摘要
本文利用在线滤波代替Lanczos滤波,建立实时的累积高频风指数并构建ENSO强度预测模型。结果显示,在线滤波定义的累积高频风事件与Lanczos滤波有着类似的时空分布特征,这一大气信号与海洋充放电过程是决定ENSO成熟期强度的关键信号,且在冷暖位相下呈高度非对称特征。La Niña成熟期强度主要与2月热带太平洋的暖水体积及前一年12月至当年4月赤道中西太平洋累积东风事件紧密相关。相较之下,El Niño成熟期强度则与热带太平洋更大范围的海洋充电过程和东扩的西风事件紧密联系。基于上述非对称热带海气信号构建的ENSO强度预测模型可以稳定有效且较准确地预测ENSO强度。模型在6个月的预报时效下预测2023/2024年冬季Ni1o3.4约为1.9℃,为一次强El Niño事件。
Instead of Lanczos filtering,this paper utilizes an online filtering scheme to compute the real-time accumulated high-frequency wind index and construct the ENSO amplitude forecast model.Results show that the online-filtered high-frequency wind events exhibit similar spatiotemporal features to the original method.These high-frequency atmospheric conditions,along with the relatively slower oceanic recharged/discharged process,play important roles in determining the ENSO amplitude at the peak phase,and exhibit notable spatial asymmetry for El Niño and La Ni a events.La Ni a amplitude at the peak season is closely associated with the accumulated Easterly Wind Events(EWEs)over the equatorial western Pacific from the previous December to April and the discharged state in the western equatorial Pacific during February.In contrast,the amplitude of El Niño events is sensitive to the accumulated Westerly Wind Events(WWEs)over the eastern equatorial Pacific and the recharged state extending from the equatorial western to central Pacific.By utilizing these asymmetric oceanic and atmospheric preconditions of El Niño and La Niña events,a statistical model was established to accurately forecast the ENSO amplitude,exhibiting comparable prediction skills and robustness to the original model based on Lanczos filtering.The newly-established model predict that the 2023/2024 El Niño will be a strong event with the magnitude of nearly 1.9℃.
作者
宣卓林
张文君
姜枫
XUAN Zhuolin;ZHANG Wenjun;JIANG Feng(Zhejiang Meteorological Observatory,Hangzhou 310017,China;Key Laboratory of Meteorological Disaster of Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science&Technology,Nanjing 210044,China)
出处
《气象科学》
2024年第1期1-11,共11页
Journal of the Meteorological Sciences
基金
国家重点研发计划资助项目(2022YFF0801602)。
关键词
在线滤波
实时累积高频风指数
ENSO强度预测
online filtering
operational accumulated high-frequency wind index
ENSO amplitude forecast
