How scientists predict big winter storms

This past weekend, Winter Storm Fern struck the States. Sleet, snow and ice battered Americans all the way from New Mexico to New York. Scientists predicted its arrival in mid-January, and in anticipation of the storm, more than 20 state governors issued emergency declarations. But how did scientists know so much, so early, about the approaching storm? NPR climate reporter Rebecca Hersher https://www.npr.org/people/384067907/rebecca-hersher says it has to do with our weather models… and the data we put into them. Which begs the question: Will we continue to invest in them? Interested in more science behind the weather? Check out our episodes on better storm prediction https://www.npr.org/2025/10/10/nx-s1-5570121/weather-island-science-forecast-storm in the tropics and how the Santa Ana winds impact the fire season https://www.npr.org/transcripts/1249800806 this time of year. Have a question we haven’t covered? Email us at [email protected] mailto:[email protected]. We’d love to consider it for a future episode! Listen to every episode of Short Wave sponsor-free and support our work at NPR by signing up for Short Wave+ at plus.npr.org/shortwave http://plus.npr.org/shortwave. This episode was produced by Hannah Chinn. 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 4 Dallas-Fort Worth, and PBS Newshour. See pcm.adswizz.com https://pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences. NPR Privacy Policy https://www.npr.org/about-npr/179878450/privacy-policy

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• 0:00Speaker 00

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• 0:18Speaker 01

  You're listening to Short Wave from NPR.

• 0:23Regina Barber?

  Hello from freezing cold Washington DC. I'm Regina Barber.

• 0:28Speaker 03

  And I'm NPR climate reporter Rebecca Hersher.

• 0:30Regina Barber?

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

• 0:34Speaker 03

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

• 0:43Regina Barber?

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

• 0:46Speaker 03

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

• 0:53Regina Barber?

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

• 0:58Speaker 04

  Well, if you wanted a snowy winter, you are getting it tonight.

• 1:00Speaker 05

  The storm stretches over 2,300 miles with more than 46.

• 1:04Speaker 06

  It is windy. It is pelleting.

• 1:06Speaker 07

  About half the United States population today shoveling, scraping, and slogging their way through heavy snow.

• 1:13Speaker 03

  Yeah, at least 29 states were affected by this storm. It knocked out electricity for hundreds of thousands of people. And listen, winter storms happen. It's a thing.

• 1:22Regina Barber?

  Yeah.

• 1:23Speaker 03

  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.

• 1:36Regina 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?

• 1:43Speaker 03

  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 scientist named Kevin Reed at Stony Brook University to talk about the storm. And he said this thing that was kind of interesting.

• 1:59Speaker 06

  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.

• 2:08Speaker 03

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

• 2:14Speaker 06

  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's system so that we can build better and better models.

• 2:31Regina 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 Short Wave from NPR.

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• 4:10Regina Barber?

  OK, 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?

• 4:26Speaker 03

  So really good computer models aren't 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 going to rain, put on a jacket.

• 4:38Regina 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.

• 4:43Speaker 03

  You're not like asking more questions and Googling it.

• 4:46Regina Barber?

  No, no.

• 4:46Speaker 03

  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, which sounds very fancy. And the weather forecasts that you see on your phone or on TV are usually based on a bunch of these models.

• 5:06Regina Barber?

  OK, so like an average of all these models?

• 5:08Speaker 03

  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. 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.

• 5:29Regina Barber?

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

• 5:35Speaker 03

  Exactly. And in real life, there is a lot going on in the atmosphere, as Kevin explained. The way we estimate clouds, for example, and the processes that go into them, and how that then links to wind and temperature. 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 is going to 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 going to be.

• 6:12Regina 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.

• 6:20Speaker 03

  Totally.

• 6:20Regina Barber?

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

• 6:24Speaker 03

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

• 6:32Regina Barber?

  Yes, that relates to my food intake, right?

• 6:36Speaker 03

  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.

• 6:51Regina Barber?

  Yeah.

• 6:52Speaker 03

  You need good data, and good data looks like plentiful, granular, continuous data. And I'll go through each one of those.

• 7:01Regina Barber?

  Okay.

• 7:01Speaker 03

  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.

• 7:37Regina Barber?

  Measurements from planes.

• 7:38Speaker 03

  Planes, 100%, measurements from ships in 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.

• 7:49Regina Barber?

  It can't stop.

• 7:50Speaker 03

  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.

• 8:19Regina 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.

• 8:41Speaker 03

  Yeah, I mean, it's not perfect.

• 8:43Regina Barber?

  Okay.

• 8:43Speaker 03

  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 they use all that data.

• 9:03Regina Barber?

  And where does all this data live?

• 9:05Speaker 03

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

• 9:10Regina Barber?

  It's happening, I can feel it.

• 9:11Speaker 03

  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.

• 9:19Regina Barber?

  Yeah, makes sense.

• 9:20Speaker 03

  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 might 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.

• 9:54Regina Barber?

  Oh, wow.

• 9:55Speaker 03

  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 lab. 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.

• 10:28Regina Barber?

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

• 10:37Speaker 03

  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 forecasts 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.

• 11:02Regina Barber?

  Wow. 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.

• 11:08Speaker 03

  If I have power.

• 11:09Regina Barber?

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

• 11:12Speaker 03

  Of course, you're welcome.

• 11:16Regina Barber?

  If you liked 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 4 Dallas-Fort Worth, and PBS NewsHour. I'm Regina Barber. Thank you for listening to Shore Wave from NPR.

• 12:05Speaker 01

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