Advanced Learning Techniques for Short-Term Atmospheric Predictions: An Overview

Atmospheric Prediction is a complex task influenced by various factors such as temperature, pressure, air movement, moisture, and the earth’s rotation. Achieving accurate forecasts with high geographical resolution poses significant challenges, requiring substantial computational resources. This study focuses on nowcasting meteorological radar images. Deep learning has emerged as a promising approach for unsupervised representation learning, with next-frame prediction being a particularly intriguing research area in computer vision. This approach involves predicting future images based on prior image information, with applications ranging from robot decision-making to autonomous driving. This review presents the latest advancements in next-frame prediction networks specifically tailored for atmosphere data nowcasting. These networks can be categorized into two main approaches: Machine Learners and deep learners. The study comprehensively analyzes and compare various strategies based on their advantages and limitations, highlighting the benefits and drawbacks of each method. Additionally, potential research directions that hold promise for future investigations in this field are discussed. By identifying the most significant challenges and opportunities, the study aims to inspire further advancements in deep learning methods for atmosphere data nowcasting. Overall, this study provides a comprehensive overview of the current state-of-the-art deep learning techniques for nowcasting atmosphere data, shedding light on their potential applications and suggesting avenues for future research.