25 citations as recorded by crossref.

1. An evaluation of the convection‐permitting ensemble COSMO‐E for three contrasting precipitation events in Switzerland C. Klasa et al. https://doi.org/10.1002/qj.3245
2. A review of operational methods of variational and ensemble‐variational data assimilation R. Bannister https://doi.org/10.1002/qj.2982
3. Model Uncertainty Representation for a Convection-Allowing Ensemble Prediction System Based on CNOP-P L. Wang et al. https://doi.org/10.1007/s00376-020-9262-z
4. The Impact of Stochastically Perturbed Parameterizations on Tornadic Supercell Cases in East China S. Wang et al. https://doi.org/10.1175/MWR-D-18-0182.1
5. Investigation of the predictability and physical mechanisms of an extreme-rainfall-producing mesoscale convective system along the Meiyu front in East China: An ensemble approach Y. Luo & Y. Chen https://doi.org/10.1002/2015JD023584
6. The ARPAL operational high resolution Poor Man’s Ensemble, description and validation M. Corazza et al. https://doi.org/10.1016/j.atmosres.2017.11.031
7. Influence of targeted observations on short-term forecasts of high-impact weather events in the Mediterranean J. Campins et al. https://doi.org/10.5194/nhess-13-2891-2013
8. Representation of model error in a convective-scale ensemble prediction system L. Baker et al. https://doi.org/10.5194/npg-21-19-2014
9. Mesoscale numerical analysis of the historical November 1982 heavy precipitation event over Andorra (Eastern Pyrenees) L. Trapero et al. https://doi.org/10.5194/nhess-13-2969-2013
10. The Impact of a Stochastically Perturbing Microphysics Scheme on an Idealized Supercell Storm X. Qiao et al. https://doi.org/10.1175/MWR-D-17-0064.1
11. A Numerical Sensitivity Analysis of Soil Moisture Feedback on Convective Precipitation M. Koukoula et al. https://doi.org/10.1175/JHM-D-18-0134.1
12. Overview towards improved understanding of the mechanisms leading to heavy precipitation in the western Mediterranean: lessons learned from HyMeX S. Khodayar et al. https://doi.org/10.5194/acp-21-17051-2021
13. Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework M. Stanford et al. https://doi.org/10.1029/2019MS001730
14. Climatology of observed rainfall in Southeast France at the Regional Climate Model scales S. Froidurot et al. https://doi.org/10.1007/s00382-016-3114-7
15. An ensemble study of HyMeX IOP6 and IOP7a: sensitivity to physical and initial and boundary condition uncertainties A. Hally et al. https://doi.org/10.5194/nhess-14-1071-2014
16. Karst spring discharge modeling based on deep learning using spatially distributed input data A. Wunsch et al. https://doi.org/10.5194/hess-26-2405-2022
17. Hydro-meteorological evaluation of a convection-permitting ensemble prediction system for Mediterranean heavy precipitating events B. Vié et al. https://doi.org/10.5194/nhess-12-2631-2012
18. A highly localized high‐precipitation event over Corsica P. Scheffknecht et al. https://doi.org/10.1002/qj.2795
19. Reforecasting Two Heavy-Precipitation Events with Three Convection-Permitting Ensembles V. Capecchi https://doi.org/10.1175/WAF-D-20-0130.1
20. Evaluation of convection-permitting extreme precipitation simulations for the south of France L. Luu et al. https://doi.org/10.5194/esd-13-687-2022
21. Impact of Explicitly Parameterized Mid-to-Low Level Latent Heating on the Simulation of a Squall Line in South China H. Chu et al. https://doi.org/10.3390/w15091743
22. Ensemble simulations with perturbed physical parametrizations: Pre-HyMeX case studies A. Hally et al. https://doi.org/10.1002/qj.2257
23. Impact of Stochastically Perturbed Terminal Velocities on Convective-Scale Ensemble Forecasts of Precipitation S. Wang et al. https://doi.org/10.1155/2020/4234361
24. Fine‐scale numerical analysis of the sensitivity of the HyMeX IOP16a heavy precipitating event to the turbulent mixing‐length parametrization M. Martinet et al. https://doi.org/10.1002/qj.3167
25. On the Time Evolution of Limited-Area Ensemble Variance: Case Studies with the Convection-Permitting Ensemble COSMO-E C. Klasa et al. https://doi.org/10.1175/JAS-D-18-0013.1