Model type II regression for lagrangian validation of HF radar velocities in the NW Iberian Peninsula

Authors

  • Adrián Martínez Fernández
  • Waldo Redondo Caride
  • Fernando Alonso Pérez
  • Silvia Piedracoba Varela
  • Pablo Lorente
  • Silvia Allen-Perkins Cáceres
  • Pedro Montero Vilar
  • Garbiñe Ayensa Aguirre
  • Silvia Torres López
  • Adrián Fernández Baladrón
  • Ramiro Alberto Varela Benvenuto
  • Antón Velo Lanchas
  • Miguel Gil Coto

DOI:

https://doi.org/10.5821/iwp.2023.22.14724

Abstract

Two designs of lagrangian low-cost drifting buoys have been developed in order to monitor the ocean surface dynamics in the North-west Iberian Peninsula and provide ground-truth observations that can be used to assess the performance of High Frequency (HF) Radars of RAIA observatory from 2020 to 2022. Since regression model type I, which is typically used in buoy-HF radar antennas validations, does not consider the presence of errors in the observations from both instruments, regression model type II was proposed to instrument intercomparison. Furthermore, a new metric was developed to better assess both model types regressions in lagrangian validations.

Issue

No. 22 (2023)

Section

Articles

License

Universitat Politècnica de Catalunya has joined the Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities. This declaration proposes the universal acces to the results of scientific and technical research.

All papers presented at Martech will be published in both the UPCommons and the Martech organization web sites, the former being the institutional digital deposit of Universitat Politècnica de Catalunya (https://upcommons.upc.edu). This difussion will be made through the Creative Commons License Attribution-NonCommercial-NoDerivs 2.5 or similar (http://creativecommons.org/licenses/by-nc-nd/2.5/deed.en).

When sending the final paper, the author must agree to the conference having non-exclusive rights on this operation. The author will maintain his/her right to diffuse the paper elsewhere.