[1727706962]

Looks like it was a forward prediction.

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

[pfisherman]

This is still on retrospective data. The machine learning graveyard is filled with models that worked well on retrospective data, but did not hold up in a live inference setting. Just ask Zillow. The real test is whether they can predict the weather 14 days out in 2025.

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.

[scellus]

ECMWF runs many such models at their site, a run two or four times per day, and they have verification statistics too, no need to doubt the accuracy.

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.