PG-MoE: Physically Grouped Mixture-of-Experts with Soft Track Generation for Multi-Horizon Typhoon Track Prediction

Accurate multi-horizon typhoon track forecasting is crucial for disaster prevention and mitigation, yet existing deep learning methods for ensemble forecast post-correction suffer from two key limitations: they discard probabilistic uncertainty from ensemble members, leading to error accumulation, and single networks lack capacity for diverse typhoon dynamics, while conventional MoE lacks physical priors. We propose PG-MoE, integrating soft-track probabilistic encoding and physically grouped mixture-of-experts. The soft-track module converts multi-source ensemble forecasts into standardized probabilistic representations via a downsampling-free fully convolutional network and confidence ellipse fitting, preserving uncertainty evolution. The MoE partitions experts into 23 homogeneous groups based on six physical attributes (e.g., lead time, intensity, moving distance) with a hierarchical sparse router and two-stage decoupled training. On a Northwest Pacific typhoon test dataset (2024–2025), PG-MoE significantly outperforms mainstream deep learning baselines and ECMWF deterministic forecasts within the critical 12–72H window. Ablations validate the core components. Our method offers a general probabilistic input pipeline and a physics-informed MoE paradigm, balancing accuracy and interpretability. Current framework supports MLP experts and track data only, with future extensions to multimodal observations and adaptive partitioning.