The problem in this world of AI/weather is that "accuracy" is an extremely fuzzy concept. The leading pack of AI-NWP models (NVIDIA's FourCastNet, DeepMind's Gr

The problem in this world of AI/weather is that "accuracy" is an extremely fuzzy concept. The leading pack of AI-NWP models (NVIDIA's FourCastNet, DeepMind's Graphcast, Huawei PanguWeather), when compared on an apples-to-apples basis with the leading pack of traditional NWP systems (NOAA GFS and ECMWF HRES) have similar accuracy metrics. But here, "accuracy" is an esoteric term that is far removed from how an end user would perceive how "good" any of these given models are at predicting tomorrow's afternoon high temperature at their house. In the world of meteorology, the way you build an "accurate" (e.g. "user-perceived accuracy") forecast is to consume the entire previous class of forecasts and apply statistics/ML to post-process them. The greater your ability to probe uncertain in the forecasts from these models - e.g. by running larger ensembles or tailoring the ones you run to try to quantify the uncertainty more explicitly - the better your opportunity to improve those 'accuracy" metrics. So yes - the opportunity here is running larger sets of tailored forecast simulations as a way to statistically optimize forecast accuracy.