Articles | Volume 14, issue 4
https://doi.org/10.5194/gmd-14-2057-2021
https://doi.org/10.5194/gmd-14-2057-2021
Model description paper
 | 
21 Apr 2021
Model description paper |  | 21 Apr 2021

HIDRA 1.0: deep-learning-based ensemble sea level forecasting in the northern Adriatic

Lojze Žust, Anja Fettich, Matej Kristan, and Matjaž Ličer

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Cited articles

Bai, S., Kolter, J. Z., and Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, arXiv preprint arXiv:1803.01271, http://arxiv.org/abs/1803.01271 (last access: 14 April 2021), 2018. a
Bajo, M. and Umgiesser, G.: Storm surge forecast through a combination of dynamic and neural network models, Ocean Model., 33, 1–9, https://doi.org/10.1016/j.ocemod.2009.12.007, 2010. a
Bernier, N. B. and Thompson, K. R.: Deterministic and ensemble storm surge prediction for Atlantic Canada with lead times of hours to ten days, Ocean Model., 86, 114–127, https://doi.org/10.1016/j.ocemod.2014.12.002, 2015. a
Bertotti, L., Bidlot, J.-R., Buizza, R., Cavaleri, L., and Janousek, M.: Deterministic and ensemble-based prediction of Adriatic Sea sirocco storms leading to “acqua alta” in Venice, Q. J. Roy. Meteor. Soc., 137, 1446–1466, https://doi.org/10.1002/qj.861, 2011.  a
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Short summary
Adriatic basin sea level modelling is a challenging problem due to the interplay between terrain, weather, tides and seiches. Current state-of-the-art numerical models (e.g. NEMO) require large computational resources to produce reliable forecasts. In this study we propose HIDRA, a novel deep learning approach for sea level modeling, which drastically reduces the numerical cost while demonstrating predictive capabilities comparable to that of the NEMO model, outperforming it in many instances.
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