Skillful Prediction of Indian Monsoon Intraseasonal Precipitation Using Central Indian Ocean Mode and Machine Learning

Abstract Monsoonal precipitation is dominated by intraseasonal variabilities, whose skillful prediction lead time is currently less than 5 days and remains a grand challenge. Here we show that an intrinsic variability in the Indian Ocean, the Central Indian Ocean (CIO) mode, when combined with a machine learning (ML) algorithm, can produce skillful predictions of intraseasonal precipitation over the monsoon region with a lead time of over 15 days, which is close to the theoretical predictability limit. This remarkable skill improvement stems from the fact that the CIO mode is dynamically related to the intraseasonal monsoon rainfall, while the data‐driven ML algorithm suppresses unwanted high‐frequency noise. Using the CIO mode and the ML algorithm, the forecast system hybridizes physical fundamentals and versatility of data‐driven algorithms. The identification of CIO mode and the verification of its significant contribution to intraseasonal predictions advance our understanding of the coupled monsoon system and also underscores the great potential of ML techniques in weather forecasts and climate predictions.

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Publication Details

Journal
Geophysical Research Letters
Published
2024-12-11
DOI
https://doi.org/10.1029/2024gl112308
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.00

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article

Skillful Prediction of Indian Monsoon Intraseasonal Precipitation Using Central Indian Ocean Mode and Machine Learning

Lei Zhou, Bingqi Yan, Raghu Murtugudde, Yanwei Yu et al.
Geophysical Research Letters
Climate variability and models
article

Skillful Prediction of Indian Monsoon Intraseasonal Precipitation Using Central Indian Ocean Mode and Machine Learning

Lei Zhou, Bingqi Yan, Raghu Murtugudde, Yanwei Yu, Dake Chen, Junyu Dong, Jianhuang Qin, Youmin Tang, Xiaofeng Li, Xiaojing Li, Xingyu Zhao, Wei Tan
article en

Abstract

Abstract Monsoonal precipitation is dominated by intraseasonal variabilities, whose skillful prediction lead time is currently less than 5 days and remains a grand challenge. Here we show that an intrinsic variability in the Indian Ocean, the Central Indian Ocean (CIO) mode, when combined with a machine learning (ML) algorithm, can produce skillful predictions of intraseasonal precipitation over the monsoon region with a lead time of over 15 days, which is close to the theoretical predictability limit. This remarkable skill improvement stems from the fact that the CIO mode is dynamically related to the intraseasonal monsoon rainfall, while the data‐driven ML algorithm suppresses unwanted high‐frequency noise. Using the CIO mode and the ML algorithm, the forecast system hybridizes physical fundamentals and versatility of data‐driven algorithms. The identification of CIO mode and the verification of its significant contribution to intraseasonal predictions advance our understanding of the coupled monsoon system and also underscores the great potential of ML techniques in weather forecasts and climate predictions.

Geophysical Research LettersVol. 51(24)
University of Northern British Columbia (CA), Indian Institute of Technology Bombay (IN), Hohai University (CN), Shanghai Jiao Tong University (CN), Chinese Academy of Sciences (CN), Ministry of Natural Resources (CN), Institute of Oceanology (CN), Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (CN), Second Institute of Oceanography (CN), Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (CN), Ocean University of China (CN), University of Maryland, College Park (US), Shandong University of Science and Technology (CN)
National Natural Science Foundation of China, Directorate for Engineering, National Key Research and Development Program of China
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Climate variability and models
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