Poster Session Probabilistic Hydrometeorological Forecasting and Uncertainty Analysis. (Posters)

Wednesday, 9 January 2019: 4:00 PM-6:00 PM
Hall 4 (Phoenix Convention Center - West and North Buildings)
Host: 33rd Conference on Hydrology
Huiling Yuan, Nanjing Univ., School of Atmospheric Sciences, Nanjing; Kristie J. Franz, Iowa State Univ., Geological and Atmospheric Sciences, Ames, IA and Shugong Wang, NASA GSFC/SAIC, Hydrological Sciences Laboratory, Greenbelt, MD

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.

A Simplified Bivariate Meta-Gaussian Model of Forecast-Observation Dependence Based on the Pseudo Precipitation
Mohammadvaghef Ghazvinian, Univ. of Texas at Arlington, Arlington, TX; and Y. Zhang and D. J. Seo

Evaluation of Precipitation Forecast Uncertainty During Extreme Atmospheric River Events
Liza Ivelisse Diaz-Isaac, SIO, La Jolla, CA; and A. Hamidi, R. Weihs, F. Cannon, A. Martin, and F. M. Ralph

An Approach to Create Probabilistic Streamflow Forecasts from HRRRE Probabilistic Quantitative Precipitation Forecasts
Andrew R. Goenner, Iowa State Univ., Ames, IA; and K. J. Franz and W. A. Gallus Jr.

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