  Running high-resolution physical models is computationally expensive and
essential for many disciplines. Agriculture, transportation, and energy are
sectors that depend on high-resolution weather models, which typically consume
many hours of large High Performance Computing (HPC) systems to deliver timely
results. Many users cannot afford to run the desired resolution and are forced
to use low resolution output. One simple solution is to interpolate results for
visualization. It is also possible to combine an ensemble of low resolution
models to obtain a better prediction. However, these approaches fail to capture
the redundant information and patterns in the low-resolution input that could
help improve the quality of prediction. In this paper, we propose and evaluate
a strategy based on a deep neural network to learn a high-resolution
representation from low-resolution predictions using weather forecast as a
practical use case. We take a supervised learning approach, since obtaining
labeled data can be done automatically. Our results show significant
improvement when compared with standard practices and the strategy is still
lightweight enough to run on modest computer systems.
