A Lightweight Hybrid CNN–Transformer Architecture for High‑Resolution Downscaling and Bias Correction of Snow Water Equivalent

Accurate, high‑resolution Snow Water Equivalent (SWE) information is critical for reliable hazard assessment and effective water resource management. Yet, widely used global reanalysis products provide SWE at coarse spatial scales and exhibit substantial terrain‑ and melt‑related biases. In contrast, dynamically downscaled products offer improved detail but are costly to run and thus, remain limited in availability.

To address this limitation, we introduce the Linear Attention Snow Downscaling Model (LASDM), a lightweight hybrid deep learning architecture designed specifically to enhance the spatial detail and physical realism of SWE fields. LASDM combines convolutional neural networks with linear attention based transformer blocks, enabling efficient representation of synoptic‑to‑local snow processes while remaining highly parameter‑efficient (<1 million parameters).

Applied to the ERA5 → ERA5‑Land downscaling problem over the Great Lakes region (1980–2022), LASDM demonstrates stronger performance than U‑Net, Swin Transformer, and statistical baselines across a range of evaluation metrics. Case studies for two winter storms provide additional context for these differences. More broadly, this work suggests the potential of machine‑learning architectures for downscaling and bias correction. LASDM offers a compact and adaptable framework that may help improve snow representation and support applications that rely on higher‑resolution SWE.