Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data Source and Preprocessing
3. Methods
3.1. The DO-ResNet Model
3.2. Experimental Setup
4. Results and Discussion
4.1. Identification of Input Variable
4.2. Accuracy Comparison between the DO-ResNet Model and Other Models
4.3. Vertical Performance Evaluation of the DO-ResNet Model
4.4. Seasonal Performance of the DO-ResNet Model
4.5. Correlation Analysis between the ST (SS) and Surface Parameters
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Index | Contents | |||
---|---|---|---|---|
Study Area | Tropical Western Pacific (25° S–25° N, 125° E–150° W) | |||
Data | SSS | 2010–2020 | SMOS | Input |
SST | 2010–2020 | NOAA | ||
SSHA | 2010–2020 | AVISO | ||
SSW | 2010–2020 | CCMP | ||
ST | 2010–2020 | Argo | Label | |
SS | 2010–2020 | Argo | ||
Resolution | monthly |
Estimation Models | Parameter Values |
---|---|
DO-ResNet | , stride = 1; |
; | |
loss function: mse; optimizer: radam; learning rate: 0.02; | |
reducelronplateau: mode = ‘min’, factor = 0.1, patience = 10; | |
batch size: 2048; activation function: relu; batchnorm2d; | |
validation frequency: per epoch earlystopping: patience = 7, verbose = False, delta = 0 |
Experiments | Training Methods |
---|---|
Case 1 (3 parameters) | ST (SS) = Ensemble (SST, SSS, SSHA) |
Case 2 (5 parameters) | ST (SS) = Ensemble (SST, SSS, SSHA, USSW, VSSW) |
Case 3 (7 parameters) | ST (SS) = Ensemble (SST, SSS, SSHA, USSW, VSSW, LON, LAT) |
Models | Parameter Values |
---|---|
XGBoost | eta = 0.02, min_child_weight = 2.0, max_depth = 5, subsample = 0.8 |
RF | min_samples_split = 100, min_samples_leaf = 20, max_depth = 8, random_state = 10 |
ANN | number of neural network layers = 3, learning rate = 0.002, number of neurons per layer = 30, loss function = MSE |
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Zhou, X.; Zhu, S.; Jia, W.; Yao, H. Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model. Atmosphere 2024, 15, 1043. https://doi.org/10.3390/atmos15091043
Zhou X, Zhu S, Jia W, Yao H. Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model. Atmosphere. 2024; 15(9):1043. https://doi.org/10.3390/atmos15091043
Chicago/Turabian StyleZhou, Xianmei, Shanliang Zhu, Wentao Jia, and Hengkai Yao. 2024. "Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model" Atmosphere 15, no. 9: 1043. https://doi.org/10.3390/atmos15091043