Ask HN: Why are we still so bad a weather forecasting?

Perhaps you've heard of the "Butterfly Effect" but not a formal definition of the term.

For the issue of predicting weather, the model of weather is "sensitive". The observation metaphor of the "butterfly effect" is that relatively small perturbations to the system have the observed effect of being indistinguishable between relatively large perturbations. We may substitute "small" with "deterministic" and "large" with "non-deterministic".

A summary of this statement would be that initial or past conditions of the system poorly characterize the dynamic and chaotic behavior of nonlinear dynamic systems.

The phrase is drawn from the title of the paper by EN Lorenz:

https://static.gymportalen.dk/sites/lru.dk/files/lru/132_kap...

Somber note: Weather is in fact, becoming predictable, precise and accurate: towards chaos, volatility and instability. Measurement and policy do not adequately address the ongoing issue of climate "change", which may more adequately be defined as climate chaos.

I had a meteorologist explain this to me once: weather forecasting is highly complex, near to an art form. It's not yet possible to model the forecast with precise enough rules that it can be done by machine within a reasonable compute budget. In addition, as others have noted, variations in topography can create many microclimates, so that the forecast for a given region is actually the average forecast for all the area within that region.

Very accurate forecasts for a specific spot are possible, but today require a trained meteorologist. In practice, the only time people are willing to pay for this expertise are when they're running an airport, planning a war, or launching / landing a spacecraft.

Perhaps in the future we'll have enough compute power on demand for anyone to accurately model on-demand forecasts for specific spots.