In short, we were, are and will remain under the influence of a persistent high. The sun is shining in the mountains, while fog or high mist often hangs in the lowlands.

## High degree of inaccuracy in long-term weather forecasts

Such volatility and the high level of forecast uncertainty if you want to look more than a few days into the future can cause frustration in well-established omegas like the current one. We've resigned ourselves to the fact that it won't snow for a while anyway, but it would be nice if there was at least a reliable improvement in sight. Unlike Swiss weather oracles and their anthills, numerical meteorology has to contend with various limiting factors that make perfect forecasts impossible.

If we had any amount of computing power and a weather station on every molecule of the atmosphere, we would know how the northern congestion would turn out - or not. If you believe quantum physics, we would still reach the limits of predictability at some point. The so-called Heißenberg uncertainty principle states that it is not possible to simultaneously determine the position and momentum of a particle with arbitrary precision. On the one hand, this is negligible for large-scale processes; on the other hand, even minimal deviations in the initial conditions in chaotic systems eventually grow into the large scales, and the atmosphere is a (deterministically) chaotic system.

In fact, weather models naturally have initial value problems of incomparably larger proportions and therefore cannot even be sure what will happen in a week. Just as a true chaotic person finds everything in his room until his mother tidies up, there is also a certain order in mathematical chaos, which at least allows us to narrow down the number of possible future weather situations. In contrast to stochastic processes, the greater or lesser unpredictability of the weather is due to the fact that the initial conditions cannot be precisely determined, not because the "weather" process is not deterministic.

### Silent attractors - the Lorenz attractor

The temporal development of variables in chaotic systems follows certain patterns, known as attractors. Such attractors also exist in the modeling of turbulent air flows, albeit strange ones. A well-known strange attractor (yes, "strange attractor" is a technical term) is the Lorenz attractor, which Edward Lorenz discovered in 1963 while investigating a simple, idealized hydrodynamic system. He found that its variables developed in the form of a butterfly when he projected them into phase space. The proverbial flapping of butterfly wings that triggers a hurricane on the other side of the world can therefore also be found in the rather unpoetic world of chaos research. Perhaps there really is a small north jam sitting on one of the wings; after all, it would be within the realm of theoretical possibilities.