Residual corrective diffusion modeling for km-scale atmospheric downscaling

State of the art for weather and climate hazard prediction requires expensive km-scale numerical simulations. Here, a generative diffusion model is explored for downscaling global inputs to km-scale, as a cost-effective alternative. The model is trained to predict 2 km data from an operational regional weather model over Taiwan, conditioned on a 25 km reanalysis. To address the large resolution ratio, different physics and synthesize new channels, we employ a two-step approach. A deterministic model first predicts the mean, followed by a generative diffusion model that predicts the residual. The model exhibits encouraging deterministic and probabilistic skills, spectra and distributions that recover power law…