being sunnier are higher if the day picked is in the summer.
The point is that there's a tremendous amount of noise in short-term measurements which tend to smooth out over longer term where trends are more clearly revealed. That's the counterpoint to your argument and the reason why climate prediction is not the same as weather forecasting. Going back to the casino analogy, climate prediction is looking at your bank balance over decades; weather forecasting is deciding how to bet on a particular poker hand.
(And finally, we actually kind of do mostly know what's going to happen tomorrow, but not a week out; that's not the point you're making though.)
We’ve been discussing it for 30 years.
Having people making the same stupid comment after 3 decades needs to be handled more critically
Let’s go back 40 years and listen to the warning:
https://youtu.be/3NvgJ1b6JXs?si=yUKcegVwfyNGi2rC
We should show @blell some grace. Not everybody is born knowing everything. Today is the day they learned about climate change.
Grace is reserved for people with humility and a willingness to learn . Not self destructive suicidal death cult fools.
Are you really equating daily weather predictions with meteorological science? That's like saying "they don't know what the next 3 coin flips are going to be but they know half of the next 10,000 will be tails"
Unfortunately weather predictions aren’t as simple as a coin flip. But I’m sure meteorologists would manage to fuck up coin flips too.
and humans haven't figured out anything more complex than a coin flip...
That's not exactly true, it seems. Forecasts become less accurate the further out you go, unlike coin flips.
Weather forecasts are generally accurate about 90% of the time for a five-day forecast and around 80% for a seven-day forecast. Forecasts beyond ten days are only correct about half the time.
Why do you pack a light jacket if you go to Tasmania for a week in June, regardless of the forecast?
Why might you wear a life jacket on a boat, even though you're 99.9% likely not to have the boat flip over?
There's a difference between being slightly uncomfortable for a week and dying.
you're conflating statistics and trends with discrete predictions
Well, I'm not. The previous poster is. I'm more simply pointing out, without using terminology, that we shouldn't.
You can’t predict a coin flip because it is random. However, we have an accurate understanding of the random process producing coin flips and therefore, we can make accurate predictions about large quantities of flips.
Weather may or may not be random. It could be entirely deterministic for all we know. However, we lack the ability to fully model all the factors that contribute to weather and therefore our predictions are inaccurate.
Now let’s consider long term climate predications. Do you think these predictions are more like coin flips, where we have an extremely accurate model of the process, or more like weather, where unknown unknowns have outsized impact on accuracy?
That’s not to say climate change isn’t real, but your analogy doesn’t make sense.
All responses are so focused on exact predictions. We have high certainty that 50% of flips will be tails over long enough timespan. We don't know what any single flip will be. Climate science works the same way. But climate is not a coin, let's say it's a multisided die and it appears the sides are changing sizes as we compare data year over year.
I think you’re missing my point: we’re only able to predict large numbers of coin flips because we have an accurate model.
We don’t have an accurate model for weather, so we can’t predict it well.
I don’t see a reason to assume our model for climate is accurate, either.
Predictive models are not the same as historic data analysis and trend fitting.
Flipping coins: no predictive models, very definitive statistics Weather: +/- 2 week predictive models, 100 years of measurements getting more definitive each year where trend are headed
Our models of weather are so accurate that literally trillions of dollars per year bank on them in the agriculture sector, the shipping sector, and everywhere else. Similarly, our models of climate change have been refined and refined, and now are essentially irrefutable.
> more like weather, where unknown unknowns have outsized impact on accuracy
"Unknown unknowns" aren't the reason weather forecasts are inaccurate.
Weather is path-dependent. Small changes to starting conditions or minor differences between modeled and actual conditions shortly after the simulation begins lead to large differences by the end of the simulation. Errors propagate and magnify.
Over large time periods the errors average out.
Because energy in > energy out is a pretty simple non-chatoic thermodynamic equation with pretty limited variables and weather is one of the most complicated dynamic fractal systems imaginable. Why is this hard to understand? You might as well complain that they haven't described the exact curvature of the coast line of England at the nanometer level and yet they can avoid crashing ships into it with GPS.
And this contrasts with other fields - for example, in orbital mechanics, we're pretty darn certain where the Earth will be tomorrow. We're a bit less sure about 30 years, and increasingly uncertain after that.
Yes, that's essentially correct. Unintuitive if you don't understand statistics, but correct.
Do you really not understand that this actually makes a lot of sense?
If we're talking about dynamic systems then it makes sense
The more you pay attention to what already happened, the deeper your observation might be.
Apparently there was just a hurricane in winter and it struck New York.
I guess not everybody is going to have any understanding at all, you proably had to be there :\