Omni-Weather: Unified Multimodal Foundation Model for Weather Generation and Understanding
Authors: name: Zhiwang Zhou, name: Yuandong Pu, name: Xuming He, name: Yidi Liu, name: Yixin Chen, name: Junchao Gong, name: Xiang Zhuang, name: Wanghan Xu, name: Qinglong Cao, name: Shixiang Tang, name: Yihao Liu, name: Wenlong Zhang, name: Lei Bai

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.