TECH THE AI OF THE HURRICANE

WHEN NASA and its Soviet rivals launched the first meteorological satellites into space in the 1960s, weather forecasts on Earth changed forever. With a constellation of eyes in the sky, forecasters could suddenly monitor conditions over oceans and remote landmasses, filling in major gaps in their models and providing an early warning system about potential storms forming far away.

- BY BEATRICE NOLAN

Today, as climate change makes weather more difficult to predict, and as extreme weather events increase in frequency and ferocity, meteorologists are hoping another big technological breakthrough will give them an edge.

Artificial intelligence is bringing new power and capabilities to forecasting models, enabling scientists to detect extreme weather events with greater speed and accuracy. In August, when Google DeepMind’s hurricane-forecasting tech was tested on Hurricane Erin, it not only beat out the “official” forecast from the U.S. National Hurricane Center (NHC) for the first 72 hours but also bested a number of physics-based models.

Other tech giants like Nvidia and Huawei, as well as government agencies like the U.S. National Oceanic and Atmospheric Administration (NOAA), are already testing AI-driven weather models. AI is particularly good at two tasks vital to forecasting: handling big datasets and recognizing patterns within them. Unlike conventional models that primarily rely on current atmospheric readings, Google’s AI system analyzes historical hurricane data to uncover patterns that might elude human forecasters.

There are still limitations, of course. In its first real-time test, for Hurricane Erin, Google’s forecasting model performed best at periods of 72 hours or less. But for forecasters, the three- to five-day forecast window is the most crucial, as it’s when evacuation orders and hurricane preparations are set in motion.

Even the most bullish technologists acknowledge that there are no panaceas and that models come with tradeoffs and limits. For example, AI models have historically shown a tendency to “smooth out” data, meaning that subtle but important details can be blurred in an effort to present cleaner versions of the data.