LLMDiff: Diffusion Model Using Frozen LLM Transformers for Precipitation Nowcasting.

Area of Science:

• Meteorology and Atmospheric Science
• Artificial Intelligence
• Computer Vision

Background:

• Precipitation nowcasting is vital for real-world applications, requiring high-resolution, short-term rainfall predictions.
• Deep learning methods are increasingly used for precipitation nowcasting, with denoising diffusion models showing promise due to their probabilistic nature.
• Effective conditioning is essential for applying diffusion models to rainfall prediction tasks.

Purpose of the Study:

• To propose LLMDiff, a probabilistic spatiotemporal model for advanced precipitation nowcasting.
• To leverage large language models (LLMs) within a diffusion framework for enhanced weather forecasting.
• To improve the accuracy and temporal dependency capture in short-term rainfall prediction.

Main Methods:

• Developed LLMDiff, a model featuring a conditional encoder-decoder network and a denoising network.
• Integrated a frozen transformer block from pre-trained LLMs as a universal visual encoder in the denoising network.
• Utilized conditional information to guide the denoising network for high-quality earth system predictions.

Main Results:

• LLMDiff demonstrated superior performance compared to existing state-of-the-art models.
• The model effectively estimated motion trends by considering long-term temporal context.
• Accurate capture of temporal dependencies within frame sequences was achieved.

Conclusions:

• LLMDiff represents a significant advancement in probabilistic spatiotemporal modeling for precipitation nowcasting.
• The integration of LLMs enhances the model's ability to interpret complex spatiotemporal weather data.
• The proposed method shows strong potential for real-world weather forecasting applications.