Time-aware UNet and super-resolution deep residual networks for spatial downscaling
This work addresses the need for high-resolution ozone data for local-scale environmental analysis, representing an incremental improvement by adding temporal encoding to existing deep learning methods.
The paper tackled the problem of coarse spatial resolution in satellite atmospheric pollutant data by extending SRDRN and UNet architectures with lightweight temporal modules for spatial downscaling of tropospheric ozone, resulting in significant improvements in performance and convergence speed with only slight increases in computational complexity.
Satellite data of atmospheric pollutants are often available only at coarse spatial resolution, limiting their applicability in local-scale environmental analysis and decision-making. Spatial downscaling methods aim to transform the coarse satellite data into high-resolution fields. In this work, two widely used deep learning architectures, the super-resolution deep residual network (SRDRN) and the encoder-decoder-based UNet, are considered for spatial downscaling of tropospheric ozone. Both methods are extended with a lightweight temporal module, which encodes observation time using either sinusoidal or radial basis function (RBF) encoding, and fuses the temporal features with the spatial representations in the networks. The proposed time-aware extensions are evaluated against their baseline counterparts in a case study on ozone downscaling over Italy. The results suggest that, while only slightly increasing computational complexity, the temporal modules significantly improve downscaling performance and convergence speed.