Big Data–Enabled Early Warning Systems for Extreme Weather Events in the United States: A Deep Learning Approach

Abstract

The increasing frequency and intensity of extreme weather events in the United States have created an urgent demand for more accurate, scalable, and real-time early warning systems. Conventional forecasting approaches, primarily based on numerical weather prediction models, face limitations in processing large-scale, high-velocity, and heterogeneous climate data, often resulting in delayed responses and reduced predictive precision. This study introduces a big data–enabled early warning framework that integrates advanced deep learning techniques to improve the detection and forecasting of extreme weather phenomena.
The proposed system utilizes multi-source data, including satellite imagery, radar signals, and historical climate records, which are processed through a distributed big data architecture designed for real-time analytics and high computational efficiency. A hybrid deep learning approach, combining convolutional neural networks with spatiotemporal sequence modeling, is employed to capture complex atmospheric dynamics and evolving weather patterns. The framework is evaluated using key performance indicators such as prediction accuracy, latency, and system throughput.
Experimental findings demonstrate that the integrated big data and deep learning framework significantly enhances forecasting accuracy while reducing processing delays and improving scalability compared to traditional and standalone machine learning methods. The results emphasize the capability of data-driven approaches to support proactive disaster management and strengthen climate resilience. This research contributes to the advancement of intelligent early warning systems by providing a robust and scalable solution for real-time extreme weather prediction.