## 3 Method
### Model Architecture for Temperature Anomaly Reconstruction  
In the field of deep learning, this study introduces a fully connected transformer network (FCT), an advanced deep learning architecture that seamlessly integrates fully connected layers with the transformer model. This architecture is specifically designed to address the complex task of reconstructing ocean subsurface temperature anomalies (STAs), particularly in regions affected by typhoons. The FCT is intended to capture the intricate relationships between surface wind patterns and subsurface thermal structures, making it a powerful tool in oceanographic research. As illustrated in Figure 1, the model's input consists of wind vector data, where the u component (uvel) and v component (vvel) represent the zonal and meridional components of horizontal wind speed, respectively. These wind vectors are crucial for understanding how surface winds influence subsurface ocean temperatures, especially under extreme weather conditions such as typhoons. The wind vector data are first subjected to feature extraction through a sophisticated encoder module within the FCT. Each component of the wind vector--uvel and vvel--is processed independently by two distinct encoders. These encoders are designed to extract key features from the input data, capturing the essential dynamics that drive the interaction between atmospheric forces and oceanic responses. The use of independent encoders allows the model to separately analyze the influence of each wind component, leading to a more nuanced and accurate mapping to the ocean temperature profile. Following the initial feature extraction, the model proceeds to enhance and integrate these features through a series of critical processing steps, which are crucial for developing a comprehensive understanding of multi-scale oceanographic data. Firstly, normalization layers are employed to standardize the output features from the encoders. This standardization is essential for eliminating numerical scale differences inherent in the data, which can arise due to the varying magnitudes of the wind components and other factors. By normalizing the features, the model not only improves training stability but also enhances the effectiveness of feature representation, allowing for more precise learning of the underlying physical processes. The model then utilizes a multi-head attention mechanism (MHA), a key innovation in the transformer architecture. The MHA allows the model to compute multiple attention distributions in parallel, enabling it to focus on different aspects of the input data simultaneously. This mechanism is particularly beneficial for capturing the complex and often non-linear relationships between wind vector data and the ocean temperature profiles. The ability to consider multiple perspectives in parallel allows the FCT to effectively model the interactions between surface winds and subsurface thermal structures, even in the presence of highly dynamic and turbulent conditions associated with typhoons. Subsequent to the attention mechanism, the features are further processed by a feed-forward network (FFN), which applies nonlinear transformations to the attention-processed features. The FFN consists of multiple fully connected layers, which are designed to explore the complex patterns within the data, capturing higher-order interactions that are critical for accurate temperature reconstruction. By passing the features through these nonlinear transformations, the model enhances its capacity to understand the intricate thermal dynamics that occur beneath the ocean surface, especially in response to atmospheric disturbances like typhoons. Ultimately, the model integrates these multi-scale features, which include sea level anomaly (SLA) and sea surface temperature (SST), through a fully connected layer (FC) to generate the final reconstructed temperature profile output.