> 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.
Initializing directly from, say, geostationary and LEO satellite data with complementary surface station observations - skipping the assimilation step entirely - is clearly where this revolution is headed, but it's very important to explicitly note that we're not there yet (even in a research capacity).
Furthermore, a predictive model is not working with a complete picture of the weather, but rather some limited-resolution measurements. So, even ignoring non-weather, there may be local weather phenomena detected at time t0, escaping detection at time t1, but still affecting weather at time t2.