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)

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.

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]

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]

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]

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.

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.

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.

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)

Episode Overview

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]

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]

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]

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.

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.

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.

wavePod

How scientists predict big winter storms

Summary

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

Episode Overview

Key Discussion Points & Insights

1. The Impact of the Recent Winter Storm

2. Advances in Winter Storm Prediction

3. How Weather Models Work

4. What Makes a Good Weather Model?

5. The Importance of Past and Current Data

6. Threats to Data Collection and Future Forecasting

Notable Quotes & Memorable Moments

Important Segment Timestamps

Tone and Language

Summary Takeaways

Transcript

Summary

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

Episode Overview

Key Discussion Points & Insights

1. The Impact of the Recent Winter Storm

2. Advances in Winter Storm Prediction

3. How Weather Models Work

4. What Makes a Good Weather Model?

5. The Importance of Past and Current Data

6. Threats to Data Collection and Future Forecasting

Notable Quotes & Memorable Moments

Important Segment Timestamps

Tone and Language

Summary Takeaways