Ensemble Forecast

Ensemble forecasting aims to improve prediction accuracy and quantify uncertainty by generating multiple forecasts from a single model or combining predictions from diverse models. Current research emphasizes the use of deep learning architectures, such as diffusion models, convolutional neural networks (U-Nets), transformers (including Vision Transformers and Spherical Fourier Neural Operators), and recurrent neural networks, often integrated with data assimilation techniques or post-processing steps to enhance forecast skill and calibration. This approach is significantly impacting weather and climate prediction, improving the accuracy and reliability of forecasts for various applications, including extreme weather warnings, renewable energy planning, and climate risk assessment. Furthermore, research is actively exploring efficient ensemble generation methods to reduce computational costs and improve the representation of uncertainty, particularly for extreme events.