Joint analysis of coastal altimetry and high-frequency (HF)?radar data: observability of seasonal and mesoscale ocean dynamics in the Bay of Biscay

摘要

Land-based coastal high-frequency (HF) radar systems provide operational measurements of coastal surface currents (within 1–3?m depth) with high spatial (300?m–10?km) and temporal ( ≤1 h) sampling resolutions, while the near-continuous altimetry missions provide information, from?1993 until today, on geostrophic currents in the global ocean with typical along-track and temporal sampling resolutions of 7 km and 9 days, respectively. During the last years, the altimetry community has made a step forward in improving these data in the coastal area, where the data present lower quality than in the open ocean. The combination of HF radar and altimetry measurements arises as a promising strategy to improve the continuous monitoring of the coastal area (e.g.?by expanding the measurements made by HF?radars to adjacent areas covered by the altimetry or by validating/confirming improvements brought by specific coastal algorithms or new altimeter missions). A first step towards this combination is the comparison of both data sets in overlapping areas. In this study, a HF?radar system and two Jason-2 satellite altimetry products with different processing are compared over the period from 1?January?2009 to 24?July?2015. The results provide an evaluation of the performance of different coastal altimetry data sets within the study area and a better understanding of the ocean variability contained in the HF?radar and altimetry data sets. Both observing systems detect the main mesoscale processes within the study area (the Iberian Poleward Current and mesoscale eddies), and the highest correlations between radar and altimetry (up to?0.64) occur in the slope where the Iberian Poleward Current represents a significant part of the variability in the circulation. Besides, the use of an Ekman model, to add the wind-induced current component to the altimetry-derived geostrophic currents, increases the agreement between both data sets (increasing the correlation by around 10?%).