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whelp... Ok, how would you articulate an understanding of how the weather works? Let's face it, it's not going to be easy to frame in natural language...
You can take a meteorology course that explains how the weather works. That course is the articulation.
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>Great, so it's using historical data to make a prediction about what has already happened. That's ok though because it doesn't know it's acting on old data. Researchers would give it data and ask for the 15 day forecast, the researchers then compare against that real world data. And as noted, "it surpassed the centres forecast in more than 97 percent" of those tests. This is all referred to as backtesting.
It's still not as good as actually new data. The individual model may not overfit the existing data, but the whole system of researchers trying lots of models, choosing the best ones, trying different hyperparameters etc. easily can.
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It's still not as good as actually new data. The individual model may not overfit the existing data, but the whole system of researchers trying lots of models, choosing the best ones, trying different hyperparameters etc. easily can.
yeah, but weather patterns data isn't different now than 10 years ago, right? right? ...
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>Great, so it's using historical data to make a prediction about what has already happened. That's ok though because it doesn't know it's acting on old data. Researchers would give it data and ask for the 15 day forecast, the researchers then compare against that real world data. And as noted, "it surpassed the centres forecast in more than 97 percent" of those tests. This is all referred to as backtesting.
I wish these sort of papers would focus more on the 3 percent that it got wrong. Is it wrong by saying that a day would have a slight drizzle but it was actually sunny all day, or was it wrong by missing a catastrophic rain storm that devastated a region? I've worked on several projects trying to use AI to model various CFD calculations. We could trivially get 90+ percent accuracy on a bunch of metrics, the problem i…
This is great from a practical standpoint (being able to predict weather), but does it actually improve our understanding of the weather, or WHY those predictions are better? That is my issue with some of these AI advances. With these, we won't have actually gotten better at understanding the weather patterns, since it's all just a bunch of weights which nobody really understands.
We can work backwards from correct predictions and develop an understanding that way. But most people just need to know if it’s going to be storming, hot or, going to rain on a given day and that is where this shines.
I'm friends with a meteorologist and the 15+ day forecast is the bane of their existence because you can't accurately forecast beyond a week so I would love to know how they are measuring accuracy. The article doesn't say and I know the paper is going to go over my head.
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We can work backwards from correct predictions and develop an understanding that way. But most people just need to know if it’s going to be storming, hot or, going to rain on a given day and that is where this shines.
We can always work backwards, regardless of AI.
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We can work backwards from correct predictions and develop an understanding that way. But most people just need to know if it’s going to be storming, hot or, going to rain on a given day and that is where this shines.
We can always work backwards, regardless of AI.
OC was saying (I’m going to paraphrase) that this is the death of understanding in meteorology, but it’s not because we can always work backwards from accurate predictions.