Probabilistic Weather

Probabilistic weather forecasting aims to predict not only the most likely weather outcome but also the associated uncertainty, crucial for informed decision-making in various sectors. Current research heavily utilizes deep learning, employing architectures like graph neural networks, transformers, and 3D U-Nets to generate ensembles of forecasts and quantify uncertainty from diverse data sources, including radar, satellites, and numerical weather prediction models. These advancements improve the accuracy and efficiency of medium-range and short-term forecasts, particularly for extreme weather events, leading to better public warnings and resource management. The integration of physical principles into deep learning models is also an active area of investigation, aiming to enhance both predictive skill and interpretability.