Exploring the Role of Neural Networks in Predicting Weather Patterns

Abstract

Weather forecasting is a complex and critical task in meteorology, requiring the analysis of vast amounts of data. Recent advancements in deep learning, specifically neural networks, have shown great promise in enhancing the accuracy of weather prediction models. This article explores the role of neural networks in predicting weather patterns, focusing on applications such as temperature forecasting, precipitation prediction, and severe weather event detection. We examine how neural networks, including deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are transforming traditional weather forecasting methods.