Extreme and severe convective weather disasters: A dual-polarization radar nowcasting method based on physical constraints and a deep neural network model

Abstract

It is difficult for traditional methods, such as the PySTEPS method, which is based on optical flows, and the UNet method, which is based on convolutional neural networks, to predict extreme weather events (e.g., hail, heavy rain, lightning and mesoscale cyclones) 0–60 min in advance because these events have short life cycles, occur at small scales, rapidly develop and are highly nonlinear. Based on the structural characteristics of severe weather, we propose a physically constrained generative adversarial deep neural network nowcasting model to improve the prediction accuracy and blurry data problem associated with nowcasting extreme convective weather within the 0–60 min interval. The improved TITAN algorithm is used to quickly identify and match strong storm cells in three dimensions, and the convection-diffusion constraint equation is established. Additionally, reflectivity (Zh), differential reflectivity (Zdr), the range derivative-specific differential phase (Kdp), correlation coefficient (cc), precipitation particle phase, and velocity divergence are considered when training the model with a large sample set, and constraint equations are used for optimization. The critical success index, false alarm ratio and miss probability scores of the nowcasting results obtained with this method are better than the PySTEPS and UNet results. Additionally, the root mean square error of a hailstorm nowcasted with a 30-min lead time is significantly better than that of the other methods, and the blurry data problem that appears over time is mitigated. We show that our model can provide nowcasts with high precision to support operations at various resolutions and lead times in cases for which alternative methods struggle.