23 citations as recorded by crossref.

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9. Improving the skill of medium range ensemble rainfall forecasts over India using MoES grand ensemble (MGE)-part-I A. Dube et al. https://doi.org/10.1007/s00703-024-01035-x
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11. Multi-output Deep Learning for Bus Arrival Time Predictions N. Petersen et al. https://doi.org/10.1016/j.trpro.2019.09.025
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13. Assessing the coastal hazard of Medicane Ianos through ensemble modelling C. Ferrarin et al. https://doi.org/10.5194/nhess-23-2273-2023
14. Improved forecasting of thermospheric densities using multi-model ensembles S. Elvidge et al. https://doi.org/10.5194/gmd-9-2279-2016
15. Improving Estuarine Hydrodynamic Forecasts Through Numerical Model Ensembles I. Iglesias et al. https://doi.org/10.3389/fmars.2022.812255
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18. Improving the Accuracy of Rainfall Prediction Using Bias-Corrected NMME Outputs: A Case Study of Surabaya City, Indonesia D. Faidah et al. https://doi.org/10.1155/2022/9779829
19. Multi‐Model Ensembles for Upper Atmosphere Models S. Elvidge et al. https://doi.org/10.1029/2022SW003356
20. Two Models Solutions for the Douro Estuary: Flood Risk Assessment and Breakwater Effects I. Iglesias et al. https://doi.org/10.1007/s12237-018-0477-5
21. Performance of State‐of‐the‐Art C3S European Seasonal Climate Forecast Models for Mean and Extreme Precipitation Over Africa S. Gebrechorkos et al. https://doi.org/10.1029/2021WR031480
22. High-fidelity data-driven dynamics model for reinforcement learning-based control in HL-3 tokamak N. Wu et al. https://doi.org/10.1038/s42005-025-02302-y
23. Applicability of Multimodel Ensemble Prediction of Heavy Precipitation for the Perm Region: A Case Study for the Summer of 2019 A. Vetrov & S. Kostarev https://doi.org/10.3103/S1068373921070037