scholarly journals Variational interpolation of high-frequency radar surface currents using DIVAnd

2021 ◽  
Vol 71 (3) ◽  
pp. 293-308
Author(s):  
Alexander Barth ◽  
Charles Troupin ◽  
Emma Reyes ◽  
Aida Alvera-Azcárate ◽  
Jean-Marie Beckers ◽  
...  

AbstractDIVAnd (Data-Interpolating Variational Analysis, in n-dimensions) is a tool to interpolate observations on a regular grid using the variational inverse method. We have extended DIVAnd to include additional dynamic constraints relevant to surface currents, including imposing a zero normal velocity at the coastline, imposing a low horizontal divergence of the surface currents, temporal coherence and simplified dynamics based on the Coriolis force, and the possibility of including a surface pressure gradient. The impact of these constraints is evaluated by cross-validation using the HF (high-frequency) radar surface current observations in the Ibiza Channel from the Balearic Islands Coastal Ocean Observing and Forecasting System (SOCIB). A small fraction of the radial current observations are set aside to validate the velocity reconstruction. The remaining radial currents from the two radar sites are combined to derive total surface currents using DIVAnd and then compared to the cross-validation dataset and to drifter observations. The benefit of the dynamic constraints is shown relative to a variational interpolation without these dynamical constraints. The best results were obtained using the Coriolis force and the surface pressure gradient as a constraint which are able to improve the reconstruction from the Open-boundary Modal Analysis, a quite commonly used method to interpolate HF radar observations, once multiple time instances are considered together.

Ocean Science ◽  
2021 ◽  
Vol 17 (3) ◽  
pp. 755-768
Author(s):  
Lohitzune Solabarrieta ◽  
Ismael Hernández-Carrasco ◽  
Anna Rubio ◽  
Michael Campbell ◽  
Ganix Esnaola ◽  
...  

Abstract. The use of high-frequency radar (HFR) data is increasing worldwide for different applications in the field of 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 h of ocean currents. The method is based on finding historical analogs of Lagrangian trajectories obtained from HFR surface currents. Then, assuming that the present state will follow the same temporal evolution as the historical analog, we perform the forecast. 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. A comparison of the L-STP methodology with predictions based on persistence and reference fields is performed in order to quantify the error introduced by this approach. Furthermore, a sensitivity analysis has been conducted 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 for the identification of 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.


2020 ◽  
Vol 70 (12) ◽  
pp. 1485-1503
Author(s):  
Dylan Dumas ◽  
Anthony Gramoullé ◽  
Charles-Antoine Guérin ◽  
Anne Molcard ◽  
Yann Ourmières ◽  
...  

2012 ◽  
Vol 62 (7) ◽  
pp. 1073-1089 ◽  
Author(s):  
Ana Julia Abascal ◽  
Sonia Castanedo ◽  
Vicente Fernández ◽  
Raúl Medina

2010 ◽  
Vol 61 (5) ◽  
pp. 599-610 ◽  
Author(s):  
Alexander Barth ◽  
Aida Alvera-Azcárate ◽  
Jean-Marie Beckers ◽  
Joanna Staneva ◽  
Emil V. Stanev ◽  
...  

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Lei Ren ◽  
Stephen Nash ◽  
Michael Hartnett

This paper details work in assessing the capability of a hydrodynamic model to forecast surface currents and in applying data assimilation techniques to improve model forecasts. A three-dimensional model Environment Fluid Dynamics Code (EFDC) was forced with tidal boundary data and onshore wind data, and so forth. Surface current data from a high-frequency (HF) radar system in Galway Bay were used for model intercomparisons and as a source for data assimilation. The impact of bottom roughness was also investigated. Having developed a “good” water circulation model the authors sought to improve its forecasting ability through correcting wind shear stress boundary conditions. The differences in surface velocity components between HF radar measurements and model output were calculated and used to correct surface shear stresses. Moreover, data assimilation cycle lengths were examined to extend the improvements of surface current’s patterns during forecasting period, especially for north-south velocity component. The influence of data assimilation in model forecasting was assessed using a Data Assimilation Skill Score (DASS). Positive magnitude of DASS indicated that both velocity components were considerably improved during forecasting period. Additionally, the improvements of RMSE for vector direction over domain were significant compared with the “free run.”


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