: 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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There was a book about 35 years ago called Chaos by James Gleick that explains it well. Weather systems demonstrate a classic feature of chaotic systems: sensitive dependence on initial conditions. What that means is that tiny variations in the initial conditions can greatly change the outcomes over time. This is commonly referred to as the butterfly effect. The butterfly effect is derived from the metaphorical example of the details of a tornado (the exact time of formation, the exact path taken) being influenced by minor perturbations such as a distant butterfly flapping its wings several weeks earlier.
The problem with chaotic systems is that they are so sensitive to tiny changes in initial conditions that making predictive models becomes extremely difficult. They are difficult because they become less
and less accurate when you attempt to simplify them. All models are by definition simplifications of real world system. You must simplify them because your datasets don’t contain everything weather systems react to and you can’t factor in all the tiny seemingly insignificant variables that affect outcomes.
This idea of chaotic systems does have some somewhat stunning implications. For example, it’s easier to predict where mars will be relative to the Earth and Sun in 100 years than the exact path a single rain drop will take as runs down a pane of glass.
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