26 citations as recorded by crossref.

1. Machine Learning-Based Front Detection in Central Europe B. Bochenek et al.
2. AF‐UNet: Intelligent Recognition Model for Atmospheric Fronts Based on Adaptive Fuser X. Ding et al.
3. Using Machine Learning to Analyze Physical Causes of Climate Change: A Case Study of U.S. Midwest Extreme Precipitation F. Davenport & N. Diffenbaugh
4. Identification and forecasting of low-level wind shear based on long range Doppler wind lidar A. Chen et al.
5. Towards advancing scientific knowledge of climate change impacts on short-duration rainfall extremes H. Fowler et al.
6. Statistical-dynamical analog ensemble system for real time quantitative precipitation forecasts (QPFs) at local scale in the north-west Himalaya (NWH), India D. Singh et al.
7. Extreme Precipitation Trends and Meteorological Causes Over the Laurentian Great Lakes K. Kunkel et al.
8. Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3 J. Lee et al.
9. A deep neural network model with physics-guided term for automatic identification of atmospheric fronts X. Ding et al.
10. APPLICABILITY OF DETECTING SURFACE WEATHER FRONTS USING CONVOLUTIONAL NEURAL NETWORK BASED DEEP LERANING Y. MAO & T. YAMADA
11. Weather type reconstruction using machine learning approaches L. Pfister et al.
12. Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data T. Radke et al.
13. Automated detection and classification of synoptic-scale fronts from atmospheric data grids S. Niebler et al.
14. Can machine learning models trained using atmospheric simulation data be applied to observation data? D. Matsuoka & J. Carley
15. Bridging the Climate Gap: Multimodel Framework With Explainable Decision-Making for IOD and ENSO Forecasting H. Tiwari et al.
16. Toward a Systematic Evaluation of Warm Conveyor Belts in Numerical Weather Prediction and Climate Models. Part I: Predictor Selection and Logistic Regression Model J. Quinting & C. Grams
17. Atmospheric fronts from a climatologist's perspective: A review D. Navrátilová & R. Huth
18. Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives B. Bochenek & Z. Ustrnul
19. Anomalous subtropical zonal winds drive decreases in southern Australian frontal rain A. Pepler & I. Rudeva
20. Application of Deep Learning to Understanding ENSO Dynamics N. Shin et al.
21. Scalable Feature Extraction and Tracking (SCAFET): a general framework for feature extraction from large climate data sets A. Nellikkattil et al.
22. An automatic trough line identification method based on improved UNet Y. Cai et al.
23. Cold Front Identification Using the DETR Model with Satellite Cloud Imagery Y. Qin et al.
24. A Benchmark to Test Generalization Capabilities of Deep Learning Methods to Classify Severe Convective Storms in a Changing Climate M. Molina et al.
25. Machine Learning‐Based Detection of Weather Fronts and Associated Extreme Precipitation in Historical and Future Climates K. Dagon et al.
26. Application of the Mask R‐CNN model to cold front identification in Eurasia Y. Qin et al.