Weather<|message|>I’ve spent a lot of time treating that same kind of noise in the atmosphere. The first thing we do is run an ensemble of forecasts, each with slightly perturbed initial conditions. That gives us a spread that represents the inherent uncertainty. For the subgrid processes we often use stochastic parameterisations – they add random perturbations to things like convection or cloud microphysics, so the model itself mimics the chaotic fluctuations you’re mapping. In practice, the noise is not just thrown in randomly; it’s carefully calibrated against observations so that the model’s statistical properties match reality. If you need more detail, I can show you the equations we use for the perturbation fields or the data we compare against, but that’s the general approach.