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Comment by keule | Hacker News Reader

IMO a chaotic system will not allow for long-term forecast, but if there is any type of pattern to recognize (and I would assume there are plenty), an AI/ML model should be able to create short-term prediction with high accuracy.

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Not an expert, but "Up to 10 days in advance" sounds like long-term to me ?

I think 10 days is basically the normal term for weather, in that we can get decent predictions for that span using "classical"/non-ML methods.

IDK, I wouldn't plan a hike in the mountains based on 10-day predictions.

To be clear: With short-term I meant the mentioned 6 hours of the article. They use those 6 hours to create forecasts for up to 10 days. I would think that the initial predictors for a phenomenon (like a hurricane) are well inside that timespan. With long-term, I meant way beyond a 14-day window.

But AI/ML models require good data and the issue with chaotic systems like weather is that we don’t have good enough data.

The issue with chaotic systems is not data, is that the error grows superlinearly with time, and since you always start with some kind of error (normally due to measurement limitations) this means that after a certain time horizon the error becomes to significant to trust the prediction. That hasn't a lot to do with data quality for ML models

That’s an issue with data: If your initial conditions are wrong (Aka your data collection has any error or isn’t thorough enough) then you get a completely different result.

Every measurement has inherent errors in it - and those errors are large if the task is to measure the location and velocity of every molecule in the atmosphere.

You also need to measure the exact amount of solar radiation before it hits these molecules (which is impossible, so we assume this is constant depending on latitude and time)

These errors compound (the butterfly effect) which is why we can't get perfect predictions.

This is a limit inherent in physical systems because of physics, not really a data problem.

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