8 research outputs found
Super-Resolution of Three-Dimensional Temperature and Velocity for Building-Resolving Urban Micrometeorology Using Physics-Guided Convolutional Neural Networks with Image Inpainting Techniques
Atmospheric simulations for urban cities can be computationally intensive
because of the need for high spatial resolution, such as a few meters, to
accurately represent buildings and streets. Deep learning has recently gained
attention across various physical sciences for its potential to reduce
computational cost. Super-resolution is one such technique that enhances the
resolution of data. This paper proposes a convolutional neural network (CNN)
that super-resolves instantaneous snapshots of three-dimensional air
temperature and wind velocity fields for urban micrometeorology. This
super-resolution process requires not only an increase in spatial resolution
but also the restoration of missing data caused by the difference in the
building shapes that depend on the resolution. The proposed CNN incorporates
gated convolution, which is an image inpainting technique that infers missing
pixels. The CNN performance has been verified via supervised learning utilizing
building-resolving micrometeorological simulations around Tokyo Station in
Japan. The CNN successfully reconstructed the temperature and velocity fields
around the high-resolution buildings, despite the missing data at lower
altitudes due to the coarseness of the low-resolution buildings. This result
implies that near-surface flows can be inferred from flows above buildings.
This hypothesis was assessed via numerical experiments where all input values
below a certain height were made missing. This research suggests the
possibility that building-resolving micrometeorological simulations become more
practical for urban cities with the aid of neural networks that enhance
computational efficiency