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Advances in Meteorology
Volume 2016 (2016), Article ID 8476720, 13 pages
Research Article

Evaluation and Correction of Quantitative Precipitation Forecast by Storm-Scale NWP Model in Jiangsu, China

1State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Science, 46 Zhongguancun South Street, Haidian District, Beijing 100081, China
2Jiangsu Institute of Meteorological Science, 16 Kunlun Road, Nanjing, Jiangsu 210009, China
3National Meteorological Center, China Meteorological Administration, 46 Zhongguancun South Street, Haidian District, Beijing 100081, China

Received 25 March 2016; Accepted 11 July 2016

Academic Editor: Enrico Ferrero

Copyright © 2016 Gaili Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the development of high-performance computer systems and data assimilation techniques, storm-scale numerical weather prediction (NWP) models are gradually used for short-term deterministic forecasts. The primary objective of this study is to evaluate and correct precipitation forecasts of a storm-scale NWP model called the advanced regional prediction system (ARPS). The evaluation and correction consider five heavy precipitation events that occurred in the summer of 2015 in Jiangsu, China. The performances of the original and corrected ARPS precipitation forecasts are evaluated as a function of lead time using standard measurements and a spatial verification method called Structure-Amplitude-Location (SAL). In general, the ARPS could not produce optimal forecasts for very short lead times, and the forecast accuracy improves with increasing lead time. The ARPS overestimates precipitation for all lead times, which is confirmed by large bias in many forecasts in the first and second quadrant of the diagram of SAL, especially at the 1 h lead time. The amplitude correction is performed by matching percentile values of the ARPS precipitation forecasts and observations for each lead time. Amplitude correction significantly improved the ARPS precipitation forecasts in terms of the considered performance indices of standard measures and A-component and S-component of SAL.