Adaptive Electromagnetic Analysis via Non-Euclidean Manifold Learning for Atmospheric Precipitation Understanding

Abstract

The advent of dual-polarization meteorological sensing systems has revolutionized our capacity to comprehend atmospheric precipitation dynamics through electromagnetic signal analysis. However, the intricate nonlinear relationships within high-dimensional polarimetric signatures present formidable challenges in extracting actionable intelligence for meteorological multimedia applications. This paper presents HyperSpectral-M, a computational framework that enhances polarimetric signal interpretation through systematic manifold learning approaches in non-Euclidean spaces, enabling more precise analysis of complex atmospheric phenomena. The proposed HyperSpectral-M framework addresses the limitations of the existing methods by incorporating two key innovations: a signal disentanglement mechanism (SDM) and a physics-constrained reconstruction paradigm (PCRP). The disentanglement mechanism employs quaternion-based geodesic flow mapping coupled with adaptive spectral decomposition (SD) to project polarimetric signatures onto lower dimensional manifolds while preserving critical microphysical properties. This is augmented by a multiscale differential geometry analyzer that captures intricate spatiotemporal correlations across varying atmospheric conditions. The reconstruction paradigm leverages adversarial manifold alignment with structured probabilistic inference to synthesize high-fidelity radar representations while maintaining electromagnetic consistency constraints. HyperSpectral-M demonstrates significant real-world impact on meteorological applications by improving precipitation nowcasting accuracy by 15–20% compared to operational methods, enabling more timely and accurate flood warnings. Field validation with emergency management agencies shows that reduces false alarm rates by 30–40% while increasing lead time for severe weather warnings by 15–30 min.