Abstract
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.
Abstract (translated)
卫星大气污染物数据通常仅以较低的空间分辨率提供，这限制了它们在局部环境分析和决策中的应用。空间降尺度方法旨在将低分辨率的卫星数据转换为高分辨率的数据场。在这项工作中，考虑使用两种广泛使用的深度学习架构——超分辨率残差网络（SRDRN）和基于编码器-解码器的UNet，来进行对流层臭氧的空间降尺度处理。这两种方法都扩展了一个轻量级的时间模块，该模块通过正弦或径向基函数（RBF）编码来表示观测时间，并将这些时间特征与网络中的空间表示进行融合。在意大利臭氧降尺度案例研究中评估了所提出的具有时间感知功能的扩展方案及其基准模型的表现。结果表明，尽管计算复杂度仅略有增加，但时间模块显著提高了降尺度性能和收敛速度。
URL
https://arxiv.org/abs/2512.13753
