WSSM: Geographic-enhanced hierarchical state-space model for global station weather forecast
Journal:
arXiv
Published Date:
Jan 20, 2025
Abstract
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
