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Volume 14, issue 4 | Copyright

Special issue: Coastal marine infrastructure in support of monitoring, science,...

Ocean Sci., 14, 827-847, 2018
https://doi.org/10.5194/os-14-827-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 24 Aug 2018

Research article | 24 Aug 2018

Impact of HF radar current gap-filling methodologies on the Lagrangian assessment of coastal dynamics

Ismael Hernández-Carrasco1, Lohitzune Solabarrieta2, Anna Rubio3, Ganix Esnaola4,5, Emma Reyes1, and Alejandro Orfila6 Ismael Hernández-Carrasco et al.
  • 1ICTS-SOCIB, 07122, Palma, Spain
  • 2King Abdullah University of Science and Technology (KAUST), Red Sea Research Center (RSRC), Biological and Environmental Sciences and Engineering Division (BESE), Thuwal 23955-6900, Saudi Arabia
  • 3AZTI Marine Research, 20110, Pasaia, Spain
  • 4Nuc. Eng. and Fluid Mechanics Department, Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), Donostia-San Sebastian, Spain
  • 5Joint Research Unit BEGIK, Instituto Español de Oceanografía (IEO), Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), 48620, Plentzia, Spain
  • 6Oceanography and Global Change Department, IMEDEA (CSIC-UIB), 07190, Esporles, Spain

Abstract. High-frequency radar, HFR, is a cost-effective monitoring technique that allows us to obtain high-resolution continuous surface currents, providing new insights for understanding small-scale transport processes in the coastal ocean. In the last years, the use of Lagrangian metrics to study mixing and transport properties has been growing in importance. A common condition among all the Lagrangian techniques is that complete spatial and temporal velocity data are required to compute trajectories of virtual particles in the flow. However, hardware or software failures in the HFR system can compromise the availability of data, resulting in incomplete spatial coverage fields or periods without data. In this regard, several methods have been widely used to fill spatiotemporal gaps in HFR measurements. Despite the growing relevance of these systems there are still many open questions concerning the reliability of gap-filling methods for the Lagrangian assessment of coastal ocean dynamics. In this paper, we first develop a new methodology to reconstruct HFR velocity fields based on self-organizing maps (SOMs). Then, a comparative analysis of this method with other available gap-filling techniques is performed, i.e., open-boundary modal analysis (OMA) and data interpolating empirical orthogonal functions (DINEOFs). The performance of each approach is quantified in the Lagrangian frame through the computation of finite-size Lyapunov exponents, Lagrangian coherent structures and residence times. We determine the limit of applicability of each method regarding four experiments based on the typical temporal and spatial gap distributions observed in HFR systems unveiled by a K-means clustering analysis. Our results show that even when a large number of data are missing, the Lagrangian diagnoses still give an accurate description of oceanic transport properties.

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A new methodology to reconstruct HF radar velocity fields based on neural networks is developed. Its performance is compared with other methods focusing on the propagation of errors introduced in the reconstruction of the velocity fields through the trajectories, Lagrangian flow structures and residence times. We find that even when a large number of measurements in the HFR velocity field is missing, the Lagrangian techniques still give an accurate description of oceanic transport properties.
A new methodology to reconstruct HF radar velocity fields based on neural networks is developed....
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