  Urban downscaling is a link to transfer the knowledge from coarser climate
information to city scale assessments. These high-resolution assessments need
multiyear climatology of past data and future projections, which are complex
and computationally expensive to generate using traditional numerical weather
prediction models. The city of Austin, Texas, USA has seen tremendous growth in
the past decade. Systematic planning for the future requires the availability
of fine resolution city-scale datasets. In this study, we demonstrate a novel
approach generating a general purpose operator using deep learning to perform
urban downscaling. The algorithm employs an iterative super-resolution
convolutional neural network (Iterative SRCNN) over the city of Austin, Texas,
USA. We show the development of a high-resolution gridded precipitation product
(300 m) from a coarse (10 km) satellite-based product (JAXA GsMAP). High
resolution gridded datasets of precipitation offer insights into the spatial
distribution of heavy to low precipitation events in the past. The algorithm
shows improvement in the mean peak-signal-to-noise-ratio and mutual information
to generate high resolution gridded product of size 300 m X 300 m relative to
the cubic interpolation baseline. Our results have implications for developing
high-resolution gridded-precipitation urban datasets and the future planning of
smart cities for other cities and other climatic variables.
