10 citations as recorded by crossref.

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2. Relative humidity over ice as a key variable for Northern Hemisphere midlatitude tropopause inversion layers D. Köhler et al. https://doi.org/10.5194/acp-24-10055-2024
3. Anthropogenic warming induced intensification of summer monsoon frontal precipitation over East Asia S. Moon et al. https://doi.org/10.1126/sciadv.adh4195
4. An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection X. Ding et al. https://doi.org/10.3390/app15147855
5. AF‐UNet: Intelligent Recognition Model for Atmospheric Fronts Based on Adaptive Fuser X. Ding et al. https://doi.org/10.1155/adme/2118816
6. Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data T. Radke et al. https://doi.org/10.5194/gmd-18-1017-2025
7. A climatology of trapped lee waves over Britain and Ireland obtained using deep learning on high‐resolution model output J. Coney et al. https://doi.org/10.1002/qj.5037
8. A deep neural network model with physics-guided term for automatic identification of atmospheric fronts X. Ding et al. https://doi.org/10.1016/j.neucom.2025.131835
9. Trends, Patterns, and Driving Forces of Heat Waves in the Center and Southeast of Iran A. Ansari et al. https://doi.org/10.1007/s00024-024-03623-9
10. Detecting atmospheric fronts based on diabatic processes using the dynamic state index (DSI) L. Mack et al. https://doi.org/10.1007/s00703-026-01141-y