publications

2026

1. ICML

   Learning to Refine: Spectral-Decoupled Iterative Refinement Framework for Precipitation Nowcasting

   Yunlong Zhou, Chen Zhao, Danyang Peng, Fanfan Ji, and Xiao-Tong Yuan

   In Proceedings of the 43rd International Conference on Machine Learning (ICML), 2026

   Abs arXiv Bib HTML Code Slides

   Accurate precipitation nowcasting is vital for disaster mitigation, but deep learning methods face a key trade-off: regression models produce over-smoothed, spectrally decaying predictions that blur convective details and violate turbulence power laws; diffusion models generate realistic yet unanchored hallucinations lacking physical grounding. We propose Spectral-Decoupled Iterative Refinement (SDIR), a deterministic framework that reformulates nowcasting as progressive frequency-decoupled refinement. SDIR first extracts a stable low-frequency synoptic skeleton, then iteratively refines high-frequency textures under physical constraints, eliminating both blurring and hallucinations. It features a dual-path design: the Synoptic Frequency-Guided Former (SFG-Former) with Scale-Adaptive Transformers for global structure, and the Fourier Residual Refiner (FR-Refiner) with Scale-Conditioned Fourier Neural Operators for fine residuals. A Physically Consistent Power Spectral Density (PCPSD) loss with dynamic masking enforces a turbulence-consistent spectral distribution. Experiments on three benchmarks show SDIR significantly outperforms SOTA methods in spatial accuracy while achieving spectral fidelity competitive with diffusion-based methods, enabling reliable high-resolution operational nowcasting.

   @inproceedings{zhou2026sdir,
     title = {Learning to Refine: Spectral-Decoupled Iterative Refinement Framework for Precipitation Nowcasting},
     author = {Zhou, Yunlong and Zhao, Chen and Peng, Danyang and Ji, Fanfan and Yuan, Xiao-Tong},
     booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},
     year = {2026},
   }

2. ICML

   LoPhyDA: Low-Rank Tensor and Physics Gradient Guided Diffusion for Atmospheric Data Assimilation

   Danyang Peng, Yang Chen, Yunlong Zhou, and Xiao-Tong Yuan

   In Proceedings of the 43rd International Conference on Machine Learning (ICML), 2026

   Bib HTML Slides

   @inproceedings{peng2026lophyda,
     title = {LoPhyDA: Low-Rank Tensor and Physics Gradient Guided Diffusion for Atmospheric Data Assimilation},
     author = {Peng, Danyang and Chen, Yang and Zhou, Yunlong and Yuan, Xiao-Tong},
     booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},
     year = {2026},
   }

3. EAAI

   More realistic and accurate precipitation nowcasting with Conditional Rectified Flow Transformers

   Yunlong Zhou, Chen Zhao, Fanfan Ji, Renlong Hang, Qingshan Liu, and 1 more author

   Engineering Applications of Artificial Intelligence, 2026

   DOI Bib Code

   @article{zhou2026crft,
     title = {More realistic and accurate precipitation nowcasting with Conditional Rectified Flow Transformers},
     author = {Zhou, Yunlong and Zhao, Chen and Ji, Fanfan and Hang, Renlong and Liu, Qingshan and Yuan, Xiao-Tong},
     journal = {Engineering Applications of Artificial Intelligence},
     volume = {165},
     pages = {113402},
     year = {2026},
     doi = {10.1016/j.engappai.2025.113402},
   }

2024

1. JSTARS

   Spatiotemporal Enhanced Adversarial Network for Precipitation Nowcasting

   Yunlong Zhou, Renlong Hang, Fanfan Ji, Zefeng Pan, Qingshan Liu, and 1 more author

   IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024

   DOI Bib

   @article{zhou2024stean,
     title = {Spatiotemporal Enhanced Adversarial Network for Precipitation Nowcasting},
     author = {Zhou, Yunlong and Hang, Renlong and Ji, Fanfan and Pan, Zefeng and Liu, Qingshan and Yuan, Xiao-Tong},
     journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
     volume = {17},
     pages = {7608--7620},
     year = {2024},
     doi = {10.1109/JSTARS.2024.3381822},
   }

2. GRSL

   Deep Precipitation Nowcasting With Dual Regions Displacement Information and Global Spatiotemporal Representations Learning

   Zefeng Pan, Fanfan Ji, Yunlong Zhou, Renlong Hang, Qingshan Liu, and 1 more author

   IEEE Geoscience and Remote Sensing Letters, 2024

   DOI Bib

   @article{pan2024dual,
     title = {Deep Precipitation Nowcasting With Dual Regions Displacement Information and Global Spatiotemporal Representations Learning},
     author = {Pan, Zefeng and Ji, Fanfan and Zhou, Yunlong and Hang, Renlong and Liu, Qingshan and Yuan, Xiao-Tong},
     journal = {IEEE Geoscience and Remote Sensing Letters},
     volume = {21},
     pages = {1--5},
     year = {2024},
     doi = {10.1109/LGRS.2024.3384772}
   }