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| Evaluation of TIGGE Daily Accumulated Precipitation Forecasts over the Qu River Basin, China |
| Li LIU1, Chao GAO1, Qian ZHU2, and Yue-Ping XU1 |
1. Institute of Hydrology and Water Resources, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058;
2. School of Civil Engineering, Southeast University, Nanjing 210096 |
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Abstract Quantitative precipitation forecasts (QPFs) provided by three operational global ensemble prediction systems (EPSs) from the THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE) archive were evaluated over the Qu River basin, China during the plum rain and typhoon seasons of 2009-13. Two post-processing methods, the ensemble model output statistics based on censored shifted gamma distribution (CSGD-EMOS) and quantile mapping (QM), were used to reduce bias and to improve the QPFs. The results were evaluated by using three incremental precipitation thresholds and multiple verification metrics. It is demonstrated that QPFs from NCEP and ECMWF presented similarly skillful forecasts, although the ECMWF QPFs performed more satisfactorily in the typhoon season and the NCEP QPFs were better in the plum rain season. Most of the verification metrics showed evident seasonal discriminations, with more satisfactory behavior in the plum rain season. Lighter precipitation tended to be overestimated, but heavier precipitation was always underestimated. The post-processed QPFs showed a significant improvement from the raw forecasts and the effects of post-processing varied with the lead time, precipitation threshold, and EPS. Precipitation was better corrected at longer lead times and higher thresholds. CSGD-EMOS was more effective for probabilistic metrics and the root-mean-square error. QM had a greater effect on removing bias according to bias and categorical metrics, but was unable to warrant reliabilities. In general, raw forecasts can provide acceptable QPFs eight days in advance. After post-processing, the useful forecasts can be significantly extended beyond 10 days, showing promising prospects for flood forecasting.
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Received: 07 June 2018
Published Online: 26 May 2019
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| Supported by: Supported by the National Key Research and Development Program (2016YFE0122100) and National Natural Science Foundation of China (91547106) |
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Corresponding Authors:
Yue-Ping XU
E-mail: yuepingxu@zju.edu.cn
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