全能气象：面向天气生成与理解的多模态统一基础模型

摘要

气象建模既需要精准预测又需机制解释，但现有方法将这两个目标割裂处理，使生成与理解相互分离。为弥补这一缺陷，我们提出首个多模态基础模型Omni-Weather，将气象生成与理解统一于单一架构中。该模型通过雷达编码器处理气象生成任务，并采用共享自注意力机制进行统一处理。此外，我们构建了面向气象生成因果推理的思维链数据集，使模型既能输出可解释结果，又提升了感知质量。大量实验表明，Omni-Weather在气象生成与理解任务上均达到最先进水平。研究进一步证实，气象领域的生成与理解任务能够相互促进。该模型也证明了统一气象生成与理解机制的可行性与价值。

English

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.

全能气象：面向天气生成与理解的多模态统一基础模型

Omni-Weather: Unified Multimodal Foundation Model for Weather Generation and Understanding

摘要

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