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作 者:邓少格 胡轶佳 吕廷珍 闫微 DENG Shaoge;HU Yijia;LÜTingzhen;YAN Wei(Unit of 31682 of the Chinese People’s Liberation Army,Lanzhou 730020,Gansu,China;College of Meteorology and Oceanography,National University of Defense Technology,Nanjing 211101,Jiangsu,China)
机构地区:[1]中国人民解放军31682部队,甘肃兰州730020 [2]国防科技大学气象海洋学院,江苏南京211101
出 处:《高原气象》2021年第4期737-746,共10页Plateau Meteorology
基 金:国家重点研发计划项目(2018YFC1505803);国家自然科学基金项目(41675077)。
摘 要:针对青藏高原南部汛期降水预测研究少和预测难度大的问题,通过分析1981-2010年青藏高原南部汛期降水与国家气候中心发布的88项大气环流指数、26项海温指数和16项其他指数年际增量的相关性,采用逐步回归法筛选出与降水相关的最优预测因子组合,在此基础上建立了高原南部汛期降水年际增量与预测因子的物理统计预测模型,并对2011-2019年的汛期降水进行了独立样本回报检验。结果表明,该模型的预测准确率很高,降水年际增量和距平同号率均达到7/9,距平百分率均方根误差为13%,降水相对误差在±15%以内的年份占比高达8/9。可见,该模型能够提高高原南部汛期降水预测能力。最后,利用NCEP/NCAR再分析月平均资料和NOAA海表温度月平均资料研究了预测因子影响高原南部汛期降水的物理机制。The researches on the precipitation prediction in the southern Qinghai-Xizang Plateau during the flood period are few and difficult.To solve this problem,the correlation between the year-to-year increments of precipitation and those of 88 atmospheric circulation indices,26 oceanic indices and 16 other indices released by the National Climate Center from 1981 to 2010 were analyzed.The optimal combination of predictors was selected for the precipitation prediction by using the stepwise regression method.Based on these predictors,a physically-based statistical forecast model was established.Independent sample return tests from 2011 to 2019 were carried out to evaluate this forecast model.The results showed that the accuracy of the model was very high.The prediction of precipitation year-to-year increment and anomaly are correct with the same sign like the observation in 8 years out of 9 years.The RMSE of precipitation anomalies was 13%.The years with relative bias within±15%are 8 years out of 9 years.Thus,this model can improve the forecasting capability of precipitation in the southern Qinghai-Xizang Plateau during the flood period.At last,the physical mechanism of the predictors affecting precipitation during the flood period was explored based on the monthly NCEP/NCAR reanalysis data and the NOAA sea surface temperature data.
分 类 号:P457.6[天文地球—大气科学及气象学]
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