  In this paper, we present results on improving out-of-domain weather
prediction and uncertainty estimation as part of the \texttt{Shifts Challenge
on Robustness and Uncertainty under Real-World Distributional Shift} challenge.
We find that by leveraging a mixture of experts in conjunction with an advanced
data augmentation technique borrowed from the computer vision domain, in
conjunction with robust \textit{post-hoc} calibration of predictive
uncertainties, we can potentially achieve more accurate and better-calibrated
results with deep neural networks than with boosted tree models for tabular
data. We quantify our predictions using several metrics and propose several
future lines of inquiry and experimentation to boost performance.
