12 citations as recorded by crossref.

1. Mitigation of ECMWF–scatterometer wind biases in view of storm surge applications in the Adriatic Sea S. Zecchetto et al. https://doi.org/10.1016/j.asr.2014.12.011
2. The Adriatic Sea: A Long-Standing Laboratory for Sea Level Studies I. Vilibić et al. https://doi.org/10.1007/s00024-017-1625-8
3. A finite element operational model for storm surge prediction in Venice M. Bajo et al. https://doi.org/10.1016/j.ecss.2007.02.025
4. Sea level forecasting in Venice through high resolution meteorological fields L. Zampato et al. https://doi.org/10.1016/j.ecss.2007.02.024
5. Impact of using scatterometer and altimeter data on storm surge forecasting M. Bajo et al. https://doi.org/10.1016/j.ocemod.2017.03.014
6. Assessing storm surge model performance: what error indicators can measure the model's skill? R. Campos-Caba et al. https://doi.org/10.5194/os-20-1513-2024
7. Long-term hazard analysis of destructive storm surges using the ADCIRC- SWAN model: A case study of Bohai Sea, China Y. Wang et al. https://doi.org/10.1016/j.jag.2018.03.013
8. Storm surge forecast through a combination of dynamic and neural network models M. Bajo & G. Umgiesser https://doi.org/10.1016/j.ocemod.2009.12.007
9. Exploiting the Potential of Satellite Microwave Remote Sensing to Hindcast the Storm Surge in the Gulf of Venice F. De Biasio et al. https://doi.org/10.1109/JSTARS.2016.2603235
10. Sensitivity of global storm surge modelling to sea surface drag F. Özkan et al. https://doi.org/10.1007/s10236-025-01713-3
11. Coupling hydrodynamic models with GIS for storm surge simulation: application to the Yangtze Estuary and the Hangzhou Bay, China L. Wang et al. https://doi.org/10.1007/s11707-012-0304-4
12. Optimal index related to the shoreline dynamics during a storm: the case of Jesolo beach R. Archetti et al. https://doi.org/10.5194/nhess-16-1107-2016