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 can all 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 are not limited to:

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

Submitters: Huiling Yuan, Nanjing Univ., Nanjing, China and Yu Zhang, Department of Civil Engineering, Univ. of Texas at Arlington, Arlington, TX