Title: Omni-Weather: Unified Multimodal Foundation Model for Weather Generation and Understanding
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: AI for Science, Unified foundation model, Interpretable reasoning

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.