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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 mention
by silveraxe93 3y ago
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.
- serjester 3y agoYou 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. https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/Learning_skillful_medium-range_global_weather_forecasting.pdf https://storage.googleapis.com/deepmind-media/DeepMind.com/B...
- silveraxe93 3y agoAh nice. Thanks!
- deleted 3y ago[deleted]
- dekhn 3y agoI 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.