Use of machine learning technique in predicting ENSO and IOD
发布日期: 2020/11/09

报告人:罗京佳,南京信息工程大学教授、气候与应用前沿研究院院长

邀请人:马建

时间:2020年11月4日,周三,13:00~14:00

ZOOM会议号:93551844588  密码:282441

https://zoom.com.cn/j/93551844588

 

报告摘要:

The tropical climate variations, such as El Niño/Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) can often bring about global and regional climate extremes and ecosystem impacts. Skillful long-lead forecasts of those major year-to-year climate variations would therefore be valuable for reducing socio-economic losses. But despite decades of effort, skillful forecast of ENSO and IOD events at lead times of more than one year remains a big challenge. Here we show that a statistical forecast model employing a deep-learning approach produces skillful ENSO forecasts for lead times of up to one and a half years. To circumvent the limited amount of observational data, we first train a convolutional neural network (CNN) model by use of historical CMIP5 simulations, which is further tuned based on observational reanalysis during 1871-1973. During the validation period from 1984 to 2017, the all-season correlation skill of the Nino3.4 index of the CNN model is much higher than those of current state-of-the-art dynamical forecast systems. The CNN model is also better at predicting the detailed zonal distribution of sea surface temperature anomalies in association with different types of ENSO. In addition, the CNN model displays good skill in predicting the IOD indices, competing with the skills of dynamical models. A heat map analysis indicates that the ocean precursors selected by the CNN model for the long-lead prediction of ENSO and IOD events are consistent with existing physical understandings. Despite many caveats, the machine learning technique shows a big potential in helping improve our understanding and prediction of tropical climate.

 

报告人介绍:

罗京佳,2018年全职回国,获江苏省“双创个人”和“双创团队”。1996年7月获南京气象学院硕士学位,2001年 3月获日本东京大学海洋物理博士学位。2001-2011年任日本海洋科学技术开发机构全球变化前沿研究中心研究员。2011-2018年任澳洲气象局研究员,现为南京信息工程大学教授、气候与应用前沿研究院院长,兼任中科院地球环境研究所国家重点实验室学术委员、CLIVAR太平洋区域委员会委员等。

长期从事年际-年代际气候变化,全球气候模式研发,气候预测理论和方法,气候信息的社会应用,全球变暖背景下的气候变化机制等研究,取得了多项国际先进成果。研发并改进了两代具有优良性能的全球海-气耦合模式,开展了国际上较高水平的气候预测方面的研究,推动了热带气候预测研究的发展。世界首次成功提前一年实时预测印度洋偶极子,2008年以来发布提前2年的厄尔尼诺实时预测信息,具有广泛的国际影响力。利用人工智能预测技术大幅提高了ENSO的季节-年际预测技巧。在气候机制研究方面有多项原创性成果,包括太平洋年代际气候变化机理,热带海盆间的相互作用等,共发表Nature、Science、PNAS等论文110余篇,Google Scholar统计的文章总引用数超过1万次。荣获多项国内外奖励,包括日本科技文化体育部青年科学家奖等。在日本期间研发的气候模式从2006年开始应用,2019年1月开始,移植到南信大,作为南信大的第一代气候预测系统,并发布逐月滚动更新的实时预测信息。这些气候预测信息已经应用于国家气候中心、国家海洋环境预报中心、华东、华中、华北、东北和华南区域气候中心的预测会商。正在研发大气和海洋资料同化方案,建立天气-次季节-季节-年际无缝隙预测系统,开发WRF区域模式高精度降尺度系统以及人工智能降尺度系统,目标是把全球模式的预测信息降尺度到目标区域,提供1天到24个月的无缝隙预测信息。

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