Meteorologists have been working on this problem for a long time, and have thought of and tried every obvious idea (and many more). Weather prediction is literally where modern chaos theory started: https://en.wikipedia.org/wiki/Chaos_theory#History:~:text=Hi...
I have no particular insights about weather data or models, but I want to note that some prediction problems are just not limited by historical data (as you seem to assume implicitly). Consider the simplest example of a coin toss: the outcome of the next toss in unpredictable no matter how much historical coin toss data you collect and no matter how sophisticated your AI/ML model. (The only thing you can predict is the fraction or amount of heads/tails in a large batch of coin tosses, and you don't need much historical data for that.)
This should give you a hint that it's not as simple as having enough weather data.
You are trying to predict a chaotic weather system that is impacted by everything from sea temperatures, sun activity, wind, volcanoes, bushfires, atmosphere changes etc. Much of which we don't even have enough data for or a complete understanding of.