# An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection
## 3 Results and Discussion
### Experiment on the Effectiveness of Adapter  
To verify whether incorporating adapter modules into current machine learning methods for automatic atmospheric front identification is conducive to network training and improves the accuracy of front identification, this section conducts experiments on the effectiveness of adapters. The experimental approach involves comparing four types of networks--CNN, FCN, Unet, and MRCNN--with and without adapter modules in terms of training loss, training epochs, and identification results across multiple evaluation metrics, namely _ACC_ (accuracy), \(P\) (precision), \(R\) (recall), and \(F\)1. Figure 4 presents a comparison of training loss and epochs for the four types of networks with and without adapters under identical experimental settings. The figure reveals that for all four networks, the training loss decreases when using adapters, and the number of epochs required for the loss to stabilize is significantly reduced by an average of approximately 10 epochs. This indicates that adding adapters to current machine learning methods for front identification facilitates network training, thereby demonstrating the effectiveness of employing adapters in the proposed model of this study. Under identical experimental setups, the final evaluation results of the eight networks (including the four baseline networks and their adapter-augmented variants) on the test set are presented in Table 2. The table demonstrates that all evaluation metrics for the four baseline networks improved by over 3% after incorporating the adapter modules, indicating that adding adapters to current machine learning networks for automatic front identification is effective. The improvement in network performance by the adapter is consistent with the studies in References [17; 20]. This improvement can be attributed to two main reasons:  
Figure 4: Training loss and epoch results of four machine learning network types with/without adapter network. The application of adapter modules in deep learning and their optimization effects on network training have been extensively studied. The literature [21; 22] demonstrates the role of adapters in resolving feature conflicts and facilitating feature fusion. The weight layers within the adapter module filter out feature conflicts among multiple meteorological elements while suppressing strong non-frontal features and enhancing weak frontal features. This enables the network to efficiently and accurately learn the multi-element characteristics of fronts. Such feature conflicts primarily arise under specific weather conditions or geographical locations, where certain meteorological elements may exhibit abnormally strong non-frontal features (while others remain normally weak) or exceptionally weak frontal features (while others appear strongly frontal). 2. The fusion layer in the adapter integrates features from multiple meteorological elements. By leveraging the systematic nature of fronts, it helps the network better approximate the complex patterns between frontal systems and multi-element composite features. This not only facilitates network training but also enhances the accuracy of front identification. Additionally, we conducted a visualization of the ablation experiment results with and without the adapter on the MRCNN network, as shown in Figure 5. Figure 5 displays the visualization of frontal identification by the MRCNN network, while Figure 5 shows incorporating the adapter. A comparison reveals that Figure 5 identifies an additional frontal region within the purple box compared to Figure 5; however, this region does not actually exist. This discrepancy arises because the MRCNN network performs poorly in distinguishing strong non-frontal meteorological features. For instance, the constant temperature zone at the land-sea boundary within the purple box is mistakenly identified as a frontal feature due to its strong meteorological characteristics. In contrast, with the adapter, the corresponding region in Figure 5 is correctly recognized as a non-frontal area.