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This is fascinating: > 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 mo
by robertlagrant 3y ago
This is fascinating:
> 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.
- broast 3y agoWeather is markovian
- deleted 3y ago[deleted]
- hakuseki 3y agoThat 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.
- Imanari 3y agoInteresting 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.
- Al-Khwarizmi 3y agoI 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?
- postalrat 3y agoRead the other comments.
- counters 3y agoIt'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).
- baq 3y agoYeah 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)