A Deep Learning Model Framework for High-Resolution Downscaling of ERA5 in the Austrian Alps

We present a deep learning model that regionally downscales relatively coarse (~25 km) ERA5 reanalysis data to a 1-km grid. The model is trained on hourly fields from GeoSphere Austria’s high-resolution INCA model, a regional data assimilation and nowcasting system. Once trained, it can generate hourly high-resolution climate fields using only coarse ERA5 data and a digital elevation model as input. Early results show the deep learning model outperforms simple interpolation of ERA5 data. By comparing our model with baseline models that apply only constant bias correction and lapse rate based elevation adjustment we can quantify how much skill comes from basic statistical corrections versus the additional skill provided by deep-learning downscaling. This comparison allows us to determine whether the deep learning model is capturing nonlinear terrain/flow effects beyond what bias and elevation corrections can provide.