Articles | Volume 14, issue 4
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-Carrasco, Lohitzune Solabarrieta, Anna Rubio, Ganix Esnaola, Emma Reyes, and Alejandro Orfila


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Ismael Hernández-Carrasco on behalf of the Authors (21 Jun 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (05 Jul 2018) by Stefania Sparnocchia
RR by Anonymous Referee #2 (18 Jul 2018)
ED: Publish subject to minor revisions (review by editor) (23 Jul 2018) by Stefania Sparnocchia
AR by Anna Mirena Feist-Polner on behalf of the Authors (03 Aug 2018)  Author's response    Manuscript
ED: Publish as is (04 Aug 2018) by Stefania Sparnocchia
Short summary
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.