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工程科学与技术:2022,54(6):21-31
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BCC_S2S预报长江上中游流域夏季降水精度评估
李恒1, 朱坚1,2,3
(1.河海大学 水文水资源学院,江苏 南京 210098;2.中国气象局−3.河海大学水文气象研究联合实验室,江苏 南京 210098)
Accuracy Evaluation of BCC_S2S Summer Precipitation Forecast in the Upper and Middle Reaches of the Yangtze River
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投稿时间:2022-07-21    修订日期:2022-09-05
中文摘要: 为评估中国国家气候中心(Beijing Climate Center,BCC)季节–次季节(sub-seasonal to seasonal,S2S)预报模式预测系统(简称为BCC_S2S模式)在长江上中游流域对日降水和夏季极端降水事件的预报性能,基于BCC_S2S模式在2005—2020年每周两次滚动预报未来60 d降水回报的格点数据,把格点预报降水双线性插值到站点,将其与站点观测降水进行对比分析。通过相关系数(correlation,CC)、均方根误差(root mean square error,RMSE)和平均误差(mean error,ME)3个指标,评估模式预报日降水的性能;对于极端降水事件,依照百分位法定义极端降水,基于Heidke 技巧评分(Heidke’s skill score,HSS)指标评估单站极端降水,并利用层次聚类方法划分区域性极端降水类型,进一步评估预报性能。结果表明:BCC_S2S模式在各季节日降水的预报性能随预见期的增加而下降;在预见期大于5~10 d后,进入低预报技巧阶段。将长江上中游划分为6个子区域进一步分析,各区域平均的相关系数仍然是在预见期较短时较高;平均误差表明预报模式在流域中东部区域整体呈现出正偏差,金沙江流域则是负偏差;均方根误差同样在流域中东部偏大。通过分析3个指标的箱型分布图,表明在长江上中游的大多数区域,模式预报6月份的降水精度最佳,误差范围相对较小。对极端降水事件,单站极端降水事件的HSS评分随预见期增加而下降;对大多数区域,模式的预报技巧在月降水总量偏多、极端降水频发的月份较高。针对4类区域性极端降水,模式超前0~10 d预报的多雨带空间分布与观测较为一致,降水量值偏小;超前10 d以上预报时,效果较差。总体上,模式对长江上中游地区的日降水和极端降水事件的预报性能随预见期的增加而下降,6月预报精度相对较好,这可能与6月主要受大范围水汽输送或锋面这样较容易预报的天气系统有关。
Abstract:In order to evaluate the forecast of the sub-seasonal to seasonal (S2S) forecasting system of the Beijing Climate Center (BCC) ,called BCC_S2S model for short, for daily precipitation and summer extreme precipitation events in the upper and middle reaches of the Yangtze River (UMRYR), based on the products of the BCC_S2S model, which reforecasts 60-day precipitation twice a week from 2005 to 2020, bilinear interpolation is implemented to downscale grid point data to station data. Then, various comparison is applied to evaluate the model performance. Three indexes are used to evaluate the daily precipitation forecasting, as correlation coefficient (CC), root mean square error (RMSE) and mean error (ME). For extreme precipitation events defined by percentile method, HSS index is used to evaluate individual station precipitation extremes. Hierarchical clustering method is used to classify regional precipitation extremes, and different precipitation extreme pattern is evaluated by comparing to the observations. The results show that, for the daily precipitation, the BCC_S2S model performance decreases with the increase of the leading period in each season, and the forecast skill stays low when the leading period is longer than 5~10 days. Forecast skill is evaluated detailly in six sub-regions. The CC indexes show similar trend as decreasing with the leading period increasing. And the ME indexes indicate that the model may over estimate precipitation in the middle-east parts of UMRYR, while under estimate that in Jinsha River Basin. The RMSE indexes are high in the middle-east parts of UMRYR. The box plots show that in most parts of UMRYR, the BCC_S2S model performs more stable in June due to the small range of errors. For the precipitation extremes, the HSS decreases with the leading period increasing in forecasting station precipitation extremes. In most parts of the UMRYR, the model performs better in high total-precipitation months. In the perspective of spatial distribution, the model can well represent the strong rainfall pattern for the four regional precipitation extremes in short leading period (0~10 d), but not in long leading period over 10 days. Generally, the BCC_S2S model performance for the daily precipitation and precipitation extremes in the UMRYR shows decreasing trend with the leading period increasing. And it performs better in June, which may be related with the better model performance in representing large scale synoptical systems such as the water vapor transportation or the front.
文章编号:202200745     中图分类号:P435+2    文献标志码:
基金项目:国家重点研发计划项目(2019YFC1510703)
作者简介:第一作者:李恒(1995-),男,硕士生.研究方向:应用水文气象.E-mail:henryhohai327@163.com;通信作者:朱坚,副教授,E-mail:levoca@hhu.edu.cn
引用文本:
李恒,朱坚.BCC_S2S预报长江上中游流域夏季降水精度评估[J].工程科学与技术,2022,54(6):21-31.
LI Heng,ZHU Jian.Accuracy Evaluation of BCC_S2S Summer Precipitation Forecast in the Upper and Middle Reaches of the Yangtze River[J].Advanced Engineering Sciences,2022,54(6):21-31.