EGU2020-6658, updated on 16 May 2023
https://doi.org/10.5194/egusphere-egu2020-6658
EGU General Assembly 2020
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Ensemble Approach to Deep Learning and Numerical Ocean Modelling of Adriatic Storm Surges

Matjaz Licer1, Lojze Žust2, and Matej Kristan2
Matjaz Licer et al.
  • 1National Institute of Biology, Marine Biology Station Piran, Slovenia (matjaz.licer@nib.si)
  • 2Faculty of Computer and Information Science, University of Ljubljana

Storm surges are among the most serious threats to Venice, Chioggia, Piran and other historic coastal towns in Northern Adriatic. Adriatic Sea has a well defined lowest seiche period of approximately 22 hours and its amplitude decays on the scale of several days, reinforcing (or diminishing) the tidal signal, depending on the relative phase lag between tides and surges. This makes prediction of Adriatic sea level extremely difficult using conventional deterministic models. The current state-of-the-art predictions of sea surface height (SSH) hence involve numerical ocean models using ensemble forcing. These simulations are computationally-demanding and time consuming, making the method unsuitable for operational or civil rescue services with limited access to dedicated high-performance computing facilities.

Ensemble approach to deep learning offers a possible solution to the challenges described above. Even though training a deep network may involve substantial computational resources, the subsequent forecasting -- even ensemble forecasting -- is fast and delivers near-realtime SSH predictions (and associated error variances) on a personal computer. In this work we present an ensemble SSH forecast using new deep convolutional neural network for sea-level prediction in the Adriatic basin and compare it to the standard approach using state-of-the-art publicly available modelling components (NEMO ocean circulation model and TensorFlow libraries for deep learning).

How to cite: Licer, M., Žust, L., and Kristan, M.: Ensemble Approach to Deep Learning and Numerical Ocean Modelling of Adriatic Storm Surges, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6658, https://doi.org/10.5194/egusphere-egu2020-6658, 2020.

This abstract will not be presented.