A machine learning based prediction system for the Indian Ocean Dipole.
A machine learning based prediction system for the Indian Ocean Dipole.
复制标题
基于机器学习的印度洋偶极子预测系统。
DOI：
10.1038/s41598-019-57162-8
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发表时间：
2020
影响因子：
4.6
通讯作者：
Ratnam JV
中科院分区：
文献类型：
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作者：
Ratnam JV
印度洋偶极子（Indian Ocean Dipole，IOD）是印度洋海表温度异常的一种气候变率模式，一极在苏门答腊岛附近，另一极在东非附近。IOD事件在5月至6月的某个时候开始，在9月至10月达到高峰，并在11月结束。通过大气遥相关，它影响了世界许多地区的气候，特别是东非，澳大利亚，印度，日本和欧洲。由于其巨大的影响，以前的研究已经解决了IOD的可预测性使用最先进的耦合气候模式。在这里，我们第一次使用机器学习技术，特别是人工神经网络（ANN）来预测IOD。IOD预测是根据2月至4月的条件生成的5月至11月。人工神经网络的属性来自海表面温度，850 hPa和200 hPa的高度异常，使用1949-2018年期间的相关分析。使用500个样本，用刀切法替换产生的人工神经网络预测的合奏。IOD预测的集合平均值表明，基于机器学习的人工神经网络模型能够预测IOD指数以及提前与优秀的技能。预测技能是远远上级从持久性预测，人们会猜测从观察到的数据中获得的技能。人工神经网络模型的表现也远远好于北美多模式Ensemble（NMME）的模型，具有较高的相关系数和较低的均方根误差（RMSE）的所有目标月份的5月至11月。
The Indian Ocean Dipole (IOD) is a mode of climate variability observed in the Indian Ocean sea surface temperature anomalies with one pole off Sumatra and the other pole near East Africa. An IOD event starts sometime in May-June, peaks in September-October and ends in November. Through atmospheric teleconnections, it affects the climate of many parts of the world, especially that of East Africa, Australia, India, Japan, and Europe. Owing to its large impacts, previous studies have addressed the predictability of the IOD using state of the art coupled climate models. Here, for the first-time, we predict the IOD using machine learning techniques, in particular artificial neural networks (ANNs). The IOD forecasts are generated for May to November from February-April conditions. The attributes for the ANNs are derived from sea surface temperature, 850 hPa and 200 hPa geopotential height anomalies, using a correlation analysis for the period 1949–2018. An ensemble of ANN forecasts is generated using 500 samples with replacement using jackknife approach. The ensemble mean of the IOD forecasts indicates the machine learning based ANN models to be capable of forecasting the IOD index well in advance with excellent skills. The forecast skills are much superior to the skills obtained from the persistence forecasts that one would guess from the observed data. The ANN models also perform far better than the models of the North American Multi-Model Ensemble (NMME) with higher correlation coefficients and lower root mean square errors (RMSE) for all the target months of May-November.