《Journal of Hydrology》刊登”基于对象模型的短期定量降雨预报研究”

作者：Ali Zahraei, Kuo-lin Hsu , Soroosh Sorooshian , Jonathan J. Gourley , Yang Hong , Ali Behrangi

刊物：《水文学杂志》，2013年3月，483卷，1-15页

关键字：短期定量降雨预报；短期预报；风暴追踪

摘要：短期定量降雨预报（简称SQPF）对洪水预警、航行安全以及其他的水利工程有重要的作用。根据当前的研究，提出了一种名为PERCAST（为PERsiann-ForeCAST的缩写）新的对象模型，用以辨别、追踪和及时预报暴风雨。PERCAST利用风暴图像提取风暴特性，从而预测4小时内暴风雨的位置和降雨强度，例如可预测平流场以及风暴强度和大小的变化。PERSIANN-CCS是以前开发的利用遥感信息使用人工神经网络的云分类系统获得降雨估计的降水检索算法，PERCAST与其相耦合作用可预测降雨强度，并提出四项案例研究用以评估该模型的性能。2010年夏天，当前两个案例研究证明该模型能够很好的即时预报单风暴的同时，利用第三个和第四个案例研究在美国本土模拟该模型的性能。结果显示，在预测中考虑风暴增长和衰减（简称GD）的趋势，PERCAST-GD模型根据验证参数能进一步提高降雨可预测性。例如，和PERCAST的比较算法相比，PERCAST-GD探测概率（POD）和假警报率（FAR）就提高了15-20%。

Short-term quantitative precipitation forecasting using an object-based approach

Authors: Ali Zahraei, Kuo-lin Hsu , Soroosh Sorooshian , Jonathan J. Gourley , Yang Hong , Ali Behrangi

Journal: journal of hydrology, Volume 483, 13 March 2013, Pages 1–15

Key words: Short-term quantitative precipitation forecasting; Nowcasting; Storm tracking

Abstract: Short-term Quantitative Precipitation Forecasting (SQPF) is critical for flash-flood warning, navigation safety, and many other applications. The current study proposes a new object-based method, named PERCAST (PERsiann-ForeCAST), to identify, track, and nowcast storms. PERCAST predicts the location and rate of rainfall up to 4 h using the most recent storm images to extract storm features, such as advection field and changes in storm intensity and size. PERCAST is coupled with a previously developed precipitation retrieval algorithm called PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System) to forecast rainfall rates. Four case studies have been presented to evaluate the performance of the models. While the first two case studies justify the model capabilities in nowcasting single storms, the third and fourth case studies evaluate the proposed model over the contiguous US during the summer of 2010. The results show that, by considering storm Growth and Decay (GD) trends for the prediction, the PERCAST-GD further improves the predictability of convection in terms of verification parameters such as Probability of Detection (POD) and False Alarm Ratio (FAR) up to 15–20%, compared to the comparison algorithms such as PERCAST.

原文链接：http://www.sciencedirect.com/science/article/pii/S0022169412008694

翻译：王丽婷； 审核：刘淼