A rapid downscaling approach for meteorological wind fields using diffusion models and attention mechanisms

Extensive applications have been found for rapid downscaling on meteorological wind field data in extreme weather warning, renewable energy assessment and site selection optimization, as well as dynamic simulation of nuclear accident emergency responses. This study proposes a progressive deep learning downscaling model (from 0.25° to 3 km) for generating high-resolution wind field data by integrating diffusion models with multi-head cross-attention mechanisms. Specifically, a progressive diffusion framework is developed to generate high-resolution wind fields through iterative denoising of latent states initialized from Gaussian noise distributions. Compared to Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) architecture, our approach achieved approximately 32 % reduction in Mean Absolute Error (MAE) and 46 % reduction in Mean Squared Error (MSE), while Structural Similarity Index (SSIM) was significantly improved from 0.7 to over 0.8. It is deduced that this approach significantly improves generation quality and alleviates non-physical artifacts caused by the mode collapse within Generative Adversarial Network (GAN). The integration of multi-head cross-attention mechanisms and 2D Rotary Positional Embedding (RoPE) enables effective fusion of cross-modal information from low-resolution wind fields, terrain data, and diffusion model latent states. Ablation studies demonstrate that the attention mechanism reduced both MAE and MSE by approximately 20 %, while improving Peak Signal-to-Noise Ratio (PSNR) and SSIM values. This indicates that the attention mechanisms enable the model to capture long-range spatial dependencies, significantly enhancing its representational capacity and physical consistency in spatial feature reconstruction. Additionally, to address the slow reasoning speed of traditional diffusion models, this work implements the Denoising Diffusion Implicit Model (DDIM) sampling algorithm to achieve deterministic latent space mapping, substantially reducing inference steps while maintaining generation accuracy. This framework ultimately realizes rapid generation of high-precision wind field data within seconds. The proposed methodology provides a novel solution for meteorological wind field downscaling, directly serving various engineering applications requiring refined meteorological data.