Identifying Climate Anomalies with Simulated Antenna Data, Sensor Arrays, and Spiking Neural Networks

This paper proposes a novel framework for climate anomaly detection by integrating antenna array-derived environmental data with Spiking Neural Networks (SNNs), a biologically inspired computational approach. The methodology systematically captures key climatic parameters, including temperature, wind, and moisture, allowing a precise and structured analysis. By encoding temporal and spatial dynamics, SNNs provide an advanced mechanism for detecting subtle patterns and anomalies that conventional methods may overlook. The study demonstrates the effectiveness of simulated antenna-based climate data in identifying temperature trend anomalies, reinforcing the potential of neural architectures in climate variability analysis. The results highlight the ability of SNNs to improve anomaly detection through efficient processing of time-sensitive and spatially complex datasets. The proposed pipeline not only addresses methodological gaps but also improves the broader understanding of climate disturbances by incorporating innovative technology-driven solutions. This research underscores the importance of integrating computational intelligence into climate studies, contributing to more accurate and scalable environmental monitoring systems.