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Open access 2026

PstpNet: Dual-Branch Physics-Guided U-Net With Multiscale Spatiotemporal Disentanglement for Precipitation Nowcasting

Precipitation nowcasting remains challenging because precipitation systems exhibit strong nonlinear evolution, rapid development, and complex long-range spatiotemporal dependencies. Existing deep learning methods are still limited in characterizing precipitation dynamics and improving model interpretability. To address...

Kai Xie, Bo Yin, Yue Sun et al. · 0 citations
Open access Aug 2026

Event-Guided Spatiotemporal Transformer with Conditional Diffusion Refinement for High-Intensity Precipitation Nowcasting

Accurate nowcasting of high-intensity precipitation is critical for urban flood control and short-term hydrological risk management. However, the high stochasticity of convective systems poses a significant challenge for traditional deep learning models in generating accurate predictions. Existing regression-based mode...

Wenqi Li, Hai-Yong Zheng, Hai-Peng Cui et al. · 0 citations
Open access Aug 2026

Cascade Deep Learning With Physics Guidance for Typhoon Intensity Prediction

Typhoons pose a significant threat to both human safety and economic stability. As a crucial metric for assessing their destructive potential, typhoon intensity (TI) prediction has become an important research focus, with numerous methods developed. However, effectively combining two‐dimensional typhoon structure dom...

Zhengya Sun, Bojie Fan, Bo Yin et al. · 0 citations

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