Onemiss/PW-FouCast-Dataset

Extending Precipitation Nowcasting Horizons via Spectral Fusion of Radar Observations and Foundation Model Priors

This is the dataset repository for PW-FouCast.

🌟 Model Overview

PW-FouCast addresses the challenge of representational heterogeneities between high-resolution radar imagery and large-scale meteorological data. By leveraging Pangu-Weather forecasts as spectral priors within a Fourier-based backbone, the model effectively bridges the gap between atmospheric dynamics and local convective patterns.

Key Features

• Pangu-Weather-guided Frequency Modulation (PFM): Aligning spectral magnitudes and phases with physical meteorological priors to ensure physically consistent forecasts.
• Frequency Memory (FM): A learned repository of ground-truth spectral patterns that dynamically corrects phase discrepancies and preserves complex temporal evolutions (e.g., expansion/contraction).
• Inverted Frequency Attention (IFA): A residual-reinjection mechanism designed to recover high-frequency details typically lost during spectral filtering, maintaining sharp structural fidelity in long-term predictions.
• Extended Horizon: Demonstrates superior performance on SEVIR and MeteoNet benchmarks, significantly mitigating performance decay in long-lead nowcasting.