首个统一气象雷达生成与理解的多模态模型
Omni-Weather: A Unified Multimodal Model for Weather Radar Understanding and Generation
- 用共享自注意力机制统一处理气象生成与理解任务
- 在生成与理解上均达到当前最佳性能
- 适合气象研究与可解释性分析的科研人员
气象建模需要精确预测与机制解读，但现有方法将这两者分离。为此，我们提出Omni-Weather，首个将气象生成与理解统一于单一架构的多模态基础模型。Omni-Weather采用雷达编码器进行气象生成，通过共享自注意力机制实现统一处理，并构建了用于因果推理的思维链数据集，提升输出可解释性与感知质量。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。研究发现，气象领域的生成与理解任务可相互促进。该模型验证了统一气象生成与理解的可行性与价值。
原文摘要 · Abstract (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.
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