![]() Different ensemble systems have different numbers of ensemble members and the more ensemble members there are, the better the forecast will be as it will take into account a wider range of possibilities. ![]() If the ensembles disagree, it’s wise not to put too much confidence in one outcome or another. If all, or almost all, the ensemble members agree on a particular outcome, you can have high confidence that that outcome will occur. Ensembles are a great tool for gauging uncertainty in a forecast. Each one of these ideas will create its own outcome, known as an ensemble member. Ensembles attempt to fix this problem by starting the model with a bunch of ideas of what the atmosphere could be doing right now. Any small error in the weather model initially due to this gap in observation is compounded exponentially out through time due to chaos. Because we can’t observe every tiny bit of air in our atmosphere, our picture of the weather currently is incomplete.
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