alt Hacker NewsWeather 2 doesn't seem to have been an ensemble model. Weather 3 is, so theoretically it can get more accurate outcomes by taking the probabilistic analysis of several models concurrently to determine the most likely weather conditions.
I'm building a tool right now that uses an ensemble to determine wind gust likelihood, which is useful for safety critical work on construction sites and the like.
WeatherNext 2 was based on the FGN architecture described in [1]. It was explicitly designed and trained to produce ensemble forecasts (it was trained in such a way that the output ensemble optimized a CRPS metrics). In fact, it was a set of 4 different model weights, each of which was seeded with a random noise vector to produce an array of 16 forecasts for a total of 64 ensemble members. WeatherNext 3 trimmed that down from 4 to 2 separate model weights to use.
[1]: https://www.nature.com/articles/d41586-026-02643-w