HARECast: Stabilizing Attention for Reliable Precipitation Nowcasting
A new framework called HARECast (Head-wise Attention Response Energy-regulated) addresses instability in attention-based precipitation nowcasting models. Researchers show that cross-sample variance in attention-response energy correlates with forecast errors, and that this variability propagates through self-attention to enlarge prediction error bounds. The work, published on arXiv (2605.13181), proposes regulating attention-response energy across heads and layers to improve reliability. The study focuses on the underexplored issue of attention stability in both unimodal and multimodal settings for highly localized and rapidly evolving atmospheric dynamics.
Key facts
- HARECast stands for Head-wise Attention Response Energy-regulated framework.
- The research identifies cross-sample instability of attention-response energy as a source of forecasting unreliability.
- Inaccurate forecasts are associated with larger attention-response energy variance across heads and layers.
- Cross-sample variability can propagate through self-attention and enlarge a lower bound on prediction error.
- The paper is published on arXiv with ID 2605.13181.
- Precipitation nowcasting deals with highly localized, rapidly evolving, and heterogeneous atmospheric dynamics.
- The work emphasizes attention stability over representation learning and prediction capacity.
- The framework is applicable to both unimodal and multimodal settings.
Entities
Institutions
- arXiv