A Comparative Review of Machine Learning Algorithms for Weather Prediction: Performance, Challenges, and Future Perspectives

Abstract

Weather prediction is a complex forecasting problem because atmospheric variables interact across time and space, observations are heterogeneous, and useful forecasts must remain reliable across different lead times and weather regimes. Numerical weather prediction (NWP) remains a major operational approach, while machine learning (ML) has rapidly developed as a complementary data-driven approach. This review examines traditional machine learning and modern deep-learning algorithms for weather prediction, with emphasis on Linear Regression, Support Vector Regression, Decision Trees, Random Forest, Gradient Boosting/XGBoost, Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory networks, Graph Neural Networks, Transformers, neural operators and probabilistic generative models. The literature is compared according to data requirements, spatial and temporal representation, computational cost, interpretability, uncertainty estimation and scalability. Representative systems such as FourCastNet, Pangu-Weather, GraphCast, FuXi, GenCast and the ECMWF Artificial Intelligence Forecasting System demonstrate the progression from feature-based prediction toward learned atmospheric-state evolution. Because published studies use different datasets, resolutions, variables, lead times and verification procedures, this review does not claim a universal best algorithm. Instead, it identifies a research opportunity for controlled comparisons using common data, chronological validation, multiple metrics, extreme-event testing and computational-cost reporting.