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As John asked about applying other well known machine learning algorithms on the El Nino problem, I've been vaguely thinking about it. However, (at least to my understanding) a lot of the techniques simply won't scale to the volume of data -- either because my laptops are relatively low power and I don't have access to a cluster, or more seriously because a lot of the techniques are are solving problems by gradient descent/stochastic algorithms/etc which tend to either not converge or converge to very poor local minima in very high dimesional spaces.
If I do come up with any good dimensionaliy reduction techniques I'll put them here, but I also ought to check if this has been looked at before by others?