地球科学进展

地球科学进展 ›› 2014, Vol. 29 ›› Issue (11): 1212 -1225. doi: 10.11867/j.issn.1001-8166.2014.11.1212

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海洋可预报性和集合预报研究综述
王辉( ), 刘娜 *( ), 李本霞, 李响   
  1. 国家海洋环境预报中心国家海洋局海洋灾害预报技术研究重点实验室, 北京 100081
  • 出版日期:2014-11-27
  • 通讯作者: 刘娜 E-mail:wangh@nmefc.gov.cn;liuna@nmefc.gov.cn
  • 基金资助:
    国家科技支撑计划项目“全球海洋环境数值预报关键技术系统集成研究及应用”(编号：2011BAC03B00);国家自然科学基金青年基金项目“东中国海热收支年代际变化及其影响因素研究”(编号: 41106023)资助

An Overview of Ocean Predictability and Ocean Ensemble Forecast

Hui Wang( ), Na Liu( ), Benxia Li, Xiang Li   

  1. Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Beijing 100081, China
  • Online:2014-11-27 Published:2014-11-20
全文 ( 10 ) 下载PDF ( 1589 )

海洋是高度复杂的非线性动力系统, 由于海洋初值和数值模式本身存在无法避免的误差, 海洋数值预报具有不确定性。通过理解和认识海洋不同时空尺度运动的特征和规律, 定量估计和预测海洋动力系统的可预报性, 研究预报误差产生的原因及其增长和传播机制, 探讨减小预报误差的方法和延长可预报时限的途径, 对于改进海洋预报系统、提高预报技巧, 具有重要意义。系统回顾了海洋可预报性及其应用的研究进展, 论述了海洋可预报性的概念、分类以及国内外的研究现状, 其中重点介绍了常用的奇异向量法、李亚普诺夫指数法和繁殖向量法等3种动力学方法以及海洋集合预报研究现状, 最后对海洋可预报性的未来发展方向和应用前景给以展望。

关键词: 海洋预报, 可预报性, 预报不确定性

Ocean is a highly complex and nonlinear dynamical system. The inevitable errors in both data and numerical models lead to uncertainties in ocean numerical prediction. By understanding features and properties in the ocean on multiple scales, it is important to quantify and estimate the predictability of the ocean, and analyze the reasons and mechanism of error growth. The efforts focus on investigating the method to reduce the uncertainties and errors in forecasting and increase the time limit of ocean predictability. The advances will result in improved marine forecasting models and forecasting skill. Understanding limitations and identifying the research needed to increase accuracy will lead to fundamental progress in ocean forecast, which is of great significance. The present study described and illustrated the mechanics and computations involved in modeling and predicting uncertainties for ocean prediction and its modern applications. Firstly, it discussed the fundamental concept and classification of the ocean predictability. The research status of ocean predictability is introduced including the dynamics methodologies and the ocean ensemble prediction. Three of the dynamical computational methodologies including the singular vector, Lyapunov exponent and bred vector method were introduced. Three ocean ensemble prediction methods including initial condition ensemble, multi-model ensemble and atmospheric forcing ensemble were described and illustrated. Finally, this paper gave a future prospective of ocean predictability and its application.

Key words: Ocean forecast, Predictability, Prediction uncertainty.

中图分类号: 

图1 模式具有初始误差和参数误差时黑潮路径的预报结果 [ 60 ] 黑色实线表示CNOP-I误差, 黄线表示CNOPRI-P误差, 红线表示CNOPTAU-P误差, 蓝线表示CNOPAH-P误差, 灰线表示CNOPALL-P, 黑色虚线表示参考态黑潮大弯曲路径。图中黑潮流轴由海洋上层厚度520m等值线表征
Fig.1 Prediction of Kuroshio path under initial condition errors and model parameter errors [ 60 ] Black solid line represents CNOP-I case; yellow line represents CNOPRI-P case; red line represents CNOPTAU-P case; blue line represents CNOPAH-P case; gray line represents CNOPALL-P case; black dashed line represents reference state of the Kuroshio large meander. The Kuroshio axis is represented as 520m contour of the upper-layer thickness
图2 月平均海表面温度的年平均预报时限(月)的空间分布 [ 88 ]
Fig.2 Spatial distribution of the annual mean predictability limit (in month) of monthly SST [ 88 ]
图3 东澳大利亚海流区域海表面高度 [ 92 ] (a)~(d)分析场;(e)~(h)7天控制预报场;(i)~(l)集合平均繁殖向量(等值线)和第7天预报误差(填色);(m)~(p)4个不同的繁殖向量等值线。第1-4列分别表示2008年3月4, 11, 18, 25日
Fig.3 Surface height in the region of East Australian Current [ 92 ] (a)~(d) Analysis fields;(e)~(h) 7 days control forecasts;(i)~(l) Comparison of ensemble averaged bred vectors (contours) and day 7 forecast error (shaded);(m)~(p)Contours for each of the 4 individual bred vectors. Columns 1~4 depict results valid for the 4th, 11th, 18th and 25th March 2008 respectively
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