Omni-Weather: A Unified Multimodal Model for Weather Radar Understanding and Generation

Zhiwang Zhou; Yuandong Pu; Xuming He; Yidi Liu; Yixin Chen; Junchao Gong; Xiang Zhuang; Wanghan Xu; Qinglong Cao; Shixiang Tang; Yihao Liu; Wenlong Zhang; Lei Bai

arXiv · 2512.21643

Omni-Weather: A Unified Multimodal Model for Weather Radar Understanding and Generation

Zhiwang Zhou·Yuandong Pu·Xuming He·Yidi Liu·Yixin Chen·Junchao Gong·Xiang Zhuang·Wanghan Xu·Qinglong Cao·Shixiang Tang·Yihao Liu·Wenlong Zhang·Lei Bai

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Abstract

Weather modeling requires both accurate prediction and mechanistic interpretation, yet existing methods treat these goals in isolation, separating generation from understanding. To address this gap, we present Omni-Weather, the first multimodal foundation model that unifies weather generation and understanding within a single architecture. Omni-Weather integrates a radar encoder for weather generation tasks, followed by unified processing using a shared self-attention mechanism. Moreover, we construct a Chain-of-Thought dataset for causal reasoning in weather generation, enabling interpretable outputs and improved perceptual quality. Extensive experiments show Omni-Weather achieves state-of-the-art performance in both weather generation and understanding. Our findings further indicate that generative and understanding tasks in the weather domain can mutually enhance each other. Omni-Weather also demonstrates the feasibility and value of unifying weather generation and understanding.

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Zhiwang Zhou, Yuandong Pu, Xuming He, Yidi Liu, Yixin Chen, Junchao Gong, Xiang Zhuang, Wanghan Xu, Qinglong Cao, Shixiang Tang, Yihao Liu, Wenlong Zhang, Lei Bai. 2026-03-30. Omni-Weather: A Unified Multimodal Model for Weather Radar Understanding and Generation. https://arxiv.org/abs/2512.21643

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