Umeå University's logo

Accurate Cloud Behavior Prediction (CBP), also referred to as forecasting in this context, is essential for Solar Power Prediction (SPP), as well as for weather forecasting, climate analysis, and satellite imaging. However, the nonlinear and dynamic nature of clouds, combined with other limitations, presents significant challenges to advancing CBP. Recent developments, particularly the integration of Machine Learning (ML), Numerical Weather Prediction (NWP), and other innovative approaches, show strong potential for improving CBP and, in turn, enhancing SPP and related applications. This review presents a bibliometric analysis of 467 publications from 1970 to 2024, retrieved from the Scopus database using CBP-related keywords. It identifies trends, influential studies, major subject areas, leading authors, contributing countries, and key publishers. The study further categorizes the essential steps in CBP and provides a detailed review of the most relevant literature on cloud cover, cloud motion (including vector-based methods), and cloud image prediction. Additionally, it examines critical factors affecting model performance and introduces a framework for evaluating predictive methods based on input types, methodologies, prediction horizons, results, and evaluation metrics. Several key challenges are highlighted, including the nonlinearity of cloud behavior, limited data availability, image quality issues, and model accuracy. In response, actionable recommendations are offered, such as expanding data sources, applying hybrid imaging and modeling approaches, managing uncertainty, improving postprocessing techniques, and incorporating cloud content estimation. Given the relatively limited research in this field, this study serves as a valuable benchmark for researchers, engineers, and policymakers engaged in real-time SPP and other cloud-dependent domains.