Using AI to predict the weather

Jeffrey Schrader, an environmental economist at Columbia University School of International and Public Affairs in New York, is the author of a recent PNAS article.

Schrader and his colleagues calculated the impact of day-ahead forecast accuracy on reducing heat-related mortality, finding that such forecasts improved by 34% between 2005 and 2023.

Schrader explains what 48 expert forecasters had to say about how predictions might further improve through 2100, including the potential role

of AI.

We surveyed operational meteorologists, so people who are out there every day creating the weather forecasts that you and I and lots and lots of folks know and love and look at on an almost daily basis.

What we found is basically they expect forecasts to keep getting better a little bit slower than they've been getting better in the past because they have been getting so much better.

But they do expect forecasts to get really a lot better over the coming decades.

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.

But

they

also raised some caveats around that.

One is the worry that some folks had that AI would cut humans out of the loop.

One thing that human meteorologists are really good at doing is keeping these algorithms grounded into reality.

All weather forecasts, whether they are AI generated, whether they're traditional physics-based forecasts, are fundamentally constrained by and the quality of those forecasts is based on the quality of the input data that goes into them.

Almost all of our experts talked about this as a really important potential downside risk.

If we were to step back from investing in monitoring the weather, generating really high quality weather measurements, then that would not only hurt our current forecasting infrastructure, it would hurt any possible future AI forecast.