Holt-DI-EnKF-based System for Real-time Prediction of Ocean Temperature and Salinity

ZHANG Siwen; SHI Wentao; JING Lianyou; TU Nan; WEI Chengpeng

doi:10.11993/j.issn.2096-3920.2025-0149

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ZHANG Siwen, SHI Wentao, JING Lianyou, TU Nan, WEI Chengpeng. Holt-DI-EnKF-based System for Real-time Prediction of Ocean Temperature and Salinity[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0149

Citation:

ZHANG Siwen, SHI Wentao, JING Lianyou, TU Nan, WEI Chengpeng. Holt-DI-EnKF-based System for Real-time Prediction of Ocean Temperature and Salinity[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0149

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Holt-DI-EnKF-based System for Real-time Prediction of Ocean Temperature and Salinity

doi: 10.11993/j.issn.2096-3920.2025-0149

Ocean Institute, Northwestern Polytechnical University, Taicang 215400, China

Received Date: 2025-10-22
Accepted Date: 2025-12-11
Rev Recd Date: 2025-12-02

Available Online: 2026-03-16

Abstract

Abstract

To address the limitations of traditional ocean temperature and salinity prediction methods—weak dynamic update capability, distribution-dependent uncertainty quantification, and disjointed data assimilation-prediction models—this study focuses on real-time prediction of monthly average temperature and salinity time series at a single point. Aiming to develop a lightweight framework balancing accuracy, dynamic adaptability, and engineering practicality, it integrates and advances classic time series prediction, uncertainty estimation, and data assimilation methods, proposing a hybrid framework of Holt's double exponential smoothing, Bootstrap confidence intervals, and Dynamically Inflated Ensemble Kalman Filter(DI-EnKF). Specifically, the Holt model decomposes sequences and optimizes parameters, Bootstrap quantifies prediction uncertainty, and DI-EnKF assimilates real-time Argo observations to correct errors, forming a"prediction-assimilation"closed loop. Tests on global Argo data demonstrate that the framework outperforms hybrid models such as ARIMA-LSTM and ARIMA-BP in temperature prediction, with salinity prediction accuracy close to the optimal comparison model.

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References(23)

References

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