Home >

news Help

Publication Information


Title
Japanese: 
English:Zero-Shot Super-Resolution from Unstructured Data Using a Transformer-Based Neural Operator for Urban Micrometeorology 
Author
Japanese: Yasuda Yuki, 大西領.  
English: Yasuda Yuki, Onishi Ryo.  
Language English 
Journal/Book name
Japanese: 
English:SOLA 
Volume, Number, Page Vol. advpub    No. 0   
Published date July 2025 
Publisher
Japanese: 
English:公益社団法人 日本気象学会 
Conference name
Japanese: 
English: 
Conference site
Japanese: 
English: 
Abstract <p>This study demonstrates that a transformer-based neural operator (TNO) can perform zero shot super-resolution of two-dimensional temperature fields near the ground in urban areas. During training, super-resolution is performed from a horizontal resolution of 100 m to 20 m, while during testing, it is performed from 100 m to a finer resolution of 5 m. This setting is referred to as zero-shot, since no data with the target 5 m resolution are included in the training dataset. The 20 m and 5 m resolution data were independently obtained by dynamically downscaling the 100 m data using a physics-based micrometeorology model that resolves buildings. Compared to a convolutional neural network, the TNO more accurately reproduces temperature distributions at 5 m resolution and reduces test errors by approximately 33%. Furthermore, the TNO successfully performs zero-shot super-resolution even when trained with unstructured data, in which grid points are randomly arranged. These results suggest that the TNO recognizes building shapes independently of grid point locations and adaptively infers the temperature fields induced by buildings.</p>

©2007 Institute of Science Tokyo All rights reserved.