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

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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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