# An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection
); [EMAIL_ADDRESS] (Y.X.) 2 The College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410005, China; [EMAIL_ADDRESS] (L.Z. ); zhangyunpex  
3 High Impact Weather Key Laboratory of CMA, Changsha 410005, China  
4 Sichuan Meteorological Disaster Defense Technology Center, Chengdu 610072, China  
5 Heavy Rain and Drought-Fleod Disaster in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072, China  
6 Institute of Meteorological Sciences of Hunan Province, Changsha 410118, China; [EMAIL_ADDRESS]  
[PERSON]  
6 Institute of Meteorological Sciences of Hunan Province, Changsha 410118, China; [EMAIL_ADDRESS] (Y.Z.) 2 The College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410005, China; zhangyunpex  
6 Institute of Meteorological Sciences of Hunan Province, Changsha 410118, China; [EMAIL_ADDRESS] (Correspondence: [EMAIL_ADDRESS] (Correspondence: pengguan@hundt. Currently, automated methods for front identification mainly fall into two categories: numerical front analysis (NFA) and machine learning (ML) methods. NFA primarily involves selecting a parameter input into a manually defined function and using a threshold to diagnose whether a front exists. Such methods include the gradient method (G\(\tau\)), thermal front parameter (TFP) [4; 5; 6], wind change (WC) [7], F-diagnosis [8], and the process of growing frontiersmen (PGF) [9], among others. However, these methods face limitations such as sensitivity to grid resolution, noise from second derivatives, and challenges in threshold determination [10; 11]. The wind-shift method has rarely detected warm fronts [12]. Recent advancements in NFA, such as the dynamic state index (DSI) proposed by [13], attempt to address these issues by incorporating diabatic processes, but they still struggle with feature conflicts and systematic biases. To overcome these limitations, machine learning-based approaches have gained prominence. Early efforts utilized convolutional neural networks (CNNs) for localized frontal classification [14; 15], but these methods suffered from fragmented results due to subregion division. Fully convolutional networks (FCNs) improved upon CNNs by enabling end-to-end processing, yet they failed to capture multi-scale features [16]. The introduction of U-Net architectures marked a significant advancement by leveraging skip connections for deeper feature fusion [17], but challenges remained in resolving feature conflicts among meteorological inputs. Recent studies, such as [18], have explored hybrid models combining U-Net with attention mechanisms to enhance feature compatibility, while [11] proposed the UNET3+ model to improve multi-scale feature aggregation. Despite these efforts, existing methods still face two critical limitations:  
* Input Feature Compatibility: direct input of raw meteorological data without adaptive feature fusion leads to suboptimal model performance due to feature conflicts. * Output Granularity: semantic segmentation networks only provide frontal category information without distinguishing individual frontal systems [16; 19]. To address these challenges, this study proposes AD-MRCNN, a novel framework that integrates two key innovations:
* Intelligent Adapter Module: This module performs adaptive feature fusion by dynamically weighting and combining multi-source meteorological inputs (e.g., temperature, wind fields, humidity). It resolves feature conflicts through a normalization-weighting-fusion pipeline, enhancing the synergy of input features while suppressing noise. The adapter's design is inspired by recent work in feature fusion [17; 20], which demonstrated the effectiveness of adaptive weighting in meteorological applications. * Enhanced Instance Segmentation Network: Based on Mask R-CNN, this network extends traditional semantic segmentation by simultaneously classifying frontal types (cold/warm/stationary/occluded), localizing spatial boundaries, and identifying distinct frontal systems.