Severe heat waves and cold snaps are especially hard to forecast because atmospheric perturbations can have effects that are difficult to compute. Neural networks show promise where typical methods have stumbled.

What’s new: Researchers at Rice University used a capsule neural network — a variation on a convolutional neural network — to forecast regional temperature extremes based on far fewer variables than usual.

How it works: Good historical observations date back only to 1979 and don’t include enough extreme-weather examples to train a neural network. So the researchers trained their model on simulated data from the National Center for Atmospheric Research’s Large Ensemble Community Project (LENS).

  • Starting with 85 years’ worth of LENS data covering North America, the researchers labeled atmospheric patterns preceding extreme temperature swings by three days.
  • Trained on atmospheric pressure at 5 kilometers, the model predicted cold spells five days out with 45 percent accuracy and heat spells (which are influenced more by local conditions) five days out with 40 percent accuracy.
  • Retrained on both atmospheric pressure and surface temperature, the model’s five-day accuracy shot up to 76 percent for both winter and summer extremes.

The next step: By adding further variables like soil moisture and ocean surface temperature, the researchers believe they can extend their model’s accuracy beyond 10 days. That would help meteorologists spot regional temperature extremes well ahead of time. Then they would use conventional methods to home in on local effects.

Why it matters: Extreme temperatures are disruptive at best, deadly at worst. Advance warning would help farmers save crops, first responders save lives, and ordinary people stay safe.

Behind the news: Most weather forecasting is based on crunching dozens of variables according to math formulas. In its reliance on matching historical patterns, this study’s technique — indeed, any deep learning approach to weather prediction — is a throwback to earlier methods. For instance, the U.S. military used temperature and atmospheric pressure maps to predict the weather before the U.S. invasion of Normandy in 1944.

We’re thinking: Who says talking about the weather is boring?

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