For one thing, can you just set us straight on the role of the federal government in the US versus the private sector when it comes to forecasting?

The federal government does a lot when it comes to forecasting, but historically, there has been a fairly clear division of labor between the, uh, public and private sector here.

So one thing that the federal government plays a really important role in is just gathering data on weather and certifying that data, making sure that data is of high quality, and this is the essential input into any weather forecast, public or private.

So they run weather stations, they launch, uh, weather balloons, things like that.

They, you know, of course, control and launch satellites that, that give us a lot of our weather data.

So there's the data gathering part.

There is the research and development of forecast model, uh, side of things, which the public sector is very heavily involved in, but the private sector does some of as well.

Can you pick out a couple things you think is, people might want to know about?

In

the last couple of decades, there's been a particular relationship between mortality and forecast errors, and that relationship really shows that forecast errors are especially deadly on hot days.

In our central scenario, something like 18% of heat-related mortality is eliminated by expected improvements in forecasts by the end of this century.

As climate change makes hot days more common, forecasts are going to become more important.

Under different climate change scenarios, you see that forecasts will help prevent even more deaths from heat.

That number increases by up to 75% under even moderate climate change warming scenarios.

Most of the experts that we talked to thought that AI and machine learning did hold promise for making forecasts better.

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