Traditional Numerical Weather Prediction

Traditional numerical weather prediction (NWP) aims to forecast atmospheric conditions using mathematical models of physical processes. Current research heavily focuses on developing and comparing data-driven alternatives, employing deep learning architectures like convolutional neural networks (CNNs), transformers, and graph neural networks (GNNs) to leverage vast datasets for improved accuracy and efficiency. These machine learning models are showing competitive or superior performance to traditional NWP in various forecasting tasks, from short-term nowcasting to medium-range predictions, impacting sectors like renewable energy and disaster preparedness. The field is also exploring methods for uncertainty quantification and integrating machine learning approaches with established NWP techniques for enhanced reliability.