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It takes a Herculean effort to keep pace with weather in an atmosphere constantly in flux. The task is so difficult and complex that a reliable forecast more than a couple of days in advance was unheard of just a few decades ago.

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Forecasts took another step forward this month, experts said, thanks to GenCast, a new artificial intelligence forecast model by Google’s DeepMind. Its forecasts through 15 dayswere significantly more accurate than one of the most well-respected traditional non-AI forecast models, according to a study published by DeepMind in the journalNature.

“It’s an impressive result,” said Peter Dueben, a machine learning expert and head of Earth system modeling at the European Centre for Medium-Range Weather Forecasts, home to the model bested by GenCast. “It’s a big step.”

GenCast isn’t ready for the public yet. It and other AI models still have a few key kinks to work out, particularly in forecasting the more frequent and severe weather of a warming world, before they change forecasting and save lives in the process.

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Early computers improved forecasting in the 1950s and 1960s, but it wasn’t until 1974 that the first model able to pull in data from around the globe and generate a rudimentary forecast became operational.

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But current forecast models still have limitations. The most robust ones can only be run every few hours because of how long it takes to crunch the complex calculations. They also demand a lot of computing power and energy that make them costly.

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“Once you have multiple possible futures it gives you a sense of both the range of what might happen and it also lets you calculate how likely some (futures) are rather than others,” according to Ilan Price, the lead author of the new study and a senior research scientist with DeepMind.

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nikhil adhesives limited (526159) stock rallies ✌️【Free Access to Stock Club】✌️ Free expert predictions on stock trends and real-time data to help you make informed decisions and grow your wealth steadily. The AI model produced more accurate forecasts than the ECMWF’s traditional model for more than 97% of these variables within a 15-day timeframe, but showed particular skill within the first week of forecasts.

It showed anywhere from a 10 to 30% accuracy improvement on forecasts in the three-to-five-day range, depending on the exact combination of variables tested, according to Price. GenCast also had more accurate forecasts than the ECMWF’s model up to 15 days in the future, the study said.

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“AI-based weather forecasting is ready for prime time,” Price added. “It’s ready to start being incorporated alongside… traditional models in operation.”

GenCast is not in operation yet, but the DeepMind team plans to take another step toward it by releasing its present-day forecasts and an archive of its past forecasts, according to Price.

GenCast is a critical advancement in modeling, but like any other weather forecast model, it isn’t perfect.

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“It turns out that actually these models are more robust to those extreme events than you would think,” Dueben said. The ECMWF has tested AI models against real-time weather for more than a year now and has seen improvements in their overall accuracy, even with extreme events, he explained.

But AI models can start inventing impossible-on-Earth physics the farther out in time they look, according to Dueben.

Other prediction issues remain, particularly with one of the most destructive weather phenomena: tropical cyclones.

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GenCast showed better skill than traditional models when predicting the tracks of tropical systems but struggled to accurately capture intensity, according to Price.

In part, that’s because some of the recent notablerecord-breaking systemsweren’t included in the 40 years of data GenCast was trained on, Price noted.

It’s an issue Price is “quite confident” can be overcome in the future as the model trains on more data.

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Each step forward with this nascent technology adds another tool human weather forecasters can use to craft accurate forecasts people rely on for almost every aspect of their lives.

“You can be as skeptical as you want against machine learning forecasts in principle,” Dueben said. “These models will make a positive impact on our weather predictions; there’s no question there.”

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