Hybrid Approaches in Weather Forecasting: From Numerical Models to Deep Learning

This survey reviews the fundamental principles and major approaches used in modern forecasting, with a focus on numerical weather prediction (NWP) and deep learning. NWP remains the cornerstone of operational forecasting, utilizing mathematical equations of atmospheric dynamics to produce high-resolution predictions. The computational demands and strengths of physics-based methods are exemplified by representative models such as the Global Seasonal Forecast System (GloSea5) and the Weather Research and Forecasting (WRF) system. However, deep learning techniques like as convolutional architectures and distribution-based neural networks each have advantages that make them helpful for examining huge meteorological datasets with nonlinear correlations. Validation, uncertainty quantification, model interpretability, and accurate forecasting of extreme weather events remain challenges despite advancements. It is anticipated that future advancements in high-performance computing, multi-source data assimilation, and hybrid methodologies that integrate machine learning and physical modeling will increase the utility and dependability of predictions. This study summarizes recent advancements, points out unresolved issues, and suggests exciting avenues for further weather forecasting research.