  Machine learning for the parameterization of subgrid-scale processes in
climate models has been widely researched and adopted in a few models. A key
challenge in developing data-driven parameterization schemes is how to properly
represent rare, but important events that occur in geoscience datasets. We
investigate and develop strategies to reduce errors caused by insufficient
sampling in the rare data regime, under constraints of no new data and no
further expansion of model complexity. Resampling and importance weighting
strategies are constructed with user defined parameters that systematically
vary the sampling/weighting rates in a linear fashion and curb too much
oversampling. Applying this new method to a case study of gravity wave momentum
transport reveals that the resampling strategy can successfully improve errors
in the rare regime at little to no loss in accuracy overall in the dataset. The
success of the strategy, however, depends on the complexity of the model. More
complex models can overfit the tails of the distribution when using non-optimal
parameters of the resampling strategy.
