Version 1
: Received: 13 July 2024 / Approved: 15 July 2024 / Online: 16 July 2024 (10:55:32 CEST)
How to cite:
Zhou, X.; Zhu, S.; Jia, W.; Yao, H. Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model. Preprints2024, 2024071223. https://doi.org/10.20944/preprints202407.1223.v1
Zhou, X.; Zhu, S.; Jia, W.; Yao, H. Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model. Preprints 2024, 2024071223. https://doi.org/10.20944/preprints202407.1223.v1
Zhou, X.; Zhu, S.; Jia, W.; Yao, H. Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model. Preprints2024, 2024071223. https://doi.org/10.20944/preprints202407.1223.v1
APA Style
Zhou, X., Zhu, S., Jia, W., & Yao, H. (2024). Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model. Preprints. https://doi.org/10.20944/preprints202407.1223.v1
Chicago/Turabian Style
Zhou, X., Wentao Jia and Hengkai Yao. 2024 "Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model" Preprints. https://doi.org/10.20944/preprints202407.1223.v1
Abstract
Estimating the ocean’s subsurface thermohaline information from satellite measurements is essential for understanding ocean dynamics and El Niño phenomenon. This paper proposes an improved double-output Residual Neural Network (DO-ResNet) model to concurrently estimate the ocean subsurface temperature (OST) and ocean subsurface salinity (OSS) in the tropical Western Pacific using multi-source remote sensing data, including sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly(SSHA), sea surface wind (SSW), and geographical information (including longitude (LON) and latitude (LAT)). In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE) and coefficient of determination (R²) were employed to evaluate the model's performance. The results showed that the sea surface parameters selected in this study have a positive effect on the estimation process, and the average RMSE and R² values for estimating OST (OSS) by the proposed model are 0.34 °C (0.05 psu) and 0.91 (0.95), respectively. Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. Additionally, this study evaluates the model’s accuracy by comparing its estimations of OST and OSS across different depths with Argo-derived data, demonstrating the model's ability to effectively capture the most spatial features, and by comparing NRMSE across different depths and seasons, the model displays strong seasonal adaptability. In conclusion, this research introduces a novel artificial intelligence technique for estimating OST and OSS in the tropical Western Pacific Ocean.
Keywords
AI oceanography; machine learning; ocean thermohaline structure; remote sensing data; tropical Western Pacific
Subject
Environmental and Earth Sciences, Oceanography
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
