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 (
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 %).
Ocean dynamics result from a combination of processes of different timescales and
space scales. However, and mainly due to technical limitations, this
complexity cannot be captured by the existing observational systems if each
observing technique is analysed individually, since they are designed for
resolving certain scales. Nowadays, there is a growing tendency to combine
different observing systems for a more complete description and
understanding of the ocean dynamics. Current observatories are designed to
monitor, in an operational way, the ocean environment to support the human
activities concentrated in the coast (Liu et al., 2015). In recent years, great effort has been focused on the development and improvement of these
platforms. In the framework of European projects such as JERICO (2007–2013)
and JERICO-NEXT (2014–ongoing,
Among the different methodologies to retrieve surface currents, two are particularly interesting due to their high potential complementarity: satellite altimetry and land-based high-frequency (HF) radar (HFR) systems. The former technique consists in a constellation of altimeters onboard satellites measuring the global sea level, with a revisit period greater than a week and a track distance around tens of kilometres. These continuous sea level series are today close to completing 25 years of data, resolving the ocean dynamics from mesoscale to near-climate scale. HFRs are designed to measure the local ocean surface dynamics with a high time and space resolution. However, altimetry and HFR do not capture exactly the same dynamics. Altimetry detects surface currents that are in geostrophic equilibrium (by excluding the direct response of the surface layer to the wind and then part of the high-frequency variations), whereas HFRs measure surface total currents, i.e. the geostrophic and ageostrophic components (like wind-driven and inertial currents or the wave-induced Stokes drift; e.g. Graber et al., 1997; Law, 2001; Ardhuin et al., 2009).
Besides the effort made for collecting data from different platforms, methods for combining these data are under development. Recent studies focused on the evaluation of the performance of altimetry using HFRs, concluding that HFRs offer a way to improve the validation of altimetry products for coastal areas (Chavanne and Klein, 2010; Liu et al., 2012; Pascual et al., 2015; Troupin et al., 2015; Roesler et al., 2013). One of the most extended approaches found in the literature to study the synergy between altimetry and HFR data consists in the comparison of the total across-track currents in the along-track direction (e.g. Morrow et al., 2017; Troupin et al., 2015; Pascual et al., 2015). The combination of HFR and altimetry could help to potentiate their strengths by, for example, expanding the spatial and temporal coverage of the HFR systems or evaluating and correcting the altimetric signal near the coast.
Study area, observational systems and main characteristics of the ocean circulation (figure modified from Rubio et al., 2018). The winter IPC is represented by blue solid arrows, whereas the blue hollow arrows show the mesoscale eddy regime (although only anticyclonic arrows are represented, eddies of anticyclonic and cyclonic polarity are observed in different locations along the slope). The bold black lines delimit the HFR total-current footprint. The black stars represent the HFR stations: Matxitxako (left) and Higer (right). Jason-2 tracks 213 and 248 are represented by black crosses and the part of the track used in this study is marked in red. Grey lines: 1000, 3000, and 4000 m isobaths.
In this study, we focus on the south-eastern Bay of Biscay (SE-BoB), which is characterized by the presence of canyons (e.g. Capbreton canyon), by an abrupt change in the orientation of the coast, and by a narrow shelf and slope. The winter surface circulation in the SE-BoB is mainly related to the Iberian Poleward Current (IPC), which affects the upper 300 m of the water column. In winter, the IPC flows over the slope, advecting warm surface waters (Le Cann and Serpette, 2009; Charria et al., 2013) eastwards along the Spanish coast and northwards along the French coast (Fig. 1). In summer, the flow is reversed, being 3 times weaker than in winter (Solabarrieta et al., 2014). Overlaid to the density-driven slope circulation, wind-induced currents are the main drivers of the surface circulation in the area (e.g. Lazure, 1997; Solabarrieta et al., 2015). During autumn and winter, south-westerly winds dominate and generate northward and eastward drift over the shelf. The wind regime changes to the NE during spring, when it causes sea currents to turn toward the W–SW along the Spanish coast. The summer situation is similar to that of spring, but the weakness of the winds and the greater variability in the direction of the general drift make currents more variable (González et al., 2004; Lazure, 1997; Solabarrieta et al., 2015). In addition to these processes, mesoscale eddies in the SE-BoB are generated, mainly during winter, by the interaction of the IPC with the abrupt bathymetry (Pingree and Le Cann, 1992) (Fig. 1). The combination of these processes makes the SE-BoB an area of interesting complexity.
The existence of a long historical time series of surface current fields from a long-range HFR system in the area provides an invaluable opportunity to explore the benefit of a combined analysis of satellite and land-based remote-sensing ocean currents. This HFR network (two sites; see Fig. 1) is part of the coastal observatory of the SE-BoB, also composed of a network of oceano-meteorological coastal stations and two slope buoys. The performance of this system and its potential for the study of ocean processes and of transport patterns in the area have already been demonstrated by previous work (e.g. Solabarrieta et al., 2015; Rubio et al., 2018). With regard to the usefulness of altimetry for describing ocean dynamics in the BoB, several studies have proven its suitability to study processes that go from mesoscale (Dussurget et al., 2011; Herbert et al., 2011; Caballero et al., 2008b, 2014, 2016) to climate scale (e.g. Pingree, 2005).
The main objectives of this study are first to obtain a diagnosis of the agreement of the surface currents measured by altimetry and HFR over the SE-BoB and, second, to evaluate the observability of certain mesoscale processes by both measuring systems.
HFRs are remote-sensing instruments that send radio waves to the ocean
surface and use the signal backscattered by the waves to infer the radial
velocity of the surface current (toward or away from each HFR antenna). They
can measure surface currents over wide areas with high spatial (300 m–10 km)
and temporal (
To obtain total currents gridded onto a 5 km resolution regular orthogonal
mesh, a least mean square algorithm (spatial interpolation radius of 10 km)
was applied by using the HFR_Progs Matlab package
(
The typical spatial scales resolved by the HFRs depend on the resolution of the data and thus mainly on the operation frequency of the systems (Rubio et al., 2017). For the SE-BoB, the spatial scales resolved are typically of O(15–20) km.
The basic principle of the altimetry technology is to send a radar signal to
the sea surface and then to measure the reflected return echo. The time
needed for the signal to go and come back determines the distance between the
altimeter and the sea surface (called the range). A physically based model
(Brown, 1977) is adjusted to the resulting signal, called waveform, providing
different parameters, including range. To reduce the measurement noise, the
result is averaged and the final data rate is classically (and in our case)
1 Hz (i.e. one datum every
The two different along-track SLA time series used herein come from
Jason-2's 248 and 213 tracks from cycle 18 to 259 and with a revisit period
of
One of the altimetry data sets used in this study is the
CTOH (Center for Topographic studies
of the Ocean and Hydrosphere)-XTRACK product
(
Hourly wind data from the Weather Research and Forecasting model (WRF;
Since the time resolution of the altimetry (
Since from the altimetry data used here we can only obtain sea surface
anomaly currents, as explained in Sect. 2.1.2, the comparisons with the
radar data were carried out in terms of anomaly. In order to obtain the HFR
anomaly currents (AC
For the altimetry velocity, across-track geostrophic anomaly currents (AC
For the statistical comparison between AC
Schematic view of the pointwise comparison and of the data used for
this approximation. Jason-2's 213 and 248 tracks are depicted by orange lines.
The HFR radial directions from both sites are represented by grey lines and the
selected radial directions (from the Matxitxako site) for the pointwise comparison
are plotted in red (the central radial orthogonal to the track) and in blue
(the adjacent radials). Points
This method, previously applied in Liu et al. (2012), consists in a direct
comparison between HFR and altimetry data at a certain point, where one of
the HFR radial directions (red lines in Fig. 2) crosses the altimeter track
perpendicularly. This approximation allows us to directly use the radar radial
currents, which are in the same direction as the across-track AC
In order to make the computations more robust to the potential absence of
HFR or altimetry data at points
On the other hand, the AC
In order to assess the variability in the comparison between HFR and
altimetry from the coast to the open ocean, the comparison between the
across-track AC
Statistics of different points for the study period.
Ekman currents were estimated to evaluate what their contribution to LF
currents in the area was, and how this component contributed to part of the
differences observed between HFR and altimetry. Three different ways of calculating Ekman currents were tested to infer which one provided the best
results in the comparisons: the rule of thumb that states that the surface
currents are 3 % of the wind velocity; Ekman equations for the surface
(Ekman, 1905); and the model M1 proposed in Rio and Hernandez (2003). Ultimately,
the M1 model offered the best results:
Ekman currents initially computed in the locations of the
WRF model nodes were interpolated
and rotated (from zonal and meridional directions to along-track and
across-track directions). For the pointwise comparison, they were
interpolated in
The results of all the comparisons described above are presented in terms of
the correlation coefficient or correlation (
Note that the HFR–altimetry comparisons were carried out for the CMEMS and CTOH
data sets and that each comparison was also made with and without adding AC
Across-track AC
Table 1 provides an overview of the statistical results of the HFR and
altimetry data set comparison at points
Monthly statistical parameters of the comparison between AC
Mean and variance along track 248 of the different data sets – AC
Figure 3 displays the time series of AC
In terms of correlation, the results suggest greater agreement
in
With regard to the performances of the two altimetry products, it must be
highlighted that CTOH shows higher (lower)
In
Figure 3b and d also show the residuals between AC
The IPC winter intensification is visible in all data sets, being stronger
in AC
Since the presence of a stronger IPC signal is expected to improve the correlation between HFR and SLA data sets and the IPC shows marked seasonality, a monthly analysis has been carried out (Fig. 4). The monthly values of the statistical parameters shown in the figure, have been computed considering all the available data for that month during all the study period.
It can be observed that in terms of monthly mean currents, the three time
series have the same tendency and that in general there is low discrepancy
among them. No significant differences in terms of monthly patterns are
observed among the two altimetry products. The winter poleward current
intensification is evident from October to January with a maximum in
November (ranging for all data sets from 7.5 to 13.4 cm s
As has been mentioned previously, Figure 4 shows lower SD values for
AC
At point
Regarding the RMSD, the patterns are similar to those of the SD of
AC
The same statistical parameters computed along track 248 are shown in Fig. 5 in addition to the correlation in order to study the spatial
variability in the comparison between AC
AC
With regard to the variability, it is higher close to the coast. For
AC
It can be once again observed that the addition of the AC
The highest
The maximum (minimum) values of
In general, the addition of AC
In order to provide a complementary insight into the synergies and
differences between HFR and altimeter data, in this section the
observability of different processes detected by HFR and altimetry is
qualitatively analysed. Since the data are spatially filtered (for
AC
Along-track values of SLA
The IPC events are also
detected by AC
Time evolution from 1 January 2009 to 1 July 2015 along track 248
(
More details on these events are provided in Fig. 7, where four selected HFR
total current field (obtained from OMA as explained in Sect. 2.1.1) snapshots
are shown. Although each event is presented for a specific date, they last
around 2–3 weeks (not shown), with the dates displayed in the figure being
representative of all the period. Note that the SST maps do not show the same
date as HFR snapshots and SLA
The relationship between the IPC and the NAO (North Atlantic Oscillation) in the study area was described in Garcia-Soto et al. (2002) and Garcia-Soto (2004). They concluded that for strong IPC years, January water warmings (as a signal of the IPC) were related to negative NAO index values in the previous months (November, December). On the other hand, the eastern Atlantic (EA) is also considered to be a possible factor of the IPC intensification, with positive EA values related to current intensifications. For the four events studied here, the relationship between the IPC intensification and those indexes is shown in Table 2. In general, the NAO (EA) indexes are negatively (positively) related to the IPC in strong intensification periods; however, this relationship does not always apply (see, for instance, the events of 2011 and 2014 where a negative EA index and a positive NAO index are observed, respectively). Moreover, the intensity of the currents is not related to the amplitude of the index, leading to the same conclusion as Le Cann and Serpette (2009) and Le Hènaff et al. (2011).
NAO and EA indexes in the previous 2 months of the events.
Snapshots showing four slope current intensification events observed
by HFR, altimetry, and SST (see the dates of the events depicted in Fig. 6) in
November 2009
Figure 8 shows four examples of eddies detected by the HFR and the altimeter.
Although the effect of the presence of mesoscale eddies has not been explored
in terms of statistical results, there is a qualitative agreement between
AC
Four mesoscale eddies observed in the study area. The dots show the
points of track 248 of the CMEMS database. SLA
In this study, we have investigated the synergies and differences between
land-based HFR and satellite altimetry, two remote-sensing techniques that
provide measurements of the ocean surface currents at different temporal and
spatial scales. A general agreement between HFR and altimetry was observed
in the study area, with correlations ranging up to 0.7. The comparisons were
carried out in terms of time anomaly of currents, following different
approaches with radial and total OMA HFR data. In all cases the addition of
the LF Ekman component (AC
The best agreement between both data sets was observed in the slope area,
mainly between 200 and 1000 m isobaths, where the surface circulation was
dominated by a more energetic geostrophic component. In the coastal area,
the agreement between both data sets was lower. AC
In terms of monthly mean currents, north-eastward currents were observed in
all data sets in late autumn and in winter, while weaker north-eastward and
south-westward currents were observed in spring and summer. In the winter
period, higher variability was also observed at points
Four IPC events were isolated and described further by means of additional
SST data. From this analysis we conclude that during the IPC
intensifications the qualitative agreement between AC
The low correlation between HFR and altimetry observed in some areas and periods can be due to several factors. It is worth noting that both technologies are based on different physical approaches to measure currents, at different spatial and temporal scales, and work under different physical assumptions. Besides, the quality of the radar data is expected to decrease in the furthest points from the antennae and varies as a function of the angle formed by the radial current components used for total current estimations (affecting the along-track comparison). Altimetry also has its own limitations and might have errors in the data editing procedure or in the corrections.
Future work should be oriented towards a better understanding of the relationship of the surface circulation and the dynamics of the subsurface layers by means of the combination of remote observations with data in the water column. Since the comparison near the shoreline is inconsistent, another future work line could be the investigation of the assumptions of geostrophic balance in the coastal area and the merging of altimetry and radar measurements to improve both products. In addition, further comparison with HFR data and higher-resolution coastal altimetry products would enable a better understanding of the differences between both observing systems.
The CTOH-XTRACK altimetry product is available on the CTOH
website
(
The CMEMS along-track level-3 altimetry product is available on the CMEMS
website
(
The SST data used were retrieved from the NERC Earth Observation Data
Acquisition and Analysis Service website
(
The HF radar data are available at
IMN, AC, AR: contribution to the main structure and contents of all sections; drafting, review and final approval of the submitted version. In addition, IMN produced the figures. CD, FB: contribution to the main structure and contents; review of all sections and advice with regard to altimetry issues.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Coastal marine infrastructure in support of monitoring, science, and policy strategies”. It is not associated with a conference.
This study has been supported by the JERICO-NEXT project, funded by the
European Union's Horizon 2020 research and innovation programme under grant
agreement no. 654410 and the COMBAT project supported by the second call of
the Service Evolution of CMEMS. The work of Anna Rubio was partially
supported by the LIFE-LEMA project (LIFE15 ENV/ES/000252). This study has
also been undertaken with the financial support of the Department of
Environment, Regional Planning, Agriculture and Fisheries of the Basque
Government (Marco Program). Ivan Manso-Narvarte was also supported by a
PhD fellowship from the Department of Environment, Regional Planning,
Agriculture and Fisheries of the Basque Government. The HFR system, whose
data have been used herein, is owned by the Directorate of Emergency and
Meteorological Services (DAEM) of the Basque Government. Altimetry data used
in this study were developed, validated, and distributed partly by the
CTOH/LEGOS, France, and partly by Collecte Localisation Satellites (CLS),
France, as the Sea Level Thematic Assembly Center of the Copernicus Marine
Environment Monitoring Service. The SST level-2 images were produced and
distributed by the NERC Earth Observation Data Acquisition and Analysis
Service (NEODAAS,