Description:

Probabilistic hydrometeorological forecasting is a comprehensive integration of physical modeling, probability and statistics, and numerical methods. Model ensembles, parameter optimization, data assimilation, and input data quality all can contribute to forecast uncertainties. Significant challenges exist in improving probabilistic forecasts and addressing associated uncertainties in hydrometeorological applications. This session solicits papers on theoretical, experimental, and applied studies focusing on ensemble forecasting and uncertainty analysis in both offline and coupled systems. The topics include but not limited to:

• Integrated ensemble methods to improve hydrometeorological forecasting;
• Advanced optimization methods to calibrate parameters;
• Statistical postprocessing techniques to generate hydrometeorological data products;
• Verification methods to evaluate probabilistic forecasting;
• Uncertainties in forcing data, initial conditions, parameters, and model structure and components; and
• Machine learning methods and applications