How scientists predict big winter storms

Summary

Podcast Summary: Short Wave – "How scientists predict big winter storms"

Date: January 28, 2026
Host: Regina Barber (NPR)
Guest: Rebecca Hersher (NPR Climate Reporter)
Expert Contributor: Kevin Reed (Atmospheric Scientist, Stony Brook University)

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Episode Overview

This episode of Short Wave dives into the science of winter storm prediction, centering on how advances in computer weather models, robust data collection, and government investment have made it possible for meteorologists to warn the public about large winter storms days (or even a week) in advance. Host Regina Barber and climate reporter Rebecca Hersher use the context of a major winter storm—affecting over half of the U.S. population—to explore how such forecasts are made and what challenges future predictions may face.

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Key Discussion Points & Insights

1. The Impact of the Recent Winter Storm

• Millions were affected: "It's super cold where you are too, right, Becky?" – Regina Barber [00:30]
• The magnitude: “The storm stretches over 2,300 miles... pelleting about half the United States population today.” – Regina Barber [00:58]
• The storm brought significant disruptions: at least 29 states impacted, with power outages for hundreds of thousands. [01:06]

2. Advances in Winter Storm Prediction

• Lead Time on Warnings: Rebecca notes the unusual amount of warning for this storm, with preparations starting nearly a week prior. [01:26]
• Not Always the Case: “That wasn’t something we could do 50 years ago.” – Kevin Reed [01:58]
• Why the Change?
  • Advances in computer weather models allow for days-ahead predictions
  • Improved models stem from coordinated Earth system observations and technological development (e.g., satellites, weather balloons) [02:13]

3. How Weather Models Work

• Types of Models: Multiple models exist (e.g., the “European model”), each with strengths. Forecasts are usually based on a weighted blend of these. [04:45]
• Mechanics: Models simulate real atmospheric processes, including cloud formation, wind, and temperature.
• Scenario Predictions: Models output probabilities for various outcomes (e.g., snow vs. rain), improving with model quality and data inputs. [05:47]
• Quote: “...You can ask that model questions... It gives you some scenarios with probabilities, like, usually when the conditions are like this, it’ll snow later this week, but there’s a chance it could rain instead.” – Rebecca Hersher [05:47]

4. What Makes a Good Weather Model?

• Computing Power Matters—but the most critical aspect is the quality of data fed into the models. [06:22]
• Data Must Be:
  • Plentiful: Many measurements across layers of the atmosphere and surfaces
  • Granular: High-resolution, widespread measurements (from weather balloons, satellites, ships, aircraft, and stations globally)
  • Continuous: Long-term, uninterrupted data sets for detecting rare but extreme events
• Quote: “For weather models, you need good data. And good data looks like plentiful, granular, continuous data.” – Rebecca Hersher [06:38]

5. The Importance of Past and Current Data

• Historic Data: The rise of satellite observations (since the late ’70s) gave unprecedented continuity and reach to weather data. [08:43]
• Data Repositories: Most data are maintained by governments, given investment in data collection infrastructure. [09:07]

6. Threats to Data Collection and Future Forecasting

• Current Risks: Rebecca points out that budget and staff cuts (referencing the Trump administration) threaten agencies like NOAA and NASA, as well as the National Weather Service and research institutions, potentially jeopardizing future data collection. [09:22]
• Consequences: Accurate, long-range storm forecasts depend on robust data flows. Threats to funding may reduce early warning capability.
• Quote: “It will be difficult to keep up this level of accurate early forecast if scientists and data are stymied... if we don’t see the kind of government investment... that we have in the past.” – Rebecca Hersher [10:39]

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Notable Quotes & Memorable Moments

• On Forecast Advancements:

  “The fact that we’re talking about an event in New York City... that’s happening in a few days from now. You know, that wasn’t something we could do 50 years ago.”
  – Kevin Reed [01:58]

• On Model Inputs:

  “Garbage in, garbage out. Important for our health. Also true, I think, of many fields of science, especially things where you have a large number of observations.”
  – Rebecca Hersher [06:38]

• Humorous Interjections:

  “That relates to my food intake. Right.”
  – Regina Barber (on “garbage in, garbage out”) [06:34]

• On the Nerd Appeal of Weather Science:

  “It was, like, fascinating to me that it was basically every field of science that goes into weather prediction.”
  – Regina Barber [06:15]

• On Budget Cuts and Data Risks:

  “Here in the US, some of that data is under threat right now because of budget and staff cuts that the Trump administration is pursuing.”
  – Rebecca Hersher [09:22]

• On the Value for Listeners:

  “So that you and I and millions of other people can get over to Home Depot in time to buy shovels and hand warmers and salt.”
  – Rebecca Hersher [10:28]

• On the Future:

  “The next time there’s a big storm, we’re going to have you back on and we’ll see how well we predicted it.”
  – Regina Barber [11:05]
  “If I have power.”
  – Rebecca Hersher [11:10]

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Important Segment Timestamps

• Cold open and storm intro: [00:17–01:06]
• Why early warnings are possible: [01:35–02:47]
• Models and how they work: [04:12–06:15]
• What makes a good model/data discussion: [06:22–08:21]
• Data collection history and threats: [08:43–10:39]
• Closing reflections and sign-off: [11:05–11:18]

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Tone and Language

The conversation is accessible, engaging, and laced with humor. Technical concepts are explained for a lay audience, and there’s a friendly rapport between host and guest:

• Regina’s curiosity and occasional jokes (“That relates to my food intake. Right?”) make complex science relatable.
• Becky’s explanations are clear, concise, and occasionally witty.

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Summary Takeaways

• Predicting large winter storms has vastly improved thanks to better data and computer modeling, giving people more time to prepare.
• Successful forecasting hinges on plentiful, granular, and continuous data, much of which comes from government-run infrastructure.
• Future forecasting may be at risk due to potential funding and staffing cuts to key agencies responsible for maintaining these critical datasets.
• The science of storm prediction is a cross-disciplinary effort and depends on sustained investment and public support.

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For listeners interested in related topics, the hosts recommend Short Wave episodes on storm prediction in the tropics and the impact of Santa Ana winds on California’s fire season.

Transcript

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[Regina Barber]

You're listening to Short Wave from NPR. Hello from Freezing Cold Washington, D.C. hi, I'm Regina Barber.

[Rebecca Hersher]

And I'm NPR climate reporter Rebecca Hersher.

[Regina Barber]

And it's super cold where you are too, right, Becky?

[Rebecca Hersher]

Oh, my gosh, yes. Let me check my window thermometer here. Yeah, it is 14 degrees right now in Baltimore.

[Regina Barber]

Oh, wow. Wow. I don't even wanna check for here.

[Rebecca Hersher]

It's gross. It's cold. There's like 8 inches of snow outside plus a couple inches of ice. We got hit by this storm.

[Regina Barber]

Yeah, I mean, me, you, tens of millions of other people. Like, this is a big one.

[Rebecca Hersher]

Well, if you wanted a snowy winter.

[Regina Barber]

You are getting it. Tonight. The storm stretches over 2300 miles with more than here. It is windy. It is pelleting about half the United.

[Rebecca Hersher]

States population today, shoveling, scraping and slogging their way through heavy snow. Yeah. At least 29 states were affected by this storm. It knocked out electricity for hundreds of thousands of people. And listening, winter storms happen. It's a thing. Yeah, in the winter. But as a climate reporter, one thing that was interesting to me about this storm was how much warning we got. Like, I've been preparing for almost a week before any snow even fell, which is a lot of lead time.

[Regina Barber]

Now that you mention it, I did start hearing about the storm a long time before it arrived. Was there more warning than usual?

[Rebecca Hersher]

Well, that's what's interesting to me. You know, this is actually par for the course these days, getting a lot of warning, but it didn't used to be that way. So last week I called up this climate named Kevin Reed at Stony Brook University to talk about the storm and he said this thing that was kind of interesting.

[Kevin Reed]

I mean, the fact that we're talking about an event in New York City, where I am. Right. That's happening in a few days from now. You know, that wasn't something we could do 50 years ago.

[Rebecca Hersher]

And that's because there have been these pretty amazing advances in computer weather models.

[Kevin Reed]

The ability that we are able to predict them days in advance is centered on the fact that the United States has made large efforts in coordinated observations of the Earth system so that we can build better and better models.

[Regina Barber]

So today on the show, we're taking a closer look at those models, how they work, what they tell us and why this storm felt like a slow moving train in a good way. I'm Regina Barber and you're listening to shortwave from NPR.

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[Regina Barber]

Okay, so today we're talking about the winter storm that just walloped like half the United States with NPR climate reporter Rebecca Hersher. A lot of us knew this storm was coming for almost a week before it actually arrived. What allows meteorologists to give this kind of early warning? Becky?

[Rebecca Hersher]

So really good computer models are the key and there are a bunch of them, like, I don't know, Gina, are you a weather nerd at all or are you more of like a functional weather consumer? You know, look at your phone, see if it's gonna rain. Put on jacket.

[Regina Barber]

Yeah, I'm very functional. I look like, I'm like, woo, we can do it. But like, no, I'm not a weather nerd.

[Rebecca Hersher]

You're not like asking more questions and googling it? No, no, that's fine. Very reasonable way to consume the weather. So this might be new to you. There are lots of weather models. They have different names, usually acronyms or like nicknames, like, you'll see the European model sounds very fancy. Yeah. And the weather forecast that you see on your phone or on TV are usually based on a bunch of these models.

[Regina Barber]

Okay, so like an average of all these models.

[Rebecca Hersher]

Yeah, like a weighted average. Because some models are better at predicting different types of weather or they can handle different scales, like a whole region versus zooming in on your specific city. And anyway, we could go down a whole rabbit hole on these models, but for our purposes, it's just important to know that there are a bunch of them. And the better the models are, the better the weather forecast is going to be.

[Regina Barber]

Right. Because these computer models kind of model how the atmosphere actually works in real life.

[Rebecca Hersher]

Exactly. And in real life, there is a lot going on in the atmosphere, as Kevin explained.

[Kevin Reed]

The way we estimate clouds, for example, and the processes that go into them and how that then links to wind and temperature.

[Rebecca Hersher]

So there are a lot of elements that the computer model has to get right. But if you can get it right, then you can ask that model questions. Basically say, hey, we're seeing this kind of wind, these kinds of temperatures, this atmospheric pressure. It's this time of year. What do you think's gonna happen next? And it gives you some scenarios with probabilities, like, usually when the conditions are like this, it'll snow later this week, but there's a chance it could rain instead. You know, the better the model, the more accurate those predictions are gonna be.

[Regina Barber]

Yeah, No, I had friends that were meteorologists, and it was, like, fascinating to me that it was basically every field of science that goes into weather prediction.

[Rebecca Hersher]

Totally.

[Regina Barber]

So, like, what makes a model good? Is it about computing power?

[Rebecca Hersher]

That is one element. Yeah. But the most crucial requirement is good data. So have you heard the saying garbage in, garbage out?

[Regina Barber]

Yes. That relates to my food intake. Right.

[Rebecca Hersher]

Gross. Yes. Yes, it does. I guess. No. Garbage in, garbage out. Important for our health. Also true. I think, of many fields of science, especially things where you have a large number of observations. So for weather models, you need good data. And good data looks like plentiful granular continuous data. And I'll go through each one of those. Okay, Right. So first, plentiful. The atmosphere is so complicated. There are layers of clouds. There are currents of air and moisture and changing temperatures and interactions with the land and the water underneath. If you want to capture all of that in a computer, you need a lot of measurements. Which brings us to number two, granular. You need to know what's happening all over the place and not just in One dimension, like down on the land or in one layer of the atmosphere. You need to have weather stations all over the world, and you also need to have measurements from the air column, you know, weather balloon measurements, radar measurements from plants, planes, planes, 100% measurements from ships and the ocean, satellite data looking down from space, all of it. And that leads us to the last requirement. Your data needs to be continuous.

[Regina Barber]

It can't stop.

[Rebecca Hersher]

It won't stop. Yeah. The most valuable data for weather models, and this is true of a lot of climate science as well, are data sets that cover a really long period of time, like decades, particularly if you're looking for patterns in extreme weather, because the more extreme the weather, the less often it happens. So you need to be able to look over long periods of time to understand what conditions happen that lead to that type of weather.

[Regina Barber]

Yeah, I mean, that makes sense. Like, if a giant winter storm only hits five or 10 years, that's going to be tough for a computer model to predict. And I can see how you'd need, like, a lot of continuous, granular data to see that coming. So given that we had all of this, like, lead time for this storm, I'm guessing that that type of data is available.

[Rebecca Hersher]

Yeah, I mean, it's not perfect. Okay. But there have been huge investments in data collection in the last 50 years or so, really, starting in the late 70s, early 80s, when Earth observing satellites started collecting continuous data about the planet. And at this point, there are a lot of data sets that go back 50 years or more. All the different computer models, they use all that data.

[Regina Barber]

And where does all this data live?

[Rebecca Hersher]

Oh, I'm so glad you asked. A lot of it is. See, you're becoming a weather nerd already.

[Regina Barber]

It's happening.

[Rebecca Hersher]

I'm sure a lot of it is maintained by governments because the satellites and the buoys and the balloons that collect this information, a lot of them are publicly funded.

[Regina Barber]

Yeah, makes sense.

[Rebecca Hersher]

But here in the US Some of that data is under threat right now because of budget and staff cuts that the Trump administration is pursuing. So the National Weather Service, you remember this, was interrupted pretty badly last year by mass staffing shortages, which led to missed launches of weather balloons. For example, the administration is trying to cut the budgets of agencies like NASA and the national oceanic and Atmospheric Administration, noaa, both of which employ people who manage these continuous data sets and make them available and useful to scientists. Oh, wow. Yeah. There's also a federally supported research lab in Colorado called the national center for Atmospheric Research. The administration is moving to dismantle that the White House Office of Management and Budget didn't respond to questions that we asked them about that plan. But it all adds up to a lot of headwinds for the kind of data and research that feeds these computer models, and that in turn spit out weather forecasts many days in advance so that you and I and millions of other people can get over to Home Depot in time to buy shovels and hand warmers and salt.

[Regina Barber]

So this lead time that we got for this storm, is this not maybe gonna be the norm if this data is no longer readily available?

[Rebecca Hersher]

I would say that as the weather gets more and more extreme, it will be difficult to keep up this level of like, accurate early forecast if scientists and data are stymied in the ways that they could be if all of these cuts were to go through. It's not happening right now, but it's something that could happen for sure if we don't see the kind of government investment in this type of data that we have in the past. Wow.

[Regina Barber]

Well, the next time there's a big storm, we're going to have you back on and we'll see how well we predicted it.

[Rebecca Hersher]

If I have power.

[Regina Barber]

Yeah, if you have power. Thanks for coming to talk with us today.

[Rebecca Hersher]

Of course. You're welcome.

[Regina Barber]

If you like this episode, follow us on the NPR app or wherever else you're listening from. It helps us out and helps you never miss a show. Speaking of, if you're interested in weather science, check out our episodes on better storm prediction in the tropics and how the Santa Ana winds impact the California fire season this time of year. We'll link to them in our show notes. This episode was produced by Hannah Chin, and it was edited by our showrunner, Rebecca Ramirez. Tyler Jones and Rebecca Hersher checked the facts. The audio engineer was Robert Rodriguez. News clips were from CBS Boston, Fox Weather, Fox. For Dallas Fort Worth and PBS NewsHour, I'm Regina Barber. Thank you for listening to shortwave from npr.

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[Ira Glass]

On this American Life. One thing we like is a good mystery sometimes about really big things. But most times the little mysteries are the best.

[Rebecca Hersher]

Our lost and found is currently filled with pants. I don't know. I've never seen this happen.

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Is this true?

[Rebecca Hersher]

This is true.

[Ira Glass]

Mysteries of every size. Each week, this American Life. Wherever you get your podcasts.