陈子煊,林朝晖,江志红,俞越.IAP AGCM 4.1对淮河流域夏季降水的预报技巧评估.气象科学,2018,(4):489-497 CHEN Zixuan,LIN Zhaohui,JIANG Zhihong,YU Yue.Evaluation on the forecast skill of summer rainfall over Huaihe River Basin with IAP AGCM4.1.Journal of the Meteorological Sciences,2018,(4):489-497
IAP AGCM 4.1对淮河流域夏季降水的预报技巧评估
Evaluation on the forecast skill of summer rainfall over Huaihe River Basin with IAP AGCM4.1
投稿时间:2017-05-07  修订日期:2017-05-11
DOI:10.3969/2017jms.0045
中文关键词:  IAP AGCM 4.1大气环流模式  季节预测  淮河流域  夏季旱涝  面雨量
英文关键词:IAP atmospheric general circulation model version 4.1  Seasonal prediction  Huaihe River basin  Summer drought and flood  Areal rainfall
基金项目:公益性行业(气象)科研专项重点项目(GYHY201406021);国家重点研发计划项目(2016YFC0402702);国家自然科学基金资助项目(41575095)
作者单位E-mail
陈子煊 南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际联合实验室, 南京 210044
南京信息工程大学 气象灾害预报预警与评估协同创新中心, 南京 210044
中国科学院 大气物理研究所国际气候与环境科学中心, 北京 100029 
 
林朝晖 南京信息工程大学 气象灾害预报预警与评估协同创新中心, 南京 210044
中国科学院 大气物理研究所国际气候与环境科学中心, 北京 100029 
lzh@mail.iap.ac.cn 
江志红 南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际联合实验室, 南京 210044
南京信息工程大学 气象灾害预报预警与评估协同创新中心, 南京 210044 
 
俞越 中国科学院 大气物理研究所国际气候与环境科学中心, 北京 100029
中国科学院大学, 北京 100049 
 
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中文摘要:
      基于中国科学院大气物理研究所新一代大气环流模式IAP AGCM 4.1共30 a (1981—2010年)的集合回报试验结果,评估了模式对淮河流域夏季降水的预报技巧。分析结果表明,模式总体上可以较好地再现出淮河流域夏季平均降水南多北少的空间分布特征,其中模式模拟的6月降水量与观测值的空间相关可达0.93。但降水强度与观测相比具有系统性的偏差,且模式模拟的降水年际变率显著偏弱。基于降水距平相关系数的确定性预报技巧分析表明,模式对流域西南部夏季降水的预测技巧较高,达到0.2以上,且模式对6月降水异常的预测能力相对最好,7月次之。针对淮河不同子流域的预报技巧分析表明,IAP AGCM 4.1对蚌埠、鲁台子、王家坝水文控制站以上集水面积的夏季面雨量异常具有一定的预报技巧,30 a集合回报的时间相关系数分别为0.11、0.13、0.16。基于降水等级的概率预报技巧评估表明,模式对7月淮河流域南部少雨事件具有很好的预报能力,同时对6月流域中部多雨事件的预报技巧也较高。
英文摘要:
      The forecast skill of summer ranifall over Huaihe River Basin (HRB) was evaluated based on the 30-year ensemble hindcasting experiment from 1981 to 2010 of IAP Atmospheric General Circulation Model Version 4.1 (IAP AGCM4.1). The results show that the IAP AGCM4.1 can generally reproduce the observed spatial distribution of summer-averaged rainfall over the HRB, the spatial correlation coefficient between precipitation and observation simulated by the model reaches 0.93 for June. However, systematic bias can be found in rainfall intensity and observation, and the rainfall interannual variability simulated by the model is also significantly weaker than the observation. The deterministic predictive skill of summer rainfall over HRB by IAP AGCM4.1, in terms of anomaly correlation coefficient (ACC), was found to be relatively higher in Southwestern HRB, where the ACC is larger than 0.2. The deterministic rainfall forecast skill for the whole HRB is the highest for June, and July is the second. For the mean areal rainfall of main sub-catchments in Huaihe River, IAP AGCM4.1 does show certain forecast skill, with ACC reaching 0.11 for Bengbu drainage basin, 0.13 for Lutaizi drainage basin, and 0.16 for Wangjiaba drainage basin, respectively. Based on the probabilistic forecast skill using rainfall, it's found that the IAP AGCM4.1 shows a high skill in forecasting the below-normal rainfall events occurred in Southern part of HRB in July, and the above-normal rainfall events in the central part of HRB in June.
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