> 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.
It's worth pointing out that "state of the weather" is a little bit hand-wavy. The GraphCast model requires a fully-assimilated 3D atmospheric state - which means you still need to run a full-complexity numerical weather prediction system with a massive amount of inputs to actually get to the starting line for using this forecast tool.
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).
Yeah current models aren’t quite ready to ingest real time noisy data like the actual weather… I hear they go off the rails if preprocessing is skipped (outliers, etc)
Interesting indeed, only one lagged feature for time series forecasting? I’d imagine that including more lagged inputs would increase performance.
Rolling the forecasts forward to get n-step-ahead forecasts is a common approach. I’d be interested in how they mitigated the problem of the errors accumulating/compounding.
That is not strictly true. The weather at time t0 may affect non-weather phenomena at time t1 (e.g. traffic), which in turn may affect weather at time t2.
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.
I don't know much about weather prediction, but if a model can improve the state of the art only with that data as input, my conclusion is that previous models were crap... or am I missing something?
> 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.