Ensemble Post Processing

Ensemble post-processing refines the outputs of multiple prediction models (ensembles) to improve accuracy and reliability. Current research focuses on leveraging advanced neural network architectures, such as graph neural networks, convolutional neural networks, and generative models (like Vision Transformers and Latent Diffusion Models), to capture complex spatial and temporal dependencies within forecast errors or model outputs across diverse domains, including weather forecasting and natural language processing. These techniques enhance the skill of ensemble predictions, particularly for extreme events, and offer improved uncertainty quantification, leading to more reliable and informative forecasts in various scientific and practical applications.