18 citations as recorded by crossref.

1. 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 10.1175/JAS-D-20-0139.1
2. Machine Learning-Based Front Detection in Central Europe B. Bochenek et al. 10.3390/atmos12101312
3. Atmospheric fronts from a climatologist's perspective: A review D. Navrátilová & R. Huth 10.37040/geografie.2024.009
4. Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives B. Bochenek & Z. Ustrnul 10.3390/atmos13020180
5. Anomalous subtropical zonal winds drive decreases in southern Australian frontal rain A. Pepler & I. Rudeva 10.5194/wcd-4-175-2023
6. Application of Deep Learning to Understanding ENSO Dynamics N. Shin et al. 10.1175/AIES-D-21-0011.1
7. Using Machine Learning to Analyze Physical Causes of Climate Change: A Case Study of U.S. Midwest Extreme Precipitation F. Davenport & N. Diffenbaugh 10.1029/2021GL093787
8. Towards advancing scientific knowledge of climate change impacts on short-duration rainfall extremes H. Fowler et al. 10.1098/rsta.2019.0542
9. Scalable Feature Extraction and Tracking (SCAFET): a general framework for feature extraction from large climate data sets A. Nellikkattil et al. 10.5194/gmd-17-301-2024
10. 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. 10.1007/s00703-024-01048-6
11. Extreme Precipitation Trends and Meteorological Causes Over the Laurentian Great Lakes K. Kunkel et al. 10.3389/frwa.2022.804799
12. Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3 J. Lee et al. 10.5194/gmd-17-3919-2024
13. An automatic trough line identification method based on improved UNet Y. Cai et al. 10.1016/j.atmosres.2021.105839
14. A Benchmark to Test Generalization Capabilities of Deep Learning Methods to Classify Severe Convective Storms in a Changing Climate M. Molina et al. 10.1029/2020EA001490
15. Machine Learning‐Based Detection of Weather Fronts and Associated Extreme Precipitation in Historical and Future Climates K. Dagon et al. 10.1029/2022JD037038
16. Application of the Mask R‐CNN model to cold front identification in Eurasia Y. Qin et al. 10.1002/joc.8549
17. Automated detection and classification of synoptic-scale fronts from atmospheric data grids S. Niebler et al. 10.5194/wcd-3-113-2022
18. Can machine learning models trained using atmospheric simulation data be applied to observation data? D. Matsuoka & J. Carley 10.1017/exp.2022.3