Abstract
Estimating the ocean subsurface thermal structure (OSTS) based on multisource sea surface data in the western Pacific Ocean is of great significance for studying ocean dynamics and El Niño phenomenon, but it is challenging to accurately estimate the OSTS from sea surface parameters in the area. This paper proposed an improved neural network model to estimate the OSTS from 0–2000 m from multisource sea surface data including sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and sea surface wind (SSW). In the model experiment, the rasterized monthly average data from 2005–2015 and 2016 were selected as the training and testing set, respectively. The results showed that the sea surface parameters selected in the paper had a positive effect on the estimation process, and the average RMSE value of the ocean subsurface temperature (OST) estimated by the proposed model was 0.55 °C. Moreover, there were pronounced seasonal variation signals in the upper layers (the upper 200 m), however, this signal gradually diminished with increasing depth. Compared with known estimation models such as the random forest (RF), the multiple linear regression (MLR), and the extreme gradient boosting (XGBoost), the proposed model outperformed these models under the data conditions of the paper. This research can provide an advanced artificial intelligence technique for estimating subsurface thermohaline structure in major sea areas.
Highlights
As an example of the application of the 2 shows th the artificial neural network (ANN) model can effectively capture the temperature in the shallow sea area [42], there distribution of the Argo-derived and ANN-estimated ocean subsurface temperature (OST) for Case 1 and Case is a huge error in the estimation effect of the model relying only on the sea surface temperature (SST) in the sea area vember
A novel ANN model was proposed to estimate the ocean subsurface thermal structure (OSTS) in this area based on the fusion of multi-sourced sea surface data (SST, sea surface height (SSH), sea surface salinity (SSS), UW, and VW)
The model performance was quantitatively analyzed by mean of the RMSE and R2
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. About 93% of the energy of global warming is stored in the oceans, where heat changes and redistribution in the subsurface and deep ocean (300–2000 m) play an important role in the global warming process [1,2,3,4]. Dynamic processes within the ocean are very complex, and many important physical oceanic phenomena and dynamic processes exist in a certain depth range below the surface [5]. The thermal structure of the ocean subsurface is crucial for the study of the mechanisms and dynamic processes of internal ocean phenomena
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