TriPhysGAN-Attn: A Physics-Informed Generative Model for Radar Echo Forecasting via Triple Mechanism Decomposition and Attention Fusion

Yonghong Zhang, Ziwei Yuan, Atta Badii, Junfei Wang, Peishan Li

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 01 January 2026 · Nanjing University of Information Science & Technology (NUIST), University of Reading

Paper

In one sentence. TriPhysGAN-Attn is a physics-guided GAN for radar echo nowcasting that splits advection, initiation and deformation into branches, improving accuracy under severe convective weather.

Abstract

Nowcasting of precipitation plays a critical role in meteorological operations and disaster prevention. However, existing deep learning-based radar echo prediction models often suffer from unclear physical mechanisms and insufficient capability to represent the multi-scale dynamic processes of precipitation systems, making them less effective in handling severe convective weather. In this study, we propose TriPhysGAN-Attn, a novel radar echo prediction model guided by meteorological physical mechanisms. We divide the evolution of precipitation into three fundamental processes: advection, initiation and dissipation, and deformation. Accordingly, we design an advection branch to model multi-scale motion fields, an initiation and dissipation branch driven by state differences, and a deformation branch guided by motion field gradients. To enhance physical consistency, we incorporate a physically informed loss function along with meteorological priors and supervision signals. A multi-head cross-attention mechanism is employed to dynamically fuse information across the three processes, facilitating collaborative modeling. Additionally, a generative adversarial framework and a coordinate attention module are integrated to better capture local discontinuities and spatial structures. Experimental results show that TriPhysGAN-Attn outperforms existing methods on real radar datasets, achieving superior performance in structural preservation, prediction accuracy, and robustness under extreme weather conditions.

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