Weather forecasting is having an AI moment

From Technology Review.com (July 11, 2023):

Is it hot where you are? It sure is here in London. I’m writing this newsletter with a fan blasting at full power in my direction and still feel like my brain is melting. Last week was the hottest week on record. It’s yet another sign that climate change is “out of control,” the UN secretary general said.

Punishing heat waves and extreme weather events like hurricanes and floods are going to become more common as the climate crisis worsens, making it more important than ever before to produce accurate weather forecasts. 

AI is proving increasingly helpful with that. In the past year, weather forecasting has been having an AI moment.

Three recent papers from Nvidia, Google DeepMind, and Huawei have introduced machine-learning methods that are able to predict weather at least as accurately as conventional methods, and much more quickly. Last week I wrote about Pangu-Weather, an AI model developed by Huawei. Pangu-Weather is able to forecast not only weather but also the path of tropical cyclones. Read more here.

Huawei’s Pangu-Weather, Nvidia’s FourcastNet, and Google DeepMind’s GraphCast, are making meteorologists “reconsider how we use machine learning and weather forecasts,” Peter Dueben, head of Earth system modeling at the European Centre for Medium-Range Weather Forecasts (ECMWF), told me for the story.

ECMWF’s weather forecasting model is considered the gold standard for medium-term weather forecasting (up to 15 days ahead). Pangu-Weather managed to get comparable accuracy to the ECMWF model, while Google DeepMind claims in an non-peer-reviewed paper to have beat it 90% of the time in the combinations they tested.

Using AI to predict weather has a big advantage: it’s fast. Traditional forecasting models are big, complex computer algorithms based on atmospheric physics and take hours to run. AI models can create forecasts in just seconds.

But they are unlikely to replace conventional weather prediction models anytime soon. AI-powered forecasting models are trained on historical weather data that goes back decades, which means they are great at predicting events that are similar to the weather of the past. That’s a problem in an era of increasingly unpredictable conditions.

We don’t know if AI models will be able to predict rare and extreme weather events, says Dueben. He thinks the way forward might be for AI tools to be adopted alongside traditional weather forecasting models to get the most accurate predictions.

Big Tech’s arrival on the weather forecasting scene is not purely based on scientific curiosity, reckons Oliver Fuhrer, the head of the numerical prediction department at MeteoSwiss, the Swiss Federal Office of Meteorology and Climatology.

Our economies are becoming increasingly dependent on weather, especially with the rise of renewable energy, says Fuhrer. Tech companies’ businesses are also linked to weather, he adds, pointing to anything from logistics to the number of search queries for ice cream. 

The field of weather forecasting could gain a lot from the addition of AI. Countries track and record weather data, which means there is plenty of publicly available data out there to use in training AI models. When combined with human expertise, AI could help speed up a painstaking process. What’s next isn’t clear, but the prospects are exciting. “Part of it is also just exploring the space and figuring out what potential services or business models might be,” Fuhrer says. [read more]

It seems that AI is affecting every aspect of life.