17 citations as recorded by crossref.

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4. Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator H. Sørup et al. 10.5194/hess-20-1387-2016
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6. A space‐time Neyman‐Scott model of rainfall: Empirical analysis of extremes P. Cowpertwait et al. 10.1029/2001WR000709
7. The effect of temporal variations in rainfall on scale dependency in runoff coefficients J. Wainwright & A. Parsons 10.1029/2000WR000188
8. Downscaling transient climate change using a Neyman–Scott Rectangular Pulses stochastic rainfall model A. Burton et al. 10.1016/j.jhydrol.2009.10.031
9. Continuous rainfall simulation: 1. A regionalized subdaily disaggregation approach S. Westra et al. 10.1029/2011WR010489
10. Pyraingen: A python package for constrained continuous rainfall generation C. Dykman et al. 10.1016/j.envsoft.2024.105984
11. A Criterion for Extending Continuous-Mixture Identifiability Results M. Powers & J. Xu 10.1007/s44199-025-00124-z
12. Relative Importance of the Different Rainfall Statistics in the Calibration of Stochastic Rainfall Generation Models D. Kim & F. Olivera 10.1061/(ASCE)HE.1943-5584.0000453
13. A long short-term memory model for sub-hourly temporal disaggregation of precipitation H. Oates et al. 10.1007/s00477-025-02996-0
14. Retention performance of green roofs in representative climates worldwide F. Viola et al. 10.1016/j.jhydrol.2017.08.033
15. Use of Radar Information on Atmospheric Precipitation in Applied Hydrology V. Shutov 10.1023/B:WARE.0000028694.42732.77
16. Modelling extreme rainfalls using a modified random pulse Bartlett–Lewis stochastic rainfall model (with uncertainty) D. Cameron et al. 10.1016/S0309-1708(00)00042-7
17. NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process J. Diez-Sierra et al. 10.5194/gmd-16-5035-2023