Articles | Volume 15, issue 2
https://doi.org/10.5194/os-15-401-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-15-401-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Apr 2019
Research article |
| 12 Apr 2019
| 12 Apr 2019
A simple predictive model for the eddy propagation trajectory in the northern South China Sea
Jiaxun Li
Department of Atmospheric and Oceanic Sciences, Institute of
Atmospheric Science, Fudan University, Shanghai, China
Naval Institute of Hydrographic Surveying and Charting, Tianjin, China
Guihua Wang
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Sciences, Institute of
Atmospheric Science, Fudan University, Shanghai, China
Huijie Xue
State Key Laboratory of Tropical Oceanography, South China Sea
Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China
School of Marine Sciences, University of Maine, Orono, Maine, USA
Huizan Wang
Institute of Meteorology and Oceanography, National University of
Defense Technology, Nanjing, China
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Cited
17 citations as recorded by crossref.
- A Simple Statistical Intra-Seasonal Prediction Model for Sea Surface Variables Utilizing Satellite Remote Sensing Q. Shao et al. 10.3390/rs14051162
- The Prediction of Oceanic Mesoscale Eddy Properties and Propagation Trajectories Based on Machine Learning X. Wang et al. 10.3390/w12092521
- Global Oceanic Mesoscale Eddies Trajectories Prediction With Knowledge-Fused Neural Network X. Zhang et al. 10.1109/TGRS.2024.3388040
- Refinement of Mean Dynamic Topography Over Island Areas Using Airborne Gravimetry and Satellite Altimetry Data in the Northwestern South China Sea Y. Wu et al. 10.1029/2021JB021805
- The Predictability Limit of Ocean Mesoscale Eddy Tracks in the Kuroshio Extension Region Y. Meng et al. 10.3389/fmars.2021.658125
- A Metadata-Enhanced Deep Learning Method for Sea Surface Height and Mesoscale Eddy Prediction R. Zhu et al. 10.3390/rs16081466
- Major Migration Channels of Mesoscale Eddies in the Southern Ocean Derived From Satellite Altimetry X. Guo et al. 10.1029/2022JC019096
- Ensemble forecasting greatly expands the prediction horizon for ocean mesoscale variability P. Thoppil et al. 10.1038/s43247-021-00151-5
- Deep blue artificial intelligence for knowledge discovery of the intermediate ocean G. Chen et al. 10.3389/fmars.2022.1034188
- Coastal Mean Dynamic Topography Recovery Based on Multivariate Objective Analysis by Combining Data from Synthetic Aperture Radar Altimeter Y. Wu et al. 10.3390/rs14010240
- Medium-range forecasting of oceanic eddy trajectory X. Chen et al. 10.1080/17538947.2023.2300325
- The Identification and Prediction of Mesoscale Eddy Variation via Memory in Memory With Scheduled Sampling for Sea Level Anomaly R. Nian et al. 10.3389/fmars.2021.753942
- Characterizing the capability of mesoscale eddies to carry drifters in the northwest Pacific H. Wang et al. 10.1007/s00343-019-9149-y
- A Deep Learning Model for Forecasting Sea Surface Height Anomalies and Temperatures in the South China Sea Q. Shao et al. 10.1029/2021JC017515
- A deep learning approach to predict sea surface temperature based on multiple modes S. Xu et al. 10.1016/j.ocemod.2022.102158
- Direct prediction for oceanic mesoscale eddy geospatial distribution through prior statistical deep learning H. Tang et al. 10.1016/j.eswa.2024.123737
- Observed spatiotemporal variation of three-dimensional structure and heat/salt transport of anticyclonic mesoscale eddy in Northwest Pacific J. Dai et al. 10.1007/s00343-019-9148-z
17 citations as recorded by crossref.
- A Simple Statistical Intra-Seasonal Prediction Model for Sea Surface Variables Utilizing Satellite Remote Sensing Q. Shao et al. 10.3390/rs14051162
- The Prediction of Oceanic Mesoscale Eddy Properties and Propagation Trajectories Based on Machine Learning X. Wang et al. 10.3390/w12092521
- Global Oceanic Mesoscale Eddies Trajectories Prediction With Knowledge-Fused Neural Network X. Zhang et al. 10.1109/TGRS.2024.3388040
- Refinement of Mean Dynamic Topography Over Island Areas Using Airborne Gravimetry and Satellite Altimetry Data in the Northwestern South China Sea Y. Wu et al. 10.1029/2021JB021805
- The Predictability Limit of Ocean Mesoscale Eddy Tracks in the Kuroshio Extension Region Y. Meng et al. 10.3389/fmars.2021.658125
- A Metadata-Enhanced Deep Learning Method for Sea Surface Height and Mesoscale Eddy Prediction R. Zhu et al. 10.3390/rs16081466
- Major Migration Channels of Mesoscale Eddies in the Southern Ocean Derived From Satellite Altimetry X. Guo et al. 10.1029/2022JC019096
- Ensemble forecasting greatly expands the prediction horizon for ocean mesoscale variability P. Thoppil et al. 10.1038/s43247-021-00151-5
- Deep blue artificial intelligence for knowledge discovery of the intermediate ocean G. Chen et al. 10.3389/fmars.2022.1034188
- Coastal Mean Dynamic Topography Recovery Based on Multivariate Objective Analysis by Combining Data from Synthetic Aperture Radar Altimeter Y. Wu et al. 10.3390/rs14010240
- Medium-range forecasting of oceanic eddy trajectory X. Chen et al. 10.1080/17538947.2023.2300325
- The Identification and Prediction of Mesoscale Eddy Variation via Memory in Memory With Scheduled Sampling for Sea Level Anomaly R. Nian et al. 10.3389/fmars.2021.753942
- Characterizing the capability of mesoscale eddies to carry drifters in the northwest Pacific H. Wang et al. 10.1007/s00343-019-9149-y
- A Deep Learning Model for Forecasting Sea Surface Height Anomalies and Temperatures in the South China Sea Q. Shao et al. 10.1029/2021JC017515
- A deep learning approach to predict sea surface temperature based on multiple modes S. Xu et al. 10.1016/j.ocemod.2022.102158
- Direct prediction for oceanic mesoscale eddy geospatial distribution through prior statistical deep learning H. Tang et al. 10.1016/j.eswa.2024.123737
- Observed spatiotemporal variation of three-dimensional structure and heat/salt transport of anticyclonic mesoscale eddy in Northwest Pacific J. Dai et al. 10.1007/s00343-019-9148-z
Latest update: 29 Jun 2024
Short summary
A novel predictive model is built for eddy propagation trajectory using the multiple linear regression method. This model relates various oceanic parameters to eddy propagation position changes in the northern South China Sea (NSCS). Its performance is examined in the NSCS based on five years of satellite altimeter data, and demonstrates its significant forecasting skills over a 4-week forecast window compared to the traditional persistence method.
A novel predictive model is built for eddy propagation trajectory using the multiple linear...
