How AI Can Make Weather Forecasting Better and Cheaper

How AI Can Make Weather Forecasting Better and Cheaper (



from the meteorologists-of-the-future dept.

An anonymous reader quotes a report from Bloomberg: In early February a black box crammed with computer processors took a flight from California to Uganda. The squat, 4-foot-high box resembled a giant stereo amp. Once settled into place in Kampala, its job was to predict the weather better than anything the nation had used before (Warning: source may be paywalled; alternative source). The California startup that shipped the device, Atmo AI, plans by this summer to swap it out for a grander invention: a sleek, metallic supercomputer standing 8 feet tall and packing in 20 times more power. “It’s meant to be the iPhone of global meteorology,” says Alexander Levy, Atmo’s co-founder and chief executive officer. That’s a nod to Apple’s design cred and market strategy: In many countries, consumers who’d never owned desktop computers bought smartphones in droves. Similarly, Atmo says, countries without the pricey supercomputers and data centers needed to make state-of-the-art weather forecasts — effectively, every nation that’s not a global superpower — will pay for its cheaper device instead.

For its first customer, though, the Uganda National Meteorological Authority (UNMA), Atmo is sending its beta version, the plain black box. Prizing function over form seems wise for the urgent problem at hand. In recent years, Uganda has had landslides, floods, and a Biblical plague of locusts that devastated farms. The locusts came after sporadic drought and rain, stunning officials who didn’t anticipate the swarms. “It became an eye-opener for us,” says David Elweru, UNMA’s acting executive director. Many nations facing such ravages lack the most modern tools to plan for the changing climate. Atmo says artificial intelligence programs are the answer. “Response begins with predictions,” Levy says. “If we expect countries to react to events only after they’ve happened, we’re dooming people to disaster and suffering.” It’s a novel approach. Meteorology poses considerable challenges for AI systems, and only a few weather authorities have experimented with it. Most countries haven’t had the resources to try.

Ugandan officials signed a multi-year deal with Atmo but declined to share the terms. The UNMA picked the startup partly because its device was “way, way cheaper” than alternatives, according to Stephen Kaboyo, an investor advising Atmo in Uganda. Kaboyo spoke by phone in February, Kampala’s dry season, as rain pelted the city. “We haven’t seen this before,” he said of the weather. “Who knows what is going to happen in the next three seasons?” […] Atmo reports that its early tests have doubled the accuracy scores of baseline forecasts in Southeast Asia, where the startup is pursuing contracts. Initial tests on the ground in Uganda correctly predicted rainfall when other systems didn’t, according to UNMA officials.

10.0 times 0.1 is hardly ever 1.0.


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