Tornado Prediction and Tracking Using Cloud-Based Data Solutions

Tornado prediction and tracking have been significantly enhanced by leveraging real-time data insights, which improve accuracy in forecasting and emergency response. This paper presents a novel approach to tornado prediction using cloud-based solutions powered by serverless technology for scalable and efficient meteorological data processing. The system processes critical meteorological parameters such as wind speed, atmospheric pressure, and temperature to enable accurate forecasting. Additionally, machine learning algorithms are integrated for real-time data analysis, allowing instant predictions and actionable insights. By transforming traditional weather analysis methodologies, this approach aims to minimize severe weather risks and enhance preparedness. Experimental evaluation of the proposed system demonstrated a prediction accuracy of 88%, which surpasses traditional statistical models and recent deep learning approaches. The serverless deployment architecture reduced processing latency by approximately 35% compared to conventional cloud-hosted frameworks. Furthermore, the integration of real-time visualization modules, including heatmaps and time-series risk graphs, significantly improved interpretability and supported faster decision-making during severe weather events. These outcomes validate the effectiveness of the TORNADYN algorithm and the cloud-based infrastructure in enhancing tornado forecasting capabilities.