this post was submitted on 04 Dec 2024
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106
DeepMind's GenCast AI is really good at forecasting the weather (www.engadget.com)
submitted 3 weeks ago by [email protected] to c/[email protected]
39 comments fedilink hide all child comments
 

Pretty much the only thing I think AI could be useful for - forecasting the weather based off tracking massive amounts of data. I look forward to seeing how this particular field of study is improved.

Bonus points, AI weather modeling, for once, saves energy relative to physics models. Pair it with some sort of light weight physical model to keep the hallucinations at bay, and you've got a good combo.

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[–] [email protected] 11 points 3 weeks ago* (last edited 3 weeks ago) (2 children)

Absolutely, but training is only once, being so efficient to make the actual forecast, you could have a forecast personally made for your own garden, which may be very different than a generic one covering hundreds of km². Then the about 90% accuracy will feel WAY more accurate.

[–] [email protected] 6 points 3 weeks ago (1 children)

I feel this personally, I live in the hills outside of a valley metro. All weather data is forecasted off of valley sensors, but shit gets weird when you suddenly climb 2000+ ft.

The best weather services in my area are those that can factor in peoples household meters into their forecasting, but those services still aren't perfect.

[–] [email protected] 1 points 3 weeks ago

I live in a hilly county in a country at the intersection of two weather cells, with a warm ocean current bathing our coast. Prediction in those conditions is a real challenge. For example, my neighbors 50 metres from me get consistently more snow and ice than I do. More stations would really help, but moving from there to crowd-sourced forecasting has issues due to lack of calibration and other biases. It can help, but not as much as you might think.

[–] [email protected] 3 points 3 weeks ago

The non-AI models in use now all get feedback on each run from actual observations, that's used to correct model parameters for later runs.