This session seeks contributions from the research, operational, and user communities that utilize precipitation datasets in applications that address scientific and societal needs from flood forecasts to climate impact studies, including novel uses of artificial intelligence/machine learning in those applications. Uncertainties in precipitation data have a significant impact on the usefulness of these applications. Thus, this session also seeks contributions that present advances in error characterization and uncertainty quantification in diverse precipitation datasets and enhance our understanding on how the uncertainties propagate to hydrological processes and thus affecting the modeling and data-assimilation in these applications. Presentations that present precipitation applications and various error components in precipitation datasets are welcome. The session can also host broad topics including evaluation efforts.

• A - Atmospheric Sciences
• NH - Natural Hazards