  We propose a super-resolution (SR) simulation system that consists of a
physics-based meteorological simulation and an SR method based on a deep
convolutional neural network (CNN). The CNN is trained using pairs of
high-resolution (HR) and low-resolution (LR) images created from meteorological
simulation results for different resolutions so that it can map LR simulation
images to HR ones. The proposed SR simulation system, which performs LR
simulations, can provide HR prediction results in much shorter operating cycles
than those required for corresponding HR simulation prediction system. We apply
the SR simulation system to urban micrometeorology, which is strongly affected
by buildings and human activity. Urban micrometeorology simulations that need
to resolve urban buildings are computationally costly and thus cannot be used
for operational real-time predictions even when run on supercomputers. We
performed HR micrometeorology simulations on a supercomputer to obtain datasets
for training the CNN in the SR method. It is shown that the proposed SR method
can be used with a spatial scaling factor of 4 and that it outperforms
conventional interpolation methods by a large margin. It is also shown that the
proposed SR simulation system has the potential to be used for operational
urban micrometeorology predictions.
