11 citations as recorded by crossref.

1. Amplification of annual and diurnal cycles of alpine lightning T. Simon et al. https://doi.org/10.1007/s00382-023-06786-8
2. Robust weather-adaptive post-processing using model output statistics random forests T. Muschinski et al. https://doi.org/10.5194/npg-30-503-2023
3. Research on Lightning Prediction Based on GCN-LSTM Model Integrating Spatiotemporal Features W. Zhou et al. https://doi.org/10.3390/atmos16040447
4. Random forests for homogeneous and non-homogeneous Poisson processes with excess zeros W. Mathlouthi et al. https://doi.org/10.1177/0962280219888741
5. Cholesky-based multivariate Gaussian regression T. Muschinski et al. https://doi.org/10.1016/j.ecosta.2022.03.001
6. Improving Probabilistic Lightning Forecasts Through Ensemble Postprocessing with Mesoscale Information H. Li et al. https://doi.org/10.3390/atmos17040371
7. Revisiting the link between extreme sea levels and climate variability using a spline-based non-stationary extreme value analysis J. Rohmer et al. https://doi.org/10.1016/j.wace.2021.100352
8. Machine Learning-Based Identification of Key Predictors for Lightning Events in the Third Pole Region H. Jadhav et al. https://doi.org/10.3390/ijgi14080319
9. Lightning climatology for the eastern Alpine region on the kilometer scale with daily resolution T. Simon & G. Mayr https://doi.org/10.1007/s00502-022-01032-1
10. Deep Learning Prediction of Thunderstorm Severity Using Remote Sensing Weather Data Y. Essa et al. https://doi.org/10.1109/JSTARS.2022.3172785
11. Pre-estimation of Distance-Based Lightning Using Effective Meteorological Parameters Ş. Yücelbaş et al. https://doi.org/10.1007/s13369-020-05257-0