Automated Ensemble Weather Pattern Prediction via Multi-Modal Data Fusion and Dynamic Bayesian Networks

**Abstract:** This paper presents a novel system for enhanced short-term weather forecasting by fusing disparate datasets – radar imagery, satellite data, surface observations, and numerical weather prediction model outputs – through a dynamic Bayesian network (DBN) architecture. Our approach employs a multi-layered evaluation pipeline to assess both the logical consistency and predictive power of ensemble forecasts, culminating in a "HyperScore" that quantifies forecast reliability. This system aims to improve prediction accuracy, particularly in handling rapidly evolving weather phenomena, and offers a commercially viable solution for enhanced operational weather decision support.