Precipitation nowcasting diffusion model based on turbulence theory and multi-source data

Precipitation nowcasting is a long-standing challenge due to the inherent unpredictability, which often lead to significant risks and damage. While traditional approaches focus on modeling the nonlinear relationship between initial precipitation states and future states, these methods often fail to capture accurate precipitation dynamics, such as its distribution and intensity. The absence of guidance from physical theory limits data-driven methods in disclosing the chaotic nature of precipitation. To address this, we integrate Prandtl’s mixing length theory from fluid dynamics with diffusion models commonly used in computer vision to enhance the prediction of precipitation distributions and details over the next 200 minutes. This integration accounts for the turbulent properties of precipitation, improving both accuracy and granularity in forecasts. Additionally, we leverage multi-source data, particularly lightning observations, to train a control network for our diffusion model. This enhancement allows for more accurate and controllable predictions of precipitation initiation, decay, and overall spatial-temporal patterns. Our approach advances the state of the art in precipitation nowcasting, offering a robust framework that bridges physical theory with modern deep learning techniques.