Eli5: Is weather *truly* predictable? Is it possible to develop a scientific model supported by satellites, software/ai, and super computers that could predict specific storms and events months/years/decades in advance?

Question

Elephant in the room: There are factors like volcanos and human involvement that create unpredictable variables

As above. And let me lead with this: I’m a smart guy, but i know little about meteorology. My question is grounded in ignorance.

Right now we can predict with relative accuracy storms and events 3-7 days out, depending on the scope of the storm. Temperatures are even ‘easier’.

It seems like we’re close (if not already there) to having global weather data at all times. We live in a world of scientific order, so shouldn’t we be able to create a model that very accurately predicts specific weather on a long timeline?

Exa: I’m ignorant in meteorology, but I would think all of the fronts and movements that will lead to a hurricane in the Atlantic in 2025 are already in motion. Couldn’t we track that *now*? Play out the equation, so to speak?

And then we’d be able to reverse engineer the moments that disrupted the model, to accurately determine what artificially affected the weather pattern.

Thanks! And sorry if I’m dumb.

In: Planetary Science

Answer 1

In 1961, a meteorologist called Edward Lorenz began using computer simulations to try to predict the dynamics of the atmosphere. He created a system of interrelated equations that described properties of air motion and temperature. He then iterated the equation to obtain the atmospheric conditions at the next time step. These conditions were then fed back into the equations to obtain the conditions at the next time step.

When he found an interesting pattern in the data for certain initial conditions, he ran the simulation with the same initial conditions and got a completely different result. He expected to reproduce the same result because his equations were deterministic, i.e. not random but reproducible.

The source of the difference turned out to be a rounding error in the last few digits of one of the input numbers. He had entered 0.486 instead of the full 0.486134 which was the original starting value. He had assumed that such a small difference or error would not be significant and would disappear in the calculations. But instead of disappearing, the error grew with each iteration until the result was completely different from the original answer.

This is why weather models are so difficult. Google “butterfly effect” and “chaos theory”.