49. Weather Forecasts

Summary

The Economics of Everyday Things – Episode 49: Weather Forecasts

Host: Zachary Crockett
Date: June 18, 2026

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Episode Overview

In this episode, Zachary Crockett delves into the economic and practical importance of weather forecasting. The show explores who creates weather predictions, how forecasts are made, the booming business behind the data, and the ever-growing influence and limitations of technology (including AI). Through stories from live events and insights from leading experts, Crockett reveals both the reliability and uncertainty woven into the weather predictions on which so many industries (and everyday people) depend.

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Key Discussion Points & Insights

1. Weather’s Impact on Major Events and Industries

• Case Study: Beyoncé’s Concert Disruption
  • In August, 50,000 people attending Beyoncé’s Renaissance World Tour near D.C. experienced severe delays due to a storm. Event organizers kept people in the concourses, but the stifling heat led to dehydration and fainting.
  • Steve Edelman (Event Safety Alliance): “The only thing they didn’t account for is that… putting thousands upon thousands of people in the concourses becomes a health and safety disaster because people got dehydrated and started fainting.” [01:45]
• Broader Economic Stakes
  • Weather forecasts affect decision-making for live event planners, airlines, retailers, farmers, and more. Edelman underscores the downside risk: “The downside risk of leaving everyone in harm’s way when there’s lightning in the forecast is extremely bad...” [02:28]

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2. The Science and History of Forecasting

• Path to Meteorology
  • Peter Neely’s journey: Inspired by childhood weather events, now Director of Weather Forecasting Sciences and Technologies at The Weather Company (parent of The Weather Channel, weather.com, etc.) [02:58–03:19]
  • “You’ll see this kind of thing time and time again for those in our profession… They just sort of knew it.” [02:58]
• Forecasting Process
  • Data is gathered from a vast network of sensors and tools (measuring humidity, temperature, barometric pressure, etc.)—advancements since the 19th century have enabled vast improvements in weather prediction.
• Technological Progress
  • Peter Neely: “The forecast for five days in advance today is about as accurate as the forecast for three days in advance 20 years ago.” [05:20]
  • Growth in supercomputing power and more sophisticated models have steadily refined forecasts.

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3. The Business of Weather

• The Weather Company’s Reach:
  • Trusted by 300 million+ people monthly, recently acquired for >$1 billion.
  • “There’s a good chance if you’re watching six o' clock news tonight, you’re seeing a weather show which is largely produced by The Weather Company...” [06:15]
• Commercialization of Data Gathering
  • Private enterprise now launches its own satellites for more (and better) atmospheric data. Satellite costs have plunged, making participation viable for businesses.
  • The global weather forecasting market is valued at over $2 billion (includes subscriptions, apps, advertising, and special services).

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4. High-Stakes Forecasting: The Airline Industry

• Meteorologists are embedded with major airlines, helping dispatchers decide, for example, when to add extra fuel to flights based on predicted thunderstorms.
  • Peter Neely: “Should we put a little bit more fuel on this flight? Because when the flight gets to New York, there might be thunderstorms in the area… If we put a little bit more fuel on that flight, the chances that it might actually need to be diverted… go down.” [11:37]

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5. Limits of Accuracy and the Role of Chaotic Systems

• Unpredictability and Model Gaps
  • “We fundamentally don’t know the temperature five miles above the ground out over the South Pacific Ocean right now. We can make a good guess… There’s just this little gap between how good we are now and the theoretical limit...” —Peter Neely [06:35]
• Atmospheric Stability
  • The atmosphere’s chaotic nature means that small errors can lead to major differences in outcomes—especially for phenomena like thunderstorms. [12:34]

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6. The Rise of AI in Weather Prediction

• Artificial Intelligence as a Gamechanger
  • AI can detect patterns human meteorologists might miss—even outpacing established government models.
    • “Artificial intelligence has hit the scene only in the last two or three years or so, and the progress has been nothing short of stunning.” —Peter Neely [13:27]
  • Google’s AI-driven prediction model recently outperformed conventional models. [13:38-14:00]
  • “There’s so much enthusiasm for how much this approach can bring to our ability to forecast weather.” —Peter Neely [14:00]

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7. Challenges from Climate Change

• Historical Models Under Strain
  • New, unstable atmospheric conditions mean that models built on historical data can falter.
    • “It’s harder to make an accurate prediction because the models are all based on historical data, but the historical data is based on pre climate change circumstances.” —Steve Edelman [14:38]
  • Lloyd’s of London warns global economic losses from extreme weather could exceed $700 billion in five years. [14:09]
  • Faster, more violent storms increase evacuation and safety risks. [15:00]
    • “Even the weather trigger charts that we rely on… That 30 minutes may now be 27 minutes. Because of climate change…” —Steve Edelman [15:00]

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8. Current Forecast Accuracy and Public Perception

• Remarkable Gains, Persistent Uncertainty
  • Although weather apps are now highly accurate for daily use, certainty is never 100%.
    • “Whenever my family asks me about what the weather forecast is, I always give them the standard answer: partly cloudy, chance of showers.” —Peter Neely [15:58]
  • In daily life, you can generally trust your phone—just not absolutely.

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Memorable Quotes & Moments (with Timestamps)

• “The downside risk of leaving everyone in harm’s way when there’s lightning in the forecast is extremely bad…” —Steve Edelman [02:28]
• “The forecast for five days in advance today is about as accurate as the forecast for three days in advance 20 years ago.” —Peter Neely [05:20]
• “There’s a good chance if you’re watching six o’ Clock News tonight, you’re seeing a weather show which is largely produced by The Weather Company, or at least using the technologies and data from the Weather Company.” —Peter Neely [06:15]
• “Artificial intelligence has hit the scene only in the last two or three years or so, and the progress has been nothing short of stunning.” —Peter Neely [13:27]
• “It’s harder to make an accurate prediction because the models are all based on historical data, but the historical data is based on pre-climate change circumstances.” —Steve Edelman [14:38]
• “Whenever my family asks me about what the weather forecast is, I always give them the standard answer. Partly cloudy, chance of showers.” —Peter Neely [15:58]

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Timestamps for Important Segments

• [00:58–01:45] Beyoncé concert and live event weather risks
• [02:58–03:27] Peter Neely’s origin and The Weather Company’s brands
• [04:08–05:20] From historical weather measurement to computer modeling
• [06:15–06:35] The Weather Company’s business success and model limitations
• [09:22–10:33] Growth in commercial/private weather data collection
• [11:37–12:15] Meteorologists working in aviation
• [13:27–14:09] Artificial intelligence’s impact on weather prediction
• [14:38–15:36] Challenges of climate change for accurate predictions
• [15:58–16:09] Public perception and weather forecast realities

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Summary

“The Economics of Everyday Things” shines a light on the critical, complex, and surprisingly high-stakes world of weather forecasting, from live events and aviation to the role of big data, business, and technology. Weather predictions underpin billions of dollars in global activity, and their accuracy keeps improving—though not without new challenges from climate change and the fundamental unpredictability of complex systems. Above all, the episode reminds us: on most days, your weather app is probably right—but never bet on 100% certainty.

Transcript

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[Zachary Crockett]

Last August, more than 50,000 people made their way to a stadium in the suburbs outside of Washington, D.C. they were there to see Beyonce during her Renaissance World Tour, but Mother Nature wasn't making it easy.

[Steve Edelman]

There was a storm front rolling in. People were waiting outside, the organizers said. You don't want to go out to the seats where you're exposed to lightning strikes. Stay in the concourse.

[Zachary Crockett]

That's Steve Adelman, vice president of the Event Safety Alliance. He's a sports and entertainment lawyer who works on live events, and he spends a lot of time thinking about how things could go wrong. His biggest concern is usually the weather.

[Steve Edelman]

The organizers of the Beyonce concert, the only thing they didn't account for is that on a hot, steamy August night in the D.C. area, putting thousands upon thousands of people in the concourses becomes a health and safety disaster because people got dehydrated and started fainting in the concourses.

[Zachary Crockett]

Edelman works in just one of the many industries that hinge on weather Live event planners, airlines, retailers, farmers, they all have to plan ahead and make weather related decisions because as it turns out, there's a lot at stake.

[Steve Edelman]

When is the storm likely to arrive? What is the nature of the storm? The downside risk of leaving everyone in harm's way when there's lightning in the forecast is extremely bad for the Freakonomics radio network.

[Zachary Crockett]

This is the economics of everyday things. I'm Zachary Crockett. Today, weather forecasts from the time he was eight years old, Peter Neely knew exactly what he wanted to be when he grew up.

[Peter Neely]

You'll See this kind of thing time and time again for those in our profession. You know, they were five years old and they saw a tornado at their grandmother's house, or they experienced a big blizzard and they just sort of knew it.

[Zachary Crockett]

Young Peter Neely was right. He's now director of weather forecasting sciences and technologies for the weather company.

[Peter Neely]

We're probably best known by some of our brands, like the Weather Channel, weather.com, weather Underground.

[Zachary Crockett]

Predicting the weather requires a number of skills.

[Peter Neely]

First of all, fundamentally understanding the physics of how things work. Like hot air rises. Well, how much will it rise and how quickly will it rise? How does a raindrop form? Will it stay in the cloud, or will it fall out of the cloud and become precipitation? That's all sort of governed by details, details of what we call the cloud physics. We then can embody those details of the physics in algorithms that describe the evolution of the atmosphere based on a current state of the atmosphere.

[Zachary Crockett]

To start, meteorologists read the current state of the atmosphere from a huge host of observational tools that continually record data. We've had instruments to measure humidity, temperature, and barometric pressure for more than 500 years. But it wasn't until the 19th century that organized weather observation networks were formed across the United States. Over the following decades, tools like the telegraph, the radiosonde, and radar allowed meteorologists to collect a wide range of data and detect patterns that could create a forecast. By the 1940s, it started to become clear that making accurate predictions required a lot of fast calculations, making it the perfect task for a computer.

[Peter Neely]

And over the last 75 years, since the advent of supercomputers, they've just incrementally gotten better and better.

[Zachary Crockett]

Supercomputers digest all that data and feed it into algorithmic models crafted by scientists. The meteorologists then interpret the output to predict what is most likely to happen next in our atmosphere.

[Peter Neely]

The precision by which we can run these models increases as there's more computer capacity going on, the science that's in the models gets better. And so our forecasts have gotten better by about a day per decade. And what that means is the forecast for five days in advance today is about as accurate as the forecast for three days in advance 20 years ago.

[Zachary Crockett]

That improvement has meant good business for Neely and his colleagues. In 2023, a morning consult survey put the weather company in the top 10 most trusted brands, alongside UPS and Kleenex. It serves more than 300 million people each month through its digital properties alone, and it was recently acquired by a private equity firm for more than a billion dollars.

[Peter Neely]

There's a good chance if you're watching six o' Clock News tonight, you're seeing a weather show which is largely produced by the Weather Company, or at least using the technologies and data from the Weather Company.

[Zachary Crockett]

Yet when it comes to predictions, even the Weather Company can't account for everything.

[Peter Neely]

We fundamentally don't know the temperature five miles above the ground out over the South Pacific Ocean right now. We can make a good guess of it, but we fundamentally don't know it. And we'll never be able to know what the temperature and the precipitation, the moisture and everything is at every place all the time. There's just this little gap between how good we are now and the theoretical limit to how good we ever could be.

[Zachary Crockett]

Lots of folks are trying to narrow this gap, but can they? That's coming up. Hey Fidelity, how can I remember to invest every month?

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[Zachary Crockett]

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[Peter Neely]

Yeah, I do.

[Zachary Crockett]

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[Peter Neely]

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[Zachary Crockett]

Interest in the weather has never been greater, which means there's money to be made. The global market for forecasting services has recently been valued at over $2 billion. That includes things like subscriptions, apps, advertising, revenue from weather stations, and special weather services purchased by businesses. Wall street has taken an interest in weather, too. There's now a $25 billion market built around buying and selling weather derivatives, Basically bets on future weather patterns within a certain period of time. Yet up until the last decade, the mightiest tools for gathering weather data, like satellite and radar systems, belonged mainly to government agencies. The weather company's Peter Neely says that's been changing

[Peter Neely]

increasingly in the last 10 years or so. There's been an advent of commercial enterprises deploying their own weather observing equipment, and oftentimes it's some new technique that will provide us a different way to observe or learn some more about what the atmosphere is.

[Zachary Crockett]

Private companies have been able to launch satellites into space to measure atmospheric conditions, all because the cost of doing so has dropped significantly over the last few decades, from hundreds of millions of dollars to around 5 million. In some cases, all those satellites have been contributing mountains of additional data, which helps increase the accuracy of forecasts. For these companies, the cost is worth it.

[Peter Neely]

A study a couple decades ago estimated that over 30% of the total US economy is dependent on the weather in some form or other. You know, agriculture, foundationally dependent on the weather, Transportation is disrupted by weather events. A lot construct and onward and onward. There's a lot of ingrained dependency in the weather.

[Zachary Crockett]

Few fields are more dependent on weather forecasting than the aviation industry, where expensive decisions are made on a minute by minute basis.

[Peter Neely]

Certainly at the weather company, we have meteorologists who are embedded inside some of the major airlines around the country sitting next to dispatchers and helping them make decisions. For example, should we put a little bit more fuel on this flight? Because when the flight gets to New York, there might be thunderstorms in the area which will cause the flight to need to circle around for a little bit because of delays of arrivals into the airport. And if we put a little bit more fuel on that flight, the chances that it might actually need to be diverted to an alternative airport go down.

[Zachary Crockett]

When it comes to the airlines, it's easy to see the value of ever more precise weather predictions. So forecasters find ways to measure how confident they can be in their Forecasts. One big factor is what's known as atmospheric stability, which is notoriously hard to measure.

[Peter Neely]

Yeah, that gets a bit tricky, and it has to do with the chaotic nature of our atmosphere. When it's unstable, little differences at the beginning translate into big differences at the end. The thunderstorm is a representation of a very unstable atmosphere. And so if we don't get the temperatures exactly right, we may not know whether or not the atmosphere is going to be stable or unstable.

[Zachary Crockett]

According to the national oceanic and atmospheric administration, since 1980, the US has seen 383 weather events with damages exceeding a billion dollars. And so meteorologists are increasingly turning to another tool to help individuals and businesses try to make better decisions when it comes to weather.

[Peter Neely]

Artificial intelligence has hit the scene only in the last two or three years or so, and the progress has been nothing short of stunning.

[Zachary Crockett]

AI tools have been able to spot patterns that might elude human meteorologists. In some cases, AI can do this before anyone even understands the underlying science. A study was published late last year on an AI powered weather prediction model built by Google, which outperformed government models that have existed for decades.

[Peter Neely]

There's so much enthusiasm for how much this approach can bring to our ability to forecast weather.

[Zachary Crockett]

A new factor, however, is making prediction harder, and it's one that AI and supercomputers can't fully account. Climate change forecasts that rely on historical data can struggle as the atmosphere becomes more unstable than ever before. Last year, insurance marketplace Lloyds of London said that global economic losses due to extreme weather events could top $700 billion in the next five years.

[Steve Edelman]

Every professional meteorologist has told us it's harder to make an accurate prediction because the models are all based on historical data, but the historical data is based on pre climate change circumstances.

[Zachary Crockett]

Again, that's live events attorney Steve Edelman.

[Steve Edelman]

Storms rise faster now than they used to. They become more violent more quickly now than they used to. And so even the weather trigger charts that we rely on to say, all right, we have 30 minutes to evacuate the house before the storm arises. That 30 minutes may now be 27 minutes. Because of climate change, temporary structures generally have wind ratings, but they were designed for a different climatic world than the one that we're living in now.

[Zachary Crockett]

Despite all of this chaos, the amazing thing is that weather forecasting is more accurate than ever. And in general, you can feel confident that the weather you'll encounter on an average day will look a lot like what your weather app predicts, as long as you don't expect 100% certainty.

[Peter Neely]

Whenever my family asks me about what the weather forecast is, I always give them the standard answer. Partly cloudy, chance of showers.

[Zachary Crockett]

For the economics of everyday things, I'm Zachary Crockett. This episode was produced by Julie Kanfer and Sarah Lilly and mixed by Jeremy Johnston. We had help from Daniel Moritz Rapson.

[Peter Neely]

Are the clouds 1,000ft above the ground or 2,000ft above the ground? It's just clouds to us. The Freakonomics Radio Network the Hidden side of Everything

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[Peter Neely]

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[Peter Neely]

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[Zachary Crockett]

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[Peter Neely]

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[Zachary Crockett]

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