A Comprehensive Review on Utilization of Deep Learning for Precipitation Nowcasting via Satellite Data

Abstract

This comprehensive review delves into the cutting-edge applications of deep learning techniques for precipitation nowcasting using satellite data. As climate variability and extreme weather events become increasingly prominent, accurate and timely precipitation predictions are essential for effective disaster management and resource allocation. The paper surveys the recent advancements in deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), showcasing their efficacy in processing and analyzing satellite-derived information. The discussion encompasses the challenges associated with satellite data, such as spatiotemporal complexities and data quality issues, and elucidates how deep learning architectures address these hurdles. The review also highlights noteworthy studies, methodologies, and benchmarks in the field, providing a comprehensive overview of the state-of-the-art approaches for precipitation nowcasting through the lens of deep learning applied to satellite data.