Deep learning approach for forecasting sea surface temperature response to tropical cyclones in the Western North Pacific

Zhang, Han; Jing, Mengyuan; Zhang, Haoyu; Li, Longjie; Zheng, Yunxia; Tang, Jie; Tian, Di; Zhu, Youhua

doi:10.1016/j.dsr.2023.104042

Cited by 7 publications

(3 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, we applied the random forest method to build models to predict the sea surface temperature (SST) responses to tropical cyclones (TCs) in the northwest Pacific using 12 factors related to TC characteristics ( V max , V trans , V dir , R 30, Lon, Lat) and pre‐storm ocean properties (

\overline{\text{MLD}}

\overline{\text{SSH}}

\overline{\text{SST}}

\overline{T75}

\overline{Ug\text{Res}}

\overline{Vg\text{Res}}

). TC characteristics have not been included as predictors in previous studies (e.g., Shao et al., 2021; Zhang et al., 2023). In this study, we have demonstrated that the performance of the model could be effectively improved by using both the TC features and pre‐storm upper‐ocean variables as predictors.…”

Section: Discussionmentioning

confidence: 99%

“…Zhang et al. (2023) developed a multi‐variate LSTM model using the same predictors as those used in Jiang et al. (2018) to predict TC‐induced SST cooling in the northwest Pacific Ocean (NWP).…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Shao et al (2021) introduced a hybrid model that combines a LSTM model with a statistical method to forecast SST and SSH anomalies after TC passage in the South China Sea (SCS). Zhang et al (2023) developed a multi-variate LSTM model using the same predictors as those used in Jiang et al (2018) to predict TC-induced SST cooling in the northwest Pacific Ocean (NWP).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Predicting Tropical Cyclone‐Induced Sea Surface Temperature Responses Using Machine Learning

Cui,

Tang,

Mei

et al. 2023

Geophysical Research Letters

View full text Add to dashboard Cite

This study proposes to construct a model using random forest method, an efficient machine learning‐based method, to predict the spatial structure and temporal evolution of the sea surface temperature (SST) cooling induced by northwest Pacific tropical cyclones (TCs), a process of the so‐called wind pump. The predictors in use include 12 predictors related to TC characteristics and pre‐storm ocean conditions. The model is shown to skillfully predict the spatiotemporal evolutions of the cold wake generated by TCs of different intensity groups, and capture the cross‐case variance in the observed SST response. Another model is further built based on the same method to assess the relative importance of the 12 predictors in determining the magnitude of the maximum cooling. Computations of feature scores of those predictors show that TC intensity, translation speed and size, and pre‐storm mixed layer depth and SST dominate, depending on the area where the cooling is considered.

show abstract

\overline{\text{MLD}}

\overline{\text{SSH}}

\overline{\text{SST}}

\overline{T75}

\overline{Ug\text{Res}}

\overline{Vg\text{Res}}

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Predicting Tropical Cyclone‐Induced Sea Surface Temperature Responses Using Machine Learning

Cui,

Tang,

Mei

et al. 2023

Geophysical Research Letters

View full text Add to dashboard Cite

show abstract

Deep learning for sea surface temperature applications: A comprehensive bibliometric analysis and methodological approach

Boufeniza,

Jingjia,

Abdela

et al. 2024

Geography and Environment

View full text Add to dashboard Cite

This study explored the potential application of deep learning techniques in sea surface temperature (SST) investigations using a mixed method, bibliometric analysis and methodological approach. CiteSpace software was utilized for a bibliometric study on 137 academic publications from 2018 to 2023. Various databases were employed for methodological analysis, which involved examining publications based on models, methodologies, applications and research areas. The data were manually organized in a relational framework of an SQL database. The analysis underscored China's prominence as a leader in the extensive research devoted to this field. The United States of America and the United Kingdom played pivotal roles in providing the essential data that served as the foundation for these studies. Moreover, the long short‐term memory (LSTM) algorithm was the predominant computational deep learning algorithm extensively used in this specific context. The analysis highlighted significant knowledge gaps in areas such as SST forecasting, modelling, satellite remote sensing, extreme events and data reconstruction. Future scientists need to show more interest in these and related subjects, while Chinese and American scientists should prioritize paper quality over quantity. Additionally, fostering stronger collaborations between universities and institutions is vital for further advancements. Ultimately, this study offers valuable insights into hotspot research areas and development processes, establishing the foundation for research and suggesting possible avenues for future development. The results of this evaluation serve as an essential guide for researchers and modellers involved in prediction initiatives using deep learning.

show abstract

Estimation of the barrier layer thickness in the Indian Ocean based on hybrid neural network model

Zhao,

Qi,

Zhu

et al. 2023

Deep Sea Research Part I: Oceanographic Research Papers

View full text Add to dashboard Cite

Deep learning approach for forecasting sea surface temperature response to tropical cyclones in the Western North Pacific

Cited by 7 publications

References 62 publications

Predicting Tropical Cyclone‐Induced Sea Surface Temperature Responses Using Machine Learning

Predicting Tropical Cyclone‐Induced Sea Surface Temperature Responses Using Machine Learning

Deep learning for sea surface temperature applications: A comprehensive bibliometric analysis and methodological approach

Estimation of the barrier layer thickness in the Indian Ocean based on hybrid neural network model

Contact Info

Product

Resources

About