| 长江流域年降水预测动力-统计降尺度方法及其应用 |
| |
| 引用本文： | 杨雅薇,陈丽娟,沈秉璐.长江流域年降水预测动力-统计降尺度方法及其应用[J].大气科学学报,2021,44(6):835-848. |
| |
| 作者姓名： | 杨雅薇 陈丽娟 沈秉璐 |
| |
| 作者单位： | 上海市气候中心, 上海 200030;国家气候中心/中国气象局气候研究开放实验室, 北京 100081;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, 江苏 南京 210044;成都信息工程大学 大气科学学院/高原大气与环境四川省重点实验室, 四川 成都 610225 |
| |
| 基金项目： | 国家重点研发计划项目“重大自然灾害监测预警与防范”（2018YFC1506006）；上海市科委研发项目（20dz1200401）；中国气象局创新发展专项（CXFZ2021Z033） |
| |
| 摘 要： | 基于站点资料、再分析数据和动力气候模式回报数据，利用经验正交函数分解（EOF，Empirical Orthogonal Function）迭代和年际增量方法，探讨了长江流域年尺度降水异常的动力-统计降尺度预测方法及其应用效果。结果表明，基于再分析数据的年尺度环流场，建立的长江流域年尺度降水异常增量的统计降尺度预测方案，其26 a回报检验的距平相关系数（ACC）平均达0.6，证明该方案具有较高的可预报性。进一步利用模式预测的年尺度环流场，建立了年降水异常增量的动力-统计降尺度预测方案，其ACC平均为0.42，显示了较高的回报技巧，远优于模式直接输出的年降水动力预报结果。通过分析调制年降水预报技巧高低的因素发现，赤道中东太平洋年平均海温距平为负值时，预报技巧更高，ACC平均达0.5以上。在拉尼娜发展年或拉尼娜持续年的冷水背景下，利用EOF迭代选取的特征向量偏多时，多尺度的大气环流信息被纳入预测模型中作为预测信号，预测技巧得到了提高。
|
| 关 键 词： | 年降水 EOF迭代 年际增量 动力-统计降尺度方法 |
| 收稿时间： | 2021/8/31 0:00:00 |
| 修稿时间： | 2021/9/22 0:00:00 |
Dynamic-statistical downscaling method for annual precipitation prediction in Yangtze River Basin and its application |
| |
| YANG Yawei,CHEN Lijuan,SHEN Binglu.Dynamic-statistical downscaling method for annual precipitation prediction in Yangtze River Basin and its application[J].大气科学学报,2021,44(6):835-848. |
| |
| Authors: | YANG Yawei CHEN Lijuan SHEN Binglu |
| |
| Institution: | Shanghai Climate Center, Shanghai 200030, China;Laboratory for Climate Studies, China Meteorological Administration/National Climate Center, Beijing 100081, China;Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China; School of Atmospheric Sciences/Plateau Atmosphereand Environment Key Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu 610225, China |
| |
| Abstract: | Based on the station data, reanalysis data and dynamic climate model hindcast data, a dynamic-statistical downscaling prediction method of annual precipitation anomaly in the Yangtze River Basin and its application skill are discussed by using the empirical orthogonal decomposition (EOF) iteration and interannual increment method.Results show that based on the annual scale circulation field of reanalysis data, a statistical downscaling prediction scheme for annual scale precipitation anomaly increment over the Yangtze River Basin is established.The average anomaly correlation coefficient (ACC) of 26-year hindcast test can reach 0.6, which proves that the scheme has high predictability.A dynamic-statistical downscaling prediction scheme of annual precipitation anomaly increment is furth erestablished by using the annual scale circulation predicted by the model.The average ACC is 0.42, showing a high hindcast skill.The skill is much better than that of the directly output precipitation of the model.By analyzing the factors affecting the skill of annual precipitation prediction, it shows that when the annual average SST anomaly in equatorial central and eastern Pacific is negative, the prediction skill is higher, and the average ACC is more than 0.5.Under the cold water background of La Niña development year or La Niña duration year, more eigenvectors are selected by EOF iteration, which are incorporated into the multi-scale atmospheric circulation information as the prediction signal, and the prediction skill of annual precipitation anomalyis improved. |
| |
| Keywords: | annual precipitation EOF iteration interannual increment dynamic-statistical downscaling method |
|
| 点击此处可从《大气科学学报》浏览原始摘要信息 |
| 点击此处可从《大气科学学报》下载免费的PDF全文 |
|
