In this last experiment, we again assume that learning is successful on the support of the training data, but show another setting in which meteorological normalization can fail if X and T are not independent, i.e., if (6b) is violated. The experiment in this section is a mixture of those in Sections 3.1 and 3.2: The dependence between X and T will cause meteorological 190 normalization to sample tuples (r i , t i ) that are, as in forecasting, not covered by the training dataset D, and that hence require good extrapolation properties of f .