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Title: 3D reconstruction of ocean velocity from high-frequency radar and acoustic Doppler current profiler: a model-based assessment study
Authors: Manso-Narvarte, Ivan; Fredj, Erick; Jorda, Gabriel; Berta, Maristella; Griffa, Annalisa; Caballero, Ainhoa; Rubio, Anna
Citation: OCEAN SCIENCE, 2020, 16, 575-591
Abstract: The effective monitoring and understanding of the dynamics of coastal currents is crucial for the development of environmentally sustainable coastal activities in order to preserve marine ecosystems as well as to support marine and navigation safety. This need is driving the set-up of a growing number of multiplatform operational observing systems, aiming for the continuous monitoring of the coastal ocean. A significant percentage of the existing observatories is equipped with land-based high-frequency radars (HFRs), which provide real-time currents with high spatio-temporal coverage and resolutions. Several approaches have been used in the past to expand the surface current velocity measurements provided by HFR to subsurface levels, since this can expand the application of the technology to other fields, like marine ecology or fisheries. The possibility of obtaining 3D velocity current fields from the combination of data from HFRs with complementary data, such as the velocity current profiles provided by in situ acoustic Doppler current profiler (ADCP) moorings is explored here. To that end, two different methods to reconstruct the 3D current velocity fields are assessed by a standard approach conceptually similar to OSSEs (observing system simulation experiments), where 3D numerical simulations are used as true ocean in order to evaluate the performance of the data-reconstruction methods. The observations of currents from a HFR and ADCP moorings are emulated by extracting the corresponding data from the 3D true ocean, and used as input for the methods. Then, the 3D reconstructed fields (outputs of the methods) are compared to the true ocean to assess the skills of the data-reconstruction methods. These methods are based on different approaches: on the one hand, the reduced order optimal interpolation uses an approximation to the velocity covariances (which can be obtained from historical data or a realistic numerical simulation) and on the other hand, the discrete cosine transform penalized least square is based on penalized least squares regression that balances fidelity to the data and smoothness of the solution. This study, which is based on the configuration of a real observatory located in the southeastern Bay of Biscay (SE-BoB), is a first step towards the application of the data-reconstruction methods to real data, since it explores their skills and limitations. In the SE-BoB, where the coastal observatory includes a long-range HFR and two ADCP moorings inside the HFR footprint area, the results show satisfactory 3D reconstructions with mean spatial (for each depth level) errors between 0.55 and 7 cm s(-1) for the first 150 m depth and mean relative errors of 0.07-1.2 times the rms value for most of the cases. The data-reconstruction methods perform better in well-sampled areas, and both show promising skills for the 3D reconstruction of currents as well as for the computation of new operational products integrating complementary observations, broadening the applications of the in situ observational data in the study area.
Issue Date: 2020
Type: Article
DOI: 10.5194/os-16-575-2020
ISSN: 1812-0784
Funder: H2020 European Research Council (JERICO-NEXT) [654410]
Appears in Publication types:Artículos científicos

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