Theoretical Assessment for Weather Nowcasting Using Deep Learning Methods

Weather is influenced by various factors such as temperature, pressure, air movement, moisture/water vapor, and the Earth's rotating motion. Accurate weather forecasting at a high geographical resolution is a complex and computationally expensive task. This study employs a nowcasting approach using meteorological radar images. Building upon the principles of unsupervised representation in deep learning, we delve into the emerging field of next-frame prediction in computer vision. This research focuses on predicting future images based on prior image data, with applications ranging from robot decision-making to autonomous driving. We present the latest advancements in next-frame prediction networks, categorizing them into two approaches: Machine Learners and deep learners. We discuss the merits and limitations of each approach, comparing them based on various parameters. Finally, we outline potential directions for future research in this field, aiming to make weather forecasting more precise and accessible.