Retrieval of Subsurface Velocities in the Southern Ocean from Satellite Observations

Determining the dynamic processes of the subsurface ocean is a critical yet formidable undertaking given the sparse measurement resources available presently. In this study, using the light gradient boosting machine algorithm (LightGBM), we report for the first time a machine learning strategy for r...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Xiang, Liang, Xu, Yongsheng, Sun, Hanwei, Zhang, Qingjun, Zhang, Liqiang, Zhang, Lin, Zhang, Xiangguang, Huang, Chao, Zhao, Dandan
Format: Report
Language:English
Published: MDPI 2023
Subjects:
subsurface velocity
light gradient boosting machine (LightGBM)
The Southern Ocean
satellite observations
long-term variability
Environmental Sciences & Ecology
Geology
Remote Sensing
Imaging Science & Photographic Technology
Environmental Sciences
Geosciences
Multidisciplinary
EMPIRICAL MODE
SURFACE
INTERIOR
CURRENTS
HEAT
Southern Ocean
Ora
Online Access:http://ir.qdio.ac.cn/handle/337002/184242
http://ir.qdio.ac.cn/handle/337002/184243
https://doi.org/10.3390/rs15245699
Description
Summary:Determining the dynamic processes of the subsurface ocean is a critical yet formidable undertaking given the sparse measurement resources available presently. In this study, using the light gradient boosting machine algorithm (LightGBM), we report for the first time a machine learning strategy for retrieving subsurface velocities at 1000 dbar depth in the Southern Ocean from information derived from satellite observations. Argo velocity measurements are used in the training and validation of the LightGBM model. The results show that reconstructed subsurface velocity agrees better with Argo velocity than reanalysis datasets. In particular, the subsurface velocity estimates have a correlation coefficient of 0.78 and an RMSE of 4.09 cm/s, which is much better than the ECCO estimates, GODAS estimates, GLORYS12V1 estimates, and Ora-S5 estimates. The LightGBM model has a higher skill in the reconstruction of subsurface velocity than the random forest and the linear regressor models. The estimated subsurface velocity exhibits a statistically significant increase at 1000 dbar since the 1990s, providing new evidence for the deep acceleration of mean circulation in the Southern Ocean. This study demonstrates the great potential and advantages of statistical methods for subsurface velocity modeling and oceanic dynamical information retrieval.

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