: With the incredible technology that we have today, why is it still impossible to have 100% accuracy on predicting the weather?
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The weather models use non-linear partial differential equations. This leads to a host of problems. First of all, they are really sensitive to initial conditions. Secondly, we only have so many places taking measurements as input, but in theory we would need an infinite number of measurements. Thirdly, most have no closed form solution, so you to grind through them one little bit of volume at a time. So computationally, they are expensive.
Plus, some of the behavior is chaotic, meaning that small changes in inputs can have really large changes in outputs.
Bottom line, modeling the weather is a very difficult problem, both theoretically, and practically.
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