Could you point me to the part where it says it depends on supercomputer output?
I didn't read the paper but the linked post seems to say otherwise? It mentions it used the supercomputer output to impute data during training. But for prediction it just needs:
> For inputs, GraphCast requires just two sets of data: the state of the weather 6 hours ago, and the current state of the weather. The model then predicts the weather 6 hours in the future. This process can then be rolled forward in 6-hour increments to provide state-of-the-art forecasts up to 10 days in advance.
You can read about it more in their paper. Specifically page 36. Their dataset, ERA5, is created using a process called reanalysis. It combines historical weather observations with modern weather models to create a consistent record of past weather conditions.
I can't find the details, but if the supercomputer job only had to run once, or a few times, while this model can make accurate predictions repeatedly on unique situations, then it doesn't matter as much that a supercomputer was required. The goal is to use the supercomputer once, to create a high value simulated dataset, then repeatedly make predictions from the lower-cost models.
I didn't read the paper but the linked post seems to say otherwise? It mentions it used the supercomputer output to impute data during training. But for prediction it just needs:
> For inputs, GraphCast requires just two sets of data: the state of the weather 6 hours ago, and the current state of the weather. The model then predicts the weather 6 hours in the future. This process can then be rolled forward in 6-hour increments to provide state-of-the-art forecasts up to 10 days in advance.