Abstract
Cities need climate information to develop resilient infrastructure and for adaptation decisions. the information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 – 10 km) and neighborhood (order of 0.1 – 1 km) resolutions from coarser climate products. Developing these higher resolution (finer grid spacing) data needed for assessments typically covering multiyear climatology of past data and future projections is complex and computationally expensive for traditional physics-Based dynamical models. in this study, we develop and adopt a novel approach for urban downscaling by generating a general-purpose operator using deep learning. This 'Downscale Bench' tool can aid the process of downscaling to any location. the Downscale Bench has been generalized for both in situ (ground- based) and satellite or reanalysis gridded data. the algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city. We apply this for the development of a high-resolution gridded precipitation product (300 m) from a relatively coarse (10 km) satellite-Based product (JAXA GsMAP). the high-resolution gridded precipitation datasets are compared against insitu observations for past heavy rain events over Austin, Texas, and shows marked improvement relative to the coarser datasets relative to cubic interpolation as a baseline. the creation of this Downscaling Bench has implications for generating high-resolution gridded urban meteorological datasets and aiding the planning process for climate-ready cities.
Recommended Citation
M. Singh et al., "Downscalebench for Developing and Applying a Deep Learning based Urban Climate Downscaling- First Results for High-Resolution Urban Precipitation Climatology over Austin, Texas," Computational Urban Science, vol. 3, no. 1, article no. 22, Springer, Dec 2023.
The definitive version is available at https://doi.org/10.1007/s43762-023-00096-9
Department(s)
Biological Sciences
Publication Status
Open Access
Keywords and Phrases
Austin; Deep learning; DownScaleBench; Smart city; Urban downscaling; Urban meteorology
International Standard Serial Number (ISSN)
2730-6852
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Springer, All rights reserved.
Publication Date
01 Dec 2023
Comments
National Science Foundation, Grant AGS 19046442