Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2021GL095663 |
An observationally trained Markov Model for MJO propagation | |
Samson Hagos; L. Ruby Leung; Chidong Zhang; Karthik Balaguru | |
2021-12-20 | |
发表期刊 | Geophysical Research Letters |
出版年 | 2021 |
英文摘要 | A Markovian stochastic model is developed for studying the propagation of the Madden-Julian Oscillation (MJO). This model represents the daily changes in real time multivariate MJO (RMM) indices as random functions of their current state and background conditions. The probability distribution function of the RMM changes is obtained using a machine learning algorithm trained to maximize MJO forecast skills using observed daily indices of RMM and different modes of variability. Skillful forecasts are obtained for lead times between 8 and 27 days. Large ensemble simulations by the stochastic model show that with monsoonal changes in the background state, MJO propagation across the Maritime Continent (MC) is most likely to be disrupted in boreal spring and summer when MJO events propagate from favorable conditions over the Indian Ocean to unfavorable ones over the MC, and predictability is higher during spring and summer when MJO activity is away from the MC region. |
领域 | 气候变化 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/344178 |
专题 | 气候变化 |
推荐引用方式 GB/T 7714 | Samson Hagos,L. Ruby Leung,Chidong Zhang,et al. An observationally trained Markov Model for MJO propagation[J]. Geophysical Research Letters,2021. |
APA | Samson Hagos,L. Ruby Leung,Chidong Zhang,&Karthik Balaguru.(2021).An observationally trained Markov Model for MJO propagation.Geophysical Research Letters. |
MLA | Samson Hagos,et al."An observationally trained Markov Model for MJO propagation".Geophysical Research Letters (2021). |
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