Over the last several decades, substantial progress has been achieved in probabilistic hydrometeorological forecasting theories and applications. However, significant challenges still exist in assessing the uncertainty of complex hydrometeorological processes and improving hydrometeorological predictions, especially extreme hydrometeorological events. This session solicits papers that focus on, but not limit to, (1) addressing uncertainty in hydrometeorological forecasting from a number of sources in both offline and couple systems, and (2) innovative methods in hydrometeorological ensemble forecasting. The former includes uncertainties in forcing data (e.g., quantitative precipitation estimation and meteorological forcing data), initial conditions (e.g., soil moisture and snow status), parameters (e.g., land use and soil texture), model structure (e.g., assumptions, formulations and numerical solutions), and calibration (e.g., single-objective optimization and multi-objective optimizations).  The latter emphasizes integrated ensemble methods to improve hydrometeorological forecasting, verification methods to evaluate probabilistic forecasting, and statistical postprocessing techniques to generate hydrometeorological data products.

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