Possible huge snow storm next week?

[Jer]

Watched the 6:00 weather on KARE 11 while riding bike at Life Time. They were calling for minimal snow here but did confirm that everyone in Iowa and Illinois and parts of Wisconsin is going to die. Bummer for you guys.

Really not surprising; Iowa is good at basketball so hell has frozen over and is unleashing punishment on Iowa for out of staters having to be subjected to more unathletic white guys that can't dunk.

 

[Wesley]

Cyclogenesis latest update says Lincoln to Omaha will get 20 inches, Des Moines around a foot and it's gonna be bad...really bad. He thinks there is no way the refs make the game on Tuesday. If they can't get there on Monday night, the game will no doubt be postponed.

Land in Chicago and put them on megabus. Seriously, they need to roll in Monday morning.

 

[alarson]

 

[cycub51]

Watched the 6:00 weather on KARE 11 while riding bike at Life Time. They were calling for minimal snow here but did confirm that everyone in Iowa and Illinois and parts of Wisconsin is going to die. Bummer for you guys.

You really trust Belinda?

 

[ClonesTwentyOne]

This thing has stayed pretty consistent

 

[Wesley]

This thing has stayed pretty consistent

The big part has shifted from Omaha to Harlan.

 

[Cyclonepride]

Obviously they don't always come down. They can go up too, but as to why they spit out 99th percentile solutions? Many reasons:

All these maps that have been posted in this thread are raw snowfall totals, that don't take into account melting or snowfall compaction. I think in many cases above there's at least a 3"+ bias baked into it because of this alone.

Something that we see particularly in the summer, but can also apply here are feedback loops, convective feedback. Basically problems with how the model handles convection and how storm that the model has spun up is modifying the modeled environment...it can create a runaway train effect (in summer, it's going to storm cause its so humid, it's so humid cause of these storms, it's going to storm cause it's humid...).

Think Spiderman 2 (I think)...when the bad guy's "precious tritium™" sun energy whatsit becomes self-sustaining, more or less what's going on sometimes with the run away snow or rain totals.

There are other biases that are in the models, things they don't/can't handle well.

Plus, with the storm still way out in the Pacific, it obviously isn't getting sampled, so there's very little real information going into the starting point. Then you add in the fact that the model is running in an "idealized" environment, and the resulting error propagation and "compounding interest" errors make for some crazy results.

You folks have just been educated. I expect you all to forget this before the next storm.

 

[Mtowncyclone13]

Am I the only person who just doesn't care about "weather events"? News hypes storms so much the past 10 years it's hard to take anything seriously anymore.

 

[CycloneErik]

You folks have just been educated. I expect you all to forget this before the next storm.

Forget what?

 

[ClonesTwentyOne]

Am I the only person who just doesn't care about "weather events"? News hypes storms so much the past 10 years it's hard to take anything seriously anymore.

I always hope for the worst. I'm hoping for 20 inches of snow. My job requires me to drive all over the place and I need to use my weather days.

 

[SplitIdentity]

Am I the only person who just doesn't care about "weather events"? News hypes storms so much the past 10 years it's hard to take anything seriously anymore.

You don't care about the end of the human race as we know it?

You're savage.

 

[Doc]

Obviously they don't always come down. They can go up too, but as to why they spit out 99th percentile solutions? Many reasons:

All these maps that have been posted in this thread are raw snowfall totals, that don't take into account melting or snowfall compaction. I think in many cases above there's at least a 3"+ bias baked into it because of this alone.

Something that we see particularly in the summer, but can also apply here are feedback loops, convective feedback. Basically problems with how the model handles convection and how storm that the model has spun up is modifying the modeled environment...it can create a runaway train effect (in summer, it's going to storm cause its so humid, it's so humid cause of these storms, it's going to storm cause it's humid...).

Think Spiderman 2 (I think)...when the bad guy's "precious tritium™" sun energy whatsit becomes self-sustaining, more or less what's going on sometimes with the run away snow or rain totals.

There are other biases that are in the models, things they don't/can't handle well.

Plus, with the storm still way out in the Pacific, it obviously isn't getting sampled, so there's very little real information going into the starting point. Then you add in the fact that the model is running in an "idealized" environment, and the resulting error propagation and "compounding interest" errors make for some crazy results.

I should have definitely been a meteorologist rather than an engineer.

 

[Tailg8er]

Obviously they don't always come down. They can go up too, but as to why they spit out 99th percentile solutions? Many reasons:

All these maps that have been posted in this thread are raw snowfall totals, that don't take into account melting or snowfall compaction. I think in many cases above there's at least a 3"+ bias baked into it because of this alone.

Something that we see particularly in the summer, but can also apply here are feedback loops, convective feedback. Basically problems with how the model handles convection and how storm that the model has spun up is modifying the modeled environment...it can create a runaway train effect (in summer, it's going to storm cause its so humid, it's so humid cause of these storms, it's going to storm cause it's humid...).

Think Spiderman 2 (I think)...when the bad guy's "precious tritium™" sun energy whatsit becomes self-sustaining, more or less what's going on sometimes with the run away snow or rain totals.

There are other biases that are in the models, things they don't/can't handle well.

Plus, with the storm still way out in the Pacific, it obviously isn't getting sampled, so there's very little real information going into the starting point. Then you add in the fact that the model is running in an "idealized" environment, and the resulting error propagation and "compounding interest" errors make for some crazy results.

I get what you're saying (to a degree). But if all that is true, why don't meteorologists take all of that that they learn from past storms & apply it to their new forecasts?

For instance, if 90% of previous forecasted winter storms have produced 50% of forecasted amounts, why not adjust today's projections down to account for the inevitable error?

 

[SaraV]

As much as we may, or may not, get in whichever part of the state, I'm SO glad I don't live in Lincoln anymore.

 

[bawbie]

I get what you're saying (to a degree). But if all that is true, why don't meteorologists take all of that that they learn from past storms & apply it to their new forecasts?

For instance, if 90% of previous forecasted winter storms have produced 50% of forecasted amounts, why not adjust today's projections down to account for the inevitable error?

But they do. At least here the models say 12+ and the forecast is 5-8.

 

[cyinne]

I get what you're saying (to a degree). But if all that is true, why don't meteorologists take all of that that they learn from past storms & apply it to their new forecasts?

For instance, if 90% of previous forecasted winter storms have produced 50% of forecasted amounts, why not adjust today's projections down to account for the inevitable error?

For meteorologists I think it's sort of a double edged sword. People complain when mets overplay a storm after the fact, people complain when mets underplay a storm or don't predict enough that actually fell. Personally I would rather mets overplay a storm so at least you are generally prepared for the worst case senario- where as a underplayed storm could mean big trouble for those that aren't prepared.

 

[dahliaclone]

That pushes the Twin Cities into more snow than previous editions (speaking of the latest image alarson attached above)

 

[BBHMagic]

I get what you're saying (to a degree). But if all that is true, why don't meteorologists take all of that that they learn from past storms & apply it to their new forecasts?

For instance, if 90% of previous forecasted winter storms have produced 50% of forecasted amounts, why not adjust today's projections down to account for the inevitable error?

Like Bawbie said the meteorologists predictions are usually lower than the models for these big storms. What I don't get (and maybe this is what you actually meant) is why the programmers wouldn't calibrate the computer models to represent what they really think will happen.

 

[Gunnerclone]

Like Bawbie said the meteorologists predictions are usually lower than the models for these big storms. What I don't get (and maybe this is what you actually meant) is why the programmers wouldn't calibrate the computer models to represent what they really think will happen.

If the whole point is protecting life and property then isn't it better to overhype and under deliver? I feel like people think about weather forecasting as a sport rather than a civil science.

 

[3TrueFans]

If the whole point is protecting life and property then isn't it better to overhype and under deliver? I feel like people think about weather forecasting as a sport rather than a civil science.