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

Hernández‐Carrasco, Ismael; Solabarrieta, Lohitzune; Rubio, Anna; Esnaola, Ganix; Reyes, Emma; Orfila, Alejandro

doi:10.5194/os-14-827-2018

Cited by 30 publications

(14 citation statements)

References 76 publications

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“…For this work, radial currents were processed to generate Open Mode Analysis (OMA) spatially gap-filled total derived currents (Kaplan and Lekien, 2007), by using the HFradar_Progs Matlab package 1 , based on Gurgel (1994) and Lipa and Barrick (1983). 85 OMA modes, built setting a minimum spatial scale of 20 km, were used to generate hourly gap-filled total fields (Solabarrieta et al, 2016;Hernández-Carrasco et al, 2018).…”

Section: Data and Simulations Hf Radar Datamentioning

confidence: 99%

Integration of HF Radar Observations for an Enhanced Coastal Mean Dynamic Topography

et al. 2020

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Satellite altimeters provide continuous information of the sea level variability and mesoscale processes for the global ocean. For estimating the sea level above the geoid and monitoring the full ocean dynamics from altimeters measurements, a key reference surface is needed: The Mean Dynamic Topography (MDT). However, in coastal areas, where, in situ measurements are sparse and the typical scales of the motion are generally smaller than in the deep ocean, the global MDT solutions are less accurate than in the open ocean, even if significant improvement has been done in the past years. An opportunity to fill in this gap has arisen with the growing availability of long time-series of high-resolution HF radar surface velocity measurements in some areas, such as the south-eastern Bay of Biscay. The prerequisite for the computation of a coastal MDT, using the newly available data of surface velocities, was to obtain a robust methodology to remove the ageostrophic signal from the HF radar measurements, in coherence with the scales resolved by the altimetry. To that end, we first filtered out the tidal and inertial motions, and then, we developed and tested a method that removed the Ekman component and the remaining divergent part of the flow. A regional high-resolution hindcast simulation was used to assess the method. Then, the processed HF radar geostrophic velocities were used in synergy with additional in situ data, altimetry, and gravimetry to compute a new coastal MDT, which shows significant improvement compared with the global MDT. This study showcases the benefit of combining satellite data with continuous, high-frequency, and synoptic in situ velocity data from coastal radar measurements; taking advantage of the different scales resolved by each of the measuring systems. The integrated analysis of in situ observations, satellite data, and numerical simulations has provided a further step in the understanding of the local ocean processes, and the new MDT a basis for more reliable monitoring of the study area. Recommendations for the replicability of the methodology in other coastal areas are also provided. Finally, the methods developed in this study and the more accurate regional MDT could benefit present and future high-resolution altimetric missions.

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Section: Data and Simulations Hf Radar Datamentioning

confidence: 99%

Integration of HF Radar Observations for an Enhanced Coastal Mean Dynamic Topography

et al. 2020

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“…The BoB HFR has been chosen as the pilot system for testing the developed methodology because of our previous knowledge regarding the circulation and dynamical processes in the study area (Rubio et al 2013, Solabarrieta et al 2014, Solabarrieta et al, 2015, Rubio et al, 2018, Hernandez-Carrasco et al 2018. The resulting methodology is then applied to the operational Red Sea HFR dataset, as a study case.…”

Section: Datamentioning

confidence: 99%

A new Lagrandian based short term prediction methodology for HF radar currents

Solabarrieta

Hernández‐Carrasco

Rubio

et al. 2020

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Abstract. The use of High Frequency Radar (HFR) data is increasing worldwide for operational oceanography and data assimilation, as it provides real-time coastal surface currents at high temporal and spatial resolution. In this work, a Lagrangian based empirical real-time, Short-Term Prediction (L-STP) system is presented in order to provide short term forecasts of up to 48 hours of ocean currents from HFR data. The method is based on the finding of historical gridded analogues of Lagrangian trajectories obtained from HFR surface currents. Then, assuming that the present state will follow the same temporal evolution as did the historical analogue, we obtain a short-term prediction of the surface currents. The method is applied to two HFR systems covering two areas with different dynamical characteristics: the southeast Bay of Biscay and the central Red Sea. The L-STP improves on previous prediction systems implemented for the SE Bay of Biscay and provides good results for the Red Sea study area. A comparison of the L-STP methodology with predictions based on persistence and reference fields has been performed in order to quantify the error introduced by this Lagrangian approach. Furthermore, a temporal sensitivity analysis has been addressed to determine the limit of applicability of the methodology regarding the temporal horizon of Lagrangian prediction. A real-time skill-score has been developed using the results of this analysis which allows to identify periods when the short-term prediction performance is more likely to be low and persistence can be used as a better predictor for the future currents.

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“…The data interpolating empirical orthogonal functions (DINEOF) (Beckers and Rixen 2003;Alvera-Azcarate et al 2005) was used to obtain a full dataset. DINEOF is a widely accepted method for reconstructing missing datasets (Huang et al 2017;Hernández-Carrasco et al 2018). It is a self-consistent, parameter-free technique that has the advantage of not needing a priori information.…”

Section: Data Interpolating Empirical Orthogonalmentioning

confidence: 99%