Rapid environmental assessment in the South China Sea: Improved inversion of sound speed profile using remote sensing data
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Abstract: Complex perturbations in the profile and the sparsity of samples often limit the validity of rapid environmental assessment (REA) in the South China Sea (SCS). In this paper, the remote sensing data were used to estimate sound speed profile (SSP) with the self-organizing map (SOM) method in the SCS. First, the consistency of the empirical orthogonal functions was examined by using k-means clustering. The clustering results indicated that SSPs in the SCS have a similar perturbation nature, which means the inverted grid could be expanded to the entire SCS to deal with the problem of sparsity of the samples without statistical improbability. Second, a machine learning method was proposed that took advantage of the topological structure of SOM to significantly improve their accuracy. Validation revealed promising results, with a mean reconstruction error of 1.26 m/s, which is 1.16 m/s smaller than the traditional single empirical orthogonal function regression (sEOF-r) method. By violating the constraints of linear inversion, the topological structure of the SOM method showed a smaller error and better robustness in the SSP estimation. The improvements to enhance the accuracy and robustness of REA in the SCS were offered. These results suggested a potential utilization of REA in the SCS based on satellite data and provided a new approach for SSP estimation derived from sea surface data.
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Figure 2. Distribution of clustered samples when five-order empirical orthogonal function coefficients were clustered into four classes. The class centers are marked by asterisks.
Figure 3. First five orders of the empirical orthogonal function. From left to right are, in order, the first to the fifth order.
Figure 4. Schematics of the training of the map (blue arrows) and reconstruction of the sound speed profile (red arrows) using self-organizing map-based inversion. Lat: latitude; Lon: longitude; SSTA: sea surface temperature anomaly; SLA: sea level anomaly.
Figure 5. Errors of reconstruction for different sample numbers. EOF: empirical orthogonal function; SOM: self-organizing map.
Figure 6. Reconstruction errors at different depths. EOF: empirical orthogonal function; SOM: self-organizing map.
Figure 7. An example of sound speed profile reconstruction. The sample used had the largest error in self-organizing map (SOM) reconstruction. EOF: empirical orthogonal function.
Figure 8. Results of inversion of the normalized projection coefficients of the first five orders. The straight line indicates perfect inversion. From left to right are, in order, the first to the fifth order. EOF: empirical orthogonal function; SOM: self-organizing map.
Table 1. Properties of reconstruction of different orders of the empirical orthogonal function (EOF)
EOF1 EOF2 EOF3 EOF4 EOF5 Variance contribution/% 74.0 13.7 4.7 2.9 1.4 Cumulative variance contribution/% 74.0 87.7 92.4 95.3 96.7 Reconstruction error/(m·s−1) 2.00 1.38 1.09 0.85 0.72 
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