Data Driven Weather

Data-driven weather forecasting leverages machine learning to improve the accuracy and efficiency of weather prediction, aiming to rival or surpass traditional physics-based models. Current research focuses on developing and refining deep learning architectures, such as transformers and graph neural networks, often incorporating techniques like data augmentation and ensemble methods to enhance forecast skill, particularly for extreme weather events and longer lead times. These advancements offer the potential for more accurate and timely weather forecasts, leading to improved societal resilience to extreme weather and more efficient resource allocation in various sectors. Furthermore, research is actively exploring methods to integrate data-driven models with existing numerical weather prediction systems to combine the strengths of both approaches.