Long tails and freak weather are the hottest topics of research in the area of data-driven weather forecasting. ECMWF, highlighted in this article, is attempting to extend its ML forecast system to ensemble predictions (https://www.ecmwf.int/en/about/media-centre/aifs-blog/2024/e...).
If these methods work, they'll likely improve our ability to model long tails. Traditional NWP is extremely expensive, so cutting-edge models can have either high resolution xor large ensembles. You need high resolution for the detail, but you need large ensembles to see into the tails of the distribution; it's a persistent problem.
In inference, ML-based models run a bit over two orders of magnitude faster than traditional NWP, with the gains split between running on GPUs (possibly replicable) and fantastic levels of numerical intensity thanks to everything being matrix-matrix products (much harder to replicate with conventional algorithms). That opens a lot of freedom to expand ensemble sizes and the like.
If these methods work, they'll likely improve our ability to model long tails. Traditional NWP is extremely expensive, so cutting-edge models can have either high resolution xor large ensembles. You need high resolution for the detail, but you need large ensembles to see into the tails of the distribution; it's a persistent problem.
In inference, ML-based models run a bit over two orders of magnitude faster than traditional NWP, with the gains split between running on GPUs (possibly replicable) and fantastic levels of numerical intensity thanks to everything being matrix-matrix products (much harder to replicate with conventional algorithms). That opens a lot of freedom to expand ensemble sizes and the like.