Joint Poster Session 2 Probabilistic Hydrometeorological Forecasting and Uncertainty Analysis - Posters

Tuesday, 14 January 2020: 4:00 PM-6:00 PM
Hall B1 (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.

The Scale Sensitivity Experiments of Precipitation Neighborhood Ensemble Probability Method
Xueqing Liu, Chinese Academy of Meteorological Sciences Zhejiang Branch, Hangzhou, China; and J. Chen

Merging Soil Moisture Multi-model Products Based on Dynamic Bayesian Model Averaging
Yong Chen, School of Atmospheric Sciences, Nanjing, China; and H. Yuan

The Uncertainty of GFS over the Eastern Asia: Error Analysis and Correction Using Optical Flow Method
Xue Zhong Wang, National Univ. of Defense Technology, Nanjing, China; and J. Wang, H. Huang, W. Zhang, B. Hu, and F. Lin

Sensitivity of ensemble forecast verification to model bias
Jingzhuo Wang, CMA(China Meteorological Administration), Beijing, China; and J. Chen and J. Du

Streamflow Forecasting Using Long Short-Term Memory Network
Lingling Ni, Nanjing University, Nanjing, China; and D. Wang and J. Wu

A Multi-Scale Post-Processing Technique for Short-to-Long Range Ensemble Streamflow Prediction
Babak Alizadeh, Univ. of Texas, Arlington, TX; and R. A. Limon, D. J. Seo, H. Lee, and J. D. Brown

Evaluation of GloFASv2 hydrological forecast skill at the global scale
Shaun Harrigan, ECMWF, Reading, United Kingdom; and E. Zsoter, D. A. Lavers, L. Alfieri, C. Prudhomme, H. Cloke, D. S. Richardson, P. Salamon, E. Stephens, and F. Pappenberger

Seasonal Forecast of Early-summer Rainfall at Stations in South China using Statistical Downscaling and BMA
Zheng Lu, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China; and Y. Guo and J. Zhu

Coupled rainfall-runoff hydrometric network design method based on information theory
Wenqi Wang, Nanjing University, Nanjing, China; and D. Wang and Y. Wang

How Circulation Adjustment Affects the Axial Error of the Precipitation Forecast
Hong Huang, National Univ. of Defense Technology, Nanjing, China; and Y. Liu, X. Z. Wang, J. Wang, and W. Zhang

Assessment of the Sea-surface Temperature Predictability based on Multimodel Hindcasts
SHOUWEN ZHANG, National Marine Environmental Forecasting Center, BEIJING, China

Reducing Bias in Flash Drought Forecasts by Optimizing Parameters in Noah-MP Multiple Parameterization Schemes
Ye Tian, Nanjing University of Information Science and Technology, Nanjing, China; and Z. L. Yang and J. Liang

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner