## 3 Results
### Training of the NN Models  
During the training, various model architectures were adjusted and evaluated to minimize the generalization error (MAE) in order to obtain the model that fits the training data best. The hyperparameters were tuned to find a trade-off between training efficiency and convergence of the validation and training losses. The objective was to maintain the resemblance between the learning curves throughout the training to prevent overfitting and overgeneralization. Each model was trained with a maximum number of 1000 epochs, and both the training and validation losses were evaluated after each epoch, with a constant learning rate of 0.001. This rate turned out to be large enough to obtain a fast improvement of the learning curve at the beginning of the training (short burn-in phase) and small enough to lead to smooth convergence without bouncing around the optimum towards the end. In both NN (thin STT and full-range STT), the batch size, the number of hidden layers, and the patience interval of Early Stopping regularization were tuned, where the number of neurons per layer was kept constant to 64. An increase of the batch size up to a value of 1024 did not significantly influence the converging trend, but it considerably decreased the amplitude of the fluctuations in the validation loss and speeded up the training time. A higher number of hidden layers usually allows a network a fast build-up of sufficient complexity. In this case, more hidden layers (>3) led to a notable reduction of the training loss, whereas the validation loss was only slightly improving. This implied that, although the obtained model complexity apparently represented the reduced training set, it did not generalize well on the validation set. The validation loss fluctuated, but it showed a decreasing trend until it stagnated, when the model started to overfit the training data. Early Stopping with a patience interval eventually prevented the model from  
Figure 5: Convex hulls around the point clouds in two-dimensional sub-feature space of Arctic data (black solid line around blue markers), and Antarctic data (red dashed line around yellow markers) (Feature pairs: (**a**) T\({}_{B}\)–T\({}_{S}\), (**b**) T\({}_{B}\)–SIC, (**c**) T\({}_{B}\)–Fb, (**d**) Fb–SIC, (**e**) Fb–T\({}_{S}\), and (**f**) SIC–T\({}_{S}\)). The application of the full-range model to Antarctic data is considered to be reliable, since most of its values are located within the convex hull and at denser areas of Arctic training data.  
this overfitting. The fit was stopped after the validation loss did not show improvement anymore during the corresponding patience interval and the best fit was called-back and saved.  
Table 2 provides a summary of the final architecture and the optimal training hyperparameters of the NNs and the SIT-range-specific MAE obtained from the prediction for the thin and full-range SIT models. The learning curves for the thin and full-range model are presented in Figures 6a and 8a, respectively. Regarding both of the models, training and validation loss converged well, and no overfitting of the training data could be observed. Validation and training curves of the thin SIT model both match well throughout the training, whereas a small mismatch remained between those of the full-range SIT model towards the end of the training. The evaluation of the test set resulted in a final MAE of 0.065 m for the thin and 0.237 m for the full-range model, respectively.