Abstract
This study addresses the uncertainty of High-Resolution Rapid Refresh (HRRR) quantitative precipitation forecasts (QPFs), which were recently appended to the operational hydrologic forecasting framework. In this study, we examine the uncertainty features of HRRR QPFs for an Iowa flooding event that occurred in September 2016. Our evaluation of HRRR QPFs is based on the conventional approach of QPF verification and the analysis of mean areal precipitation (MAP) with respect to forecast lead time. The QPF verification results show that the precipitation forecast skill of HRRR significantly drops during short lead times and then gradually decreases for further lead times. The MAP analysis also demonstrates that the QPF error sharply increases during short lead times and starts decreasing slightly beyond 4-h lead time. We found that the variability of QPF error measured in terms of MAP decreases as basin scale and lead time become larger and longer, respectively. The effects of QPF uncertainty on hydrologic prediction are quantified through the hillslope-link model (HLM) simulations using hydrologic performance metrics (e.g., Kling-Gupta efficiency). The simulation results agree to some degree with those from the MAP analysis, finding that the performance achieved from the QPF forcing decreases during 1-3-h lead times and starts increasing with 4-6-h lead times. The best performance acquired at the 1-h lead time does not seem acceptable because of the large overestimation of the flood peak, along with an erroneous early peak that is not observed in streamflow observations. This study provides further evidence that HRRR contains a well-known weakness at short lead times, and the QPF uncertainty (e.g., bias) described as a function of forecast lead times should be corrected before its use in hydrologic prediction.
Recommended Citation
B. C. Seo et al., "High-resolution QPF Uncertainty And Its Implications For Flood Prediction: A Case Study For The Eastern Iowa Flood Of 2016," Journal of Hydrometeorology, vol. 19, no. 8, pp. 1289 - 1304, American Meteorological Society, Aug 2018.
The definitive version is available at https://doi.org/10.1175/JHM-D-18-0046.1
Department(s)
Civil, Architectural and Environmental Engineering
Publication Status
Full Access
Keywords and Phrases
Flood events; Forecast verification/skill; Hydrologic models; Numerical weather prediction/forecasting; Short-range prediction
International Standard Serial Number (ISSN)
1525-7541; 1525-755X
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2023 American Meteorological Society, All rights reserved.
Publication Date
01 Aug 2018
