基于地理信息的分层状态空间模型，提升全球气象站极端天气预测精度
WSSM: Geographic-enhanced hierarchical state-space model for global station weather forecast
- 融合地理知识与时间频率特征，建模多尺度气象动态
- 在Weather-5K数据集上实现当前最优性能，显著提升极端天气预测能力
- 适合气象预测、气候建模等领域的研究人员参考
全球气象站天气预报（GSWF）是气象研究的重要方向，对提供及时的本地化天气预测至关重要。尽管现有模型在整体精度上取得进展，但高精度极端天气预测仍面临重大挑战。近年来，具备捕捉连续时间动态和潜在状态能力的状态空间模型展现出潜力。然而，早期研究表明Mamba在GSWF任务中表现不佳，需进一步适配优化。为此，本文提出专为GSWF设计的气象状态空间模型（WSSM），引入地理知识替代或补充传统位置编码，以表征绝对时空位置；从粗到细合成多尺度时频特征，建模季节性至极端天气的动态变化。实验表明，该方法有效提升整体预测精度，显著改善极端天气预测能力。在Weather-5K子集上达到当前最优结果，验证了WSSM的有效性。
原文摘要 · Abstract (English)
Global Station Weather Forecasting (GSWF), a prominent meteorological research area, is pivotal in providing timely localized weather predictions. Despite the progress existing models have made in the overall accuracy of the GSWF, executing high-precision extreme event prediction still presents a substantial challenge. The recent emergence of state-space models, with their ability to efficiently capture continuous-time dynamics and latent states, offer potential solutions. However, early investigations indicated that Mamba underperforms in the context of GSWF, suggesting further adaptation and optimization. To tackle this problem, in this paper, we introduce Weather State-space Model (WSSM), a novel Mamba-based approach tailored for GSWF. Geographical knowledge is integrated in addition to the widely-used positional encoding to represent the absolute special-temporal position. The multi-scale time-frequency features are synthesized from coarse to fine to model the seasonal to extreme weather dynamic. Our method effectively improves the overall prediction accuracy and addresses the challenge of forecasting extreme weather events. The state-of-the-art results obtained on the Weather-5K subset underscore the efficacy of the WSSM
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