In Australia, meteorologists used to be deployed across the country to local offices and would receive computer-generated forecast models (and other raw data) whenever the supercomputer at headquarters had finished running a job. The local meteorologists would then be allowed to apply their local knowledge to adjust the computer-generated forecast.

This was (and still is) particularly important in situations such as:

* Fast moving weather systems of high volatility, such as fire weather systems coupled with severe thunderstorms.

* Rare meteorological conditions where a global model trained on historical data may not have enough observed data points to consider rare conditions with the necessary weighting.

* Accuracy of forecasts for "microclimates" such as alpine resorts at the top of a ultra-prominent peak. Global models tend to smooth over such as an anomaly in the landscape as if the landscape anomaly was never present.[1]

It'd perhaps be possible to build more local monitoring stations to collect training data and run many local climate models across a landscape and run more climate models of specific rare weather systems. But it is also possibly cheaper and adequate (or more accurate) to just hire a meteorologist with local knowledge instead?

[1] Zanchi, M., Zapperi, S. & La Porta, C.A.M. Harnessing deep learning to forecast local microclimate using global climate data. Sci Rep 13, 21062 (2023). https://doi.org/10.1038/s41598-023-48028-1 https://www.nature.com/articles/s41598-023-48028-1