几种统计模型对热带印度洋海温异常的预报

doi:10.3969/j.issn.1001-909X.2018.01.001

海洋学研究 ›› 2018, Vol. 36 ›› Issue (1): 1-15.DOI: 10.3969/j.issn.1001-909X.2018.01.001

• 研究论文 • 下一篇

几种统计模型对热带印度洋海温异常的预报

方玥炜^1,2, 唐佑民^*1,2,3, 李俊德^1,2, 刘婷^1,2

1.卫星海洋环境动力学实验室,浙江杭州 310012;
2.国家海洋局第二海洋研究所,浙江杭州 310012;
3.北英属哥伦比亚大学环境科学与工程学院,BC省乔治王子城V2N4Z9

收稿日期:2017-03-20 修回日期:2017-05-16 出版日期:2018-03-15 发布日期:2022-11-21
通讯作者: * 唐佑民(1963-),男,研究员,主要从事海洋资料同化和可预报性研究。E-mail: ytang@sio.org.cn
作者简介:方玥炜(1991-),女,浙江杭州市人,主要从事统计预报研究。E-mail: fangyw@sio.org.cn;15867184750
基金资助:
国家自然科学基金面上项目资助(41276029);“全球变化与海气相互作用”专项项目资助(CASI-IPOVAI-06);国家自然科学基金重点项目资助(41530961);国家海洋局第二海洋研究所业务专项项目资助(JG1617,JG1619);卫星海洋环境动力学国家实验室自主课题资助(SOEDZZ1516);国家重点研发计划资助(2016YFC1401703,2016YFC1401701)

Several statistical models to predict tropical Indian Ocean Sea Surface Temperature Anomaly

FANG Yue-wei^1,2, TANG You-min^*1,2,3, LI Jun-de^1,2, LIU Ting^1,2

1. State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou 310012, China;
2. Second Institute of Oceanography, SOA, Hangzhou 310012, China;
3. Environmental Science and Engineering, University of Northern British Columbia, Prince George V2N4Z9,Canada

Received:2017-03-20 Revised:2017-05-16 Online:2018-03-15 Published:2022-11-21

摘要/Abstract

摘要： 本文利用神经网络模型、多元线性回归模型和马尔科夫模型分别建立了统计预报模型,对热带印度洋海表温度异常(SSTA)和印度洋偶极子(IOD)指数进行了63 a的长时间回报实验,并详细比较了线性和非线性统计预报模型的差异。结果表明:统计模型对IOD指数的预报技巧和现有动力模式预报技巧相差不大,对偶极子指数(DMI)有效预报时效为3个月,东极子指数(EIO)为5～6个月,西极子指数(WIO)达到8～9个月。IOD事件强烈的季节锁相特性使得对秋季的DMI指数可以提前4个月做出有效预报。加入同期的ENSO指数来预报IOD指数,能有效地提高IOD预报技巧,特别是对IOD峰值的预报。复杂的神经网络模型和简单的多元线性回归模型在对SSTA和IOD指数的预报具有同等的效果。

关键词: 统计预报, 印度洋偶极子, 神经网络, ENSO

Abstract: The tropical Indian Ocean Sea Surface Temperature Anomaly (SSTA) and the Indian Ocean Dipole (IOD) indices are predicted, using the multiple linear regression model, the Markov model and the neural network model respectively. 63 years' hindcast experiments are set up to compare the differences between linear and nonlinear statistical models in detail. And the results reveal that the statistical models are little different from the complicated dynamic model. Their skillful prediction (correlation coefficients above 0.5) could reach 3 months for DMI, about 5-6 months for EIO index and 8-9 months for WIO. Since the IOD event has a strong seasonal phase lock, the DMI can be predicted previously for 4 months in fall. When the synchronistic ENSO index is added as a predictor, the prediction skill, especially the IOD peak, will be improved. The complicated neural network and the simple regression model are proved to be with a similar prediction skill.

Key words: statistical prediction, IOD, neural network, ENSO

中图分类号:

P732.6

方玥炜, 唐佑民, 李俊德, 刘婷. 几种统计模型对热带印度洋海温异常的预报[J]. 海洋学研究, 2018, 36(1): 1-15.

FANG Yue-wei, TANG You-min, LI Jun-de, LIU Ting. Several statistical models to predict tropical Indian Ocean Sea Surface Temperature Anomaly[J]. Journal of Marine Sciences, 2018, 36(1): 1-15.

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几种统计模型对热带印度洋海温异常的预报

Several statistical models to predict tropical Indian Ocean Sea Surface Temperature Anomaly

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