From the linked article:
> GenCast is a machine learning weather prediction model trained on weather data from 1979 to 2018
and a google blog https://deepmind.google/discover/blog/gencast-predicts-weath...
> To rigorously evaluate GenCast's performance, we trained it on historical weather data up to 2018, and tested it on data from 2019
I am guessing they did not want to set up the data pipeline to run inference in a live setting. But that is what I would need to see to be a true believer.
Still a cool result and article though.
The Google model is probably the best so far but ECMWF's own diffusion model was already on par with ENS and many point-forecast models (graph transformers, not diffusion) outperform state-of-the-art physical models.
What is missing is initialization directly from observations. All the best-performing models initialize from ERA5 or other reconstruction.