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 the quality of hydrometeorological predictions, especially high-impact hydrometeorological events. This session solicits papers that focus on, but are not limited 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 might include uncertainties in forcing data (e.g., quantitative precipitation estimation, meteorological forcing data), initial conditions (e.g., soil moisture, heterogeneous geographical conditions), parameters, model structure (physics), and calibration. The latter emphasizes integrated ensemble methods to improve individual hydrologic and atmospheric models, coupled atmosphere–land–hydrology systems, verification methods to evaluate probabilistic hydrometeorological forecasting, and technologies to process systematic errors of hydrometeorological forecasting at different spatial and temporal scales. Work on topics of statistical postprocessing of hydrometeorological model output and assessing the uncertainty of postprocessing are also encouraged.
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