> Honestly I'd be surprised if we weren't already using ML in weather models. Statistical modeling has long been used for "post-processing" the raw, gridded ou

> Honestly I'd be surprised if we weren't already using ML in weather models. Statistical modeling has long been used for "post-processing" the raw, gridded outputs of numerical models. This performs fairly well, and it is intended to correct for local factors that are not observable at the scale of the underlying model. For example, local topology might channel winds into a few primary directions, but a O(100m)-scale valley is too fine to show up in the large-scale grid of a global weather model. You can also consider data assimilation to be a form of machine learning. ECMWF's main, traditional weather model essentially uses a form of backpropagation to find the Jacobian of observation values with respect to the input weather state, and from there it's straightforward (but far from simple) to optimize the weather system. Other centres take related approaches, such as ensemble Kalman filtering (when the underlying model is not so easily differentiated with respect to its inputs). The remarkable product of the last year or two has been the discovery that full, relatively unconstrained ML models can do a good job at the core NWP prediction task. It was not at all obvious that the historic record (the ERA5 reanalysis, mostly: https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5 https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-...) was large enough for pure data-driven models to learn appropriate responses.