Joint Session 20 Probabilistic Hydrometeorological Forecasting and Uncertainty Analysis. Part I

Tuesday, 14 January 2020: 1:30 PM-2:30 PM
253A (Boston Convention and Exhibition Center)
Hosts: (Joint between the 34th Conference on Hydrology; the 30th Conference on Weather Analysis and Forecasting (WAF)/26th Conference on Numerical Weather Prediction (NWP); and the 26th Conference on Probability and Statistics )
Huiling Yuan, Nanjing Univ., School of Atmospheric Sciences, Nanjing
Kristie Franz, Iowa State University, Ames, IA; Shugong Wang, NASA GSFC/SAIC, Hydrological Sciences Laboratory, Greenbelt, MD and Christopher J. Melick, 557th Weather Wing, 16th Weather Squadron, Offutt Air Force Base, NE

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.

2:00 PM
What Makes a "Good" Probabilistic Forecast?
K. Scharfenberg, NWS, Boulder, CO; and A. Bol, R. Graham, P. L. Heinselman, T. Alcott, H. E. Brooks, P. Skinner, K. Hoogewind, and A. Lamers
2:15 PM
Deeper Insights into Winter Weather via Probabilistic Snowfall Forecasts from The Weather Company
James I. Belanger, The Weather Company, Brookhaven, GA; and J. K. Williams, J. P. Koval, J. McDonald, P. Bayer, N. McGillis, L. Howard, and R. L. Weeks
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner