Machine Learning and Deep Learning Applications in Weather and Climate Studies: A Systematic Review

Abstract

Weather forecasting has evolved as the interest of numerous scholars from diverse study areas owing to its impact on human existence. Artificial intelligence (AI) frameworks have advanced in the last decade, combined with the widespread availability of massive weather and climate datasets and the advent of computational technology. It has motivated many researchers to investigate hidden hierarchical patterns in large volumes of datasets for weather and climatological forecasting. This comprehensive review paper highlights the evolving landscape of weather and climate research through the lens of machine learning (ML) and deep learning (DL) methodologies. As AI continues to redefine scientific inquiry, the latest advancements, applications, and challenges in leveraging ML and DL for meteorological and climatological insights has been documented. Surveying a broad spectrum of research, the review encapsulates the transformative impact of these intelligent systems on short-term weather forecasting, prediction of extreme events, climate forecasting, and refinement of weather and climate models. As a compendium of current knowledge, it serves as a guiding resource for researchers, practitioners, and policymakers navigating the dynamic intersection of climate science and machine learning, laying the groundwork for future advancements in the applications of AI frameworks in weather and climate prediction.