During the last 15 years, substantial progress has been achieved in altimetry data processing, now providing data with enough accuracy to illustrate the potential of these observations for coastal applications. In parallel, new altimetry techniques improve data quality by reducing land contamination and enhancing the signal-to-noise ratio. Satellite altimetry provides more robust and accurate measurements ever closer to the coast and resolve shorter ocean signals. An important issue is now to learn how to use altimetry data in conjunction with other coastal observing techniques.
Here, we cross-compare and combine the coastal currents provided by large datasets of ship-mounted acoustic Doppler current profilers (ADCPs), gliders, high-frequency (HF) radars and altimetry. We analyze how the different available observing techniques, with a particular focus on altimetry, capture the Northern Current variability at different timescales. We also study the coherence, divergence and complementarity of the information derived from the different instruments considered. Two generations of altimetry missions and both 1 Hz and high-rate measurements are used: Jason-2 (nadir Ku-band radar) and SARAL/AltiKa (nadir Ka-band altimetry); their performances are compared.
In terms of mean speed of the Northern Current, a very good spatial
continuity and coherence is observed at regional scale, showing the
complementarity among the types of current measurements. In terms of
current variability, there is still a good spatial coherence but the Northern
Current amplitudes derived from altimetry, glider, ADCP and HF radar data
differ, mainly because of differences in their respective spatial and
temporal resolutions. If we consider seasonal variations, 1 Hz altimetry
captures
Radar altimeters allow us to estimate sea surface height (SSH) variations
along satellite tracks at regular time intervals. Providing a large number
of continuous and accurate observations of the global oceans for more than
25 years, they have progressively evolved into one of the fundamental
instruments for many scientific and operational oceanographic applications
(Morrow and Le Traon, 2012). The SARAL mission and its first AltiKa Ka-band
frequency radar, launched in 2013, has improved the performance of
satellite altimetry (Bonnefond et al., 2018). With the launch of
Sentinel-3A and B in February 2016 and April 2018, the altimetry
constellation was completed by the first instruments always operated in high-resolution mode (commonly called synthetic aperture radar or SAR). These new
altimeters provide enhanced along-track resolution and reduced noise in
comparison to the conventional nadir-looking pulse-limited Ku-band
instruments used since the beginning of the altimetry era. In 2021, the SWOT
mission, with its SAR interferometer in Ka-band measuring SSH over 120 km
wide swaths, will be a new step forward
(
In coastal ocean areas, it is particularly important to monitor sea level
variations. About 10 % of the world's population lives in low-elevation
coastal zones (Nicholls and Cazenave, 2010) exposed to hazards such as
extreme events, flooding, shoreline erosion and retreat. The latter are
expected to increase due to the combined effects of sea level rise, climate
change and increasing human activities. In coastal regions in particular, we
expect a lot of advances from modern altimetry instruments and processing
techniques. Indeed, conventional satellite altimetry missions have not been
designed for the observation of coastal dynamics. The strongest
limitation is the modification of the radar echo in the vicinity of land, but
the sea level estimations derived are also impacted by inhomogeneity in the
water surface observed by radar and by less accurate corrections. Coastal
altimetry measurements are much more difficult to interpret than in the open
ocean and need dedicated processing and specific corrections (Gommenginger
et al., 2011; Cipollini et al., 2017a). The data resolution is also too low
to capture the fine scales of coastal ocean dynamics. As a consequence,
most altimetry data collected in coastal zones over the last 25 years have
been discarded in altimetry products and/or poorly exploited. A lot of
effort has been invested during the last 15 years in the altimetry community to
overcome these difficulties, and substantial progress has been achieved on the
data processing side (Roblou et al., 2011; Passaro et al., 2014; Valladeau et
al., 2015; Cipollini et al., 2017a), starting to provide data with enough
accuracy to illustrate the potential of altimetry for coastal applications
(Passaro et al., 2016; Birol et al., 2017a; Morrow et al., 2017). Moreover,
the use of new altimetry techniques provides more robust and accurate
measurements closer to the coast and allows us to resolve shorter spatial scales
(Dufau et al., 2016; Morrow et al., 2017). As an example from Birol and
Niño (2015), closer than 10 km to the coastline, available SARAL data
are
still
Today, observations used in coastal applications are mainly based on in situ
instruments and satellite imagery (sea surface temperature and ocean color
images). In order to address the need for monitoring the coastal ocean
environment, in situ observing systems gather information in a growing
number of regions such as along the Australian and US coasts
(
To study the contribution of altimetry amongst other types of coastal ocean
measurements, the northwestern Mediterranean Sea (NWMed) represents a
laboratory area. First, with a Rossby radius of only
The general objective of this paper is not only to investigate the accuracy of the velocity fields derived from altimetry data next to the coast at different temporal scales, but also to define its contribution compared to the other coastal ocean observing systems that exist in the region (ship-mounted ADCPs, gliders and HF radars). In this study, we combine all the different available in situ datasets that provide information on currents in the Ligurian–Provençal basin and perform systematic comparisons with currents derived from altimetry at different timescales. In particular, we analyze how the different available observing techniques capture NC variability and the coherence, divergence and complementarity of the information derived. From previous studies, we know that only a small part of the NC variations can be captured by conventional satellite altimetry. Here, we use both the Jason-2 and SARAL/AltiKa missions to investigate the progress made from Ku-band to Ka-band altimetry. We also investigate the potential of experimental 20 and 40 Hz altimetry products to monitor NC variations relative to conventional 1 Hz data.
In this paper, Sect. 2 presents the datasets used and the corresponding data processing. It is followed by an intercomparison between the currents derived from altimetry and from the different in situ datasets, with an analysis of the NC variations observed at different timescales by the different instruments (Sect. 3). Section 4 concludes the paper.
We use two altimetry missions with distinct characteristics: Jason-2 and
SARAL/AltiKa. Jason-2 was launched in June 2008 and provides long time series
of data with a 10-day repeat observation cycle. The performance of SARAL is
significantly better. With a better signal-to-noise ratio, it resolves
smaller spatial scales than Jason-2:
For both missions, because it is one of the most often used in coastal
altimetry applications, we first used the X-TRACK regional product from the
CTOH (
The Jason-2 altimeter is designed as “conventional altimetry” as it operates in the Ku-band frequency. The SARAL altimeter operates in the Ka-band, allowing for a better performance in terms of spatial resolution (the radar footprint is smaller) and measurement noise. Morrow et al. (2017) analyzed the “mesoscale capability” (defined as the wavelength at which the noise is larger than the signal, which varies spatially as shown by Dufau et al., 2016) of these two altimeters in the NWMed using a statistical method (Xu and Fu, 2012). It allows us to have an estimate of the size of the structures that can be theoretically detected by each altimeter (on average) and to define the optimal data spatial filtering. Here, we did the same computation for each of the four tracks used in this study using all the data available, unlike in Morrow et al. (2017) in which the data located over the continental shelf were discarded. We obtained 49 km for the SARAL track 302, 39 km for the SARAL track 343, 34 km for the SARAL track 887 and 67 km for the Jason-2 track 222, which is coherent with the results of Morrow et al. (2017), who obtained 39 km for SARAL and 55 km for Jason-2 without the coastal altimetry observations. It suggests that the quality of nearshore altimetry SLA remains good. The lower values obtained for SARAL are due to the better signal-to-noise ratio of the AltiKa altimeter compared to Jason-2. The differences found among the three SARAL tracks are explained by their respective geographical locations. They capture different mesoscale features.
In order to have the best signal-to-noise ratio, we then filtered the data with a low-pass Loess filter using a cutoff frequency of 35 km for SARAL. Note that we have chosen a single value for the different SARAL tracks in order to have the same data processing and facilitate comparison between the different datasets. For Jason-2, we chose to use a processing as close as possible to the one of SARAL and then used a cutoff frequency of 40 km. The same low-pass filters were used for both 1 Hz and high-rate SLAs. One needs to keep in mind that noise remains in the filtered Jason-2 data.
Altimetry only provides sea level anomalies relative to a temporal mean. In
order to estimate currents as close as possible to the currents measured or
derived from the other instruments (see below), we added the regional mean
dynamic topography (MDT) from Rio et al. (2014) to the altimetric SLA and
computed the surface velocities (
Only the across-track component of the geostrophic currents can be derived.
The MDT product used is a regional product with a horizontal resolution of
Gliders have been deployed in the NWMed since 2005. However, it is only since
2009 that they have been regularly operating as part of the MOOSE network
(
Study area and data distribution. Jason-2 and SARAL tracks are represented by the black and blue lines, respectively. The satellite tracks used in the study are indicated in bold. The region in orange corresponds to the HF radar coverage. The Nice–Calvi glider line is in purple and the TETHYS ADCP transect is in green. A map of the schematic regional circulation is presented in the upper left corner.
The campaigns were sliced into ascending (from Calvi to Nice) and descending (from Nice to Calvi) transects and the data were projected on a reference track. We assume that one dive or one ascent represents one vertical profile. In practice, data were discarded when the latitude was not monotonically varying or when the angular deviation between two consecutive points and the mean direction of the reference track was too strong (i.e., larger than 3 standard deviations (SDs) away from the mean angle). Then the data were gridded with a 4 km horizontal bin size along the reference track; 4 km corresponds to the average distance between two successive profiles.
During their mission, gliders measure temperature and salinity from the surface down to 1000 m (less if the bottom is shallower or if commanded to dive shallower). To avoid noise that is mainly due to aliased internal waves, temperature and salinity data have to be filtered. A Butterworth filter of second order (Durand et al., 2017) was applied. Different cutoff frequencies have been tested and we finally chose 15 km to avoid noise without removing small-scale variations (as in Bosse et al., 2017). From the temperature and salinity data we computed the density and then the geostrophic velocity component perpendicular to the reference track using the thermal wind equation. These velocities are referenced to 500 m, corresponding to the depth reached by all gliders. The difference with altimetry-derived currents is then that the barotropic component and the baroclinic component below 500 m are missing.
Since 1997, the TETHYS II RV has collected a large number of ADCP
measurements during frequent repeat cruises between the French coast (Nice)
and the DYFAMED–BOUSSOLE site (43
The HF radar data used here (orange zone in Fig. 1) are also part of the
MOOSE network (Zakardjian and Quentin, 2018). They target the area off the
coast of Toulon as a key zone conditioning the behavior of the NC just
upstream of the Gulf of Lions. Due to a sharp bathymetry and several islands
that deflect the NC southwestward, significant mesoscale variability and
cross-shelf exchanges exist in this area (Guihou et al., 2013), correlated
with strong northwesterly winds (Mistral, Tramontane). The system consists of
two HF (16 MHz) Wellen radar (WERA) instruments installed near Toulon in
monostatic (Cap Sicié station) and bistatic (Cap
Bénat–Porquerolles island
stations) eight-antenna configurations (see Quentin et al., 2013, 2014, for
details). They work with a 50 kHz bandwidth, resulting in a 3 km range
resolution, a direction-finding method based on MUSIC (multiple-signal
classification algorithm; see Lipa et al., 2006; Molcard et al., 2009)
allowing for a 2
In this study, we extensively compare the currents derived from the four different techniques described above with the objective of better understanding how they can optimally complement each other for the observation and study of variability in the NC circulation system. However, we must first have in mind the intrinsic characteristics of each type of current observation and the differences between the datasets.
First, the locations of the different types of observations do not coincide with each other, and their temporal and spatial sampling is also very different. After processing, current values are obtained every 2 km along the ship ADCP track, every 4 km along the glider line, in a 3 km resolution grid for the HF radar, every 5–6 to 7–8 km along the satellite track for 1 Hz Jason-2–SARAL altimetry and every 0.29 to 0.19 km for HF Jason-2–SARAL altimetry. Moreover, each instrument is characterized by specific measurement errors and a specific signal-to-noise ratio. Filtering has to be applied on the glider and altimetry data, still limiting the wavelengths of the current that can be resolved (see above and in Table 1). We also have to keep in mind instrumental limitations concerning the area that can be monitored. The ship ADCPs, the HF radars and the gliders have a higher spatial resolution than the filtered altimetry data but a much more limited spatial coverage. We also have to consider the fact that access to altimetry data, at least in the standard 1 Hz version, still remains limited in the 10–15 km coastal band. As the NC fluctuates in both location and width and at both seasonal and much higher frequencies (Albérola et al., 1995), it can make a large difference in the ability of the instrument considered to capture this current flowing along the continental slope, often located very close to the coastline (Fig. 2).
Main characteristics of the different current datasets used in this study.
Number of data samples per month for each current dataset during the period 1 January 2010–31 December 2016. The number of data selected for the climatology computation is indicated in brackets.
Concerning the temporal sampling, the HF radars and the altimetry provide current observations at regular intervals: every day for the HF radar product used here, every 10 days for Jason-2 and every 35 days for SARAL. The glider and ADCP data are available between zero and nine times per month and between zero and five times per month, respectively. These unevenly spaced time series make the corresponding data analysis more complex since it can produce significant biases in the distribution of the NC properties (for example, its seasonal variations; see Table 2). It will also be influenced by the period of observations available: from about 2 years for the HF radar to more than 6 years for the ADCP, glider and Jason-2 data (see Table 1).
The depth of the current measurement also varies for the different
instruments: HF radars and altimeters observe the ocean surface and
subsurface, while ADCPs and gliders provide vertical sections of measurements.
Using both glider and ADCP data, we compared the currents computed at
different depths (18, 34 and 50 m) and did not find significant differences:
less than 5 cm s
Moreover, the different instruments do not capture the same physical content. The ADCP and the HF radars measure total instantaneous velocities, while the gliders and altimeters allow us to derive only the geostrophic current component perpendicular to the satellite or glider track (i.e., excluding the ageostrophic parts, such as wind-driven surface current, tidal currents and internal waves, and the current component parallel to the track). Unlike the other current data sources used here, altimetry gives only access to current anomalies. But the addition of a synthetic MDT allows us to overcome this difficulty if its quality is good enough to derive a reliable mean velocity field. After the addition of the MDT, the gliders and altimeters are clearly the closest in terms of current information derived. However, the glider currents are computed from hydrographic measurement profiles with a reference level of 500 m. They miss the barotropic and the deeper baroclinic geostrophic current components, while altimetry and MDT allow us to estimate absolute geostrophic currents representative of the horizontal density gradients integrated over the whole water column. In this study, in order to minimize the differences between the current datasets, we performed a projection of the ADCP velocities to obtain the current component perpendicular to the ship transects. Concerning the gliders, estimates of depth-averaged currents computed following the Testor et al. (2018) approach were added to the velocity data as an estimation of the barotropic component.
All the differences mentioned above are summarized in Table 1. If the data appear complementary in terms of space–time coverage and resolution, we can anticipate that their respective characteristics make their comparison and combination an issue. This will be analyzed in detail in Sect. 3.
The results below are obtained from 1 Hz standard altimetry measurements, except in Sect. 3.4, which is dedicated to the analysis of the potential of 20 and 40 Hz altimetry data for coastal circulation studies.
From Fig. 1, we can expect that the different observations mentioned above allow us to efficiently detect different characteristics of the NC (intensity, position) along its axis and the variability of these characteristics. In order to have a first general view of how the different velocity fields compare, we have computed their time average and their standard deviation values at each point of observation for a common period of time: from March 2013 to October 2014. We need to keep in mind that it corresponds to very different sample sizes: 33 ADCP sections, eight glider transects, 484 days of HF radar measurements, and 54–56 and 16 current data points for Jason-2 and SARAL satellite altimetry, respectively. Glider–HF radar observations will then have the lowest–highest significance in terms of statistics. Concerning the HF radars, only the zonal current component is taken into account. Note, however, that in this area, since the NC is almost zonal, most of its mean and variability are captured in the corresponding statistics. Figure 2 shows the resulting map of the mean current and its standard deviation is in Fig. 3. Here, we choose not to represent the results for all the SARAL tracks in order to avoid overloading the figures. Both the regional map (Figs. 2a and 3a) and a zoomed-in view of the northern Ligurian Sea (Figs. 2b and 3b), where the largest number of current observations are located, are shown.
From Fig. 1 (see the circulation scheme), we expect negative–positive current
values along the northern–southern branch of the cyclonic NC system. It
corresponds to what is observed in Fig. 2, in which one can notice a very good
consistency of the mean currents derived from all the different instruments.
Putting together all the pieces of information, the regional structure of the
circulation emerges. As already shown in Birol et al. (2010), in the
Tyrrhenian Sea, the northwestward Tyrrhenian Current (TC) is well observed at
the northern end of Jason-2 track 161. Further north, the NC is formed by the
merging of the Eastern Corsica Current (ECC), captured just east of Corsica
by the Jason-2 track 085, and the Western Corsica Current (WCC), well
captured by both the gliders and the SARAL track 343. The WCC, however, appears
more extended towards the open sea in the SARAL data compared to the glider.
The NC is then strongly constrained by the bathymetry and follows the
continental slope along the coasts of Italy, France and Spain. It can be
continuously followed from the SARAL track 343 to the Jason-2 track 070,
through the ADCP, glider and HF radar observations. Mean NC velocities larger
than
If we focus on the northern Ligurian Sea (Fig. 2b), the cross-track direction
of Jason-2 track 009 is not well oriented compared to the local axis of the
NC. In this area, the continental shelf is very narrow and as a consequence
the NC is very close to the coast: altimetry struggles to observe the
corresponding flow. However, the Jason-2 track 009 and SARAL track 887 still
capture a westward current at their northern end. Considering altimetry,
Jason-2 track 222, located further southwestward, appears better oriented to
monitor the NC. In this area, despite the difference in the number of data
samples, the altimetry, ADCP and glider mean current values are very close:
between
Time–space diagrams of the current velocities derived from
Figure 3 represents the associated current variability, as captured by the
different types of observations. Not surprisingly, in all datasets, larger
standard deviation values generally coincide with the NC system. In
altimetry, we observe values of 0.12–0.2 m s
Considering the intrinsic and important differences between the different
current datasets (Sect. 2.3), these first statistical results are
encouraging. They give a coherent picture of the regional circulation, with,
except for the HF radars that capture a faster current flow, about the same
NC average velocity values. The NC variability is also clearly captured by
the different datasets all along its path, but with significant differences
in terms of amplitude. Note that when we recompute the standard deviations
using a larger period of time (not shown), ADCPs and gliders tend to converge
toward the same cross-shore profile as the one derived from Jason-2 track
222, with a maximum about 0.03 m s
In order to better understand the differences in variability captured by the
various datasets, we analyze the time–space diagrams of the currents derived
from ADCP, HF radar, glider and altimetry data over the period considered
(Fig. 4). We focus on the first 60 km off the French coast and, concerning
altimetry, on SARAL tracks 302 and 887 and on Jason-2 track 222. The HF radar
data correspond to a meridional section of the zonal current component
located at 6.2
Here we compare the monthly climatology (i.e., the mean value for each month
of the year) of the maximum NC amplitude computed from the different current
datasets (ADCP, glider, HF radar and altimetry). This time, we use all the
data available during the period 1 January 2010–31 December 2016 (note that
the HF radar data are only available over the period 2012–2014). Concerning
altimetry, we consider only Jason-2 since we have two to four samples per month for
SARAL, which is not enough to compute meaningful statistics (see Table 2).
For each data sample available, the current profiles along the Jason-2 track
222, the ADCP and glider reference transects, and a meridional HF radar
section located at 6.2
Table 2 lists the temporal distribution of the number of samples included in the calculation as a function of month (in brackets). The data density is much more important than in Sect. 3.1 and the corresponding statistics more robust. It appears relatively stable for Jason-2 altimetry and more heterogeneous for the other observations. The number of in situ data points per month is strongly variable, especially for the ADCP and to a lesser extent for the glider, and varies also a lot from one year to another. A total of 24 ADCP transects are available in 2015 and only 7 in 2012 and 2014, while the glider dataset has a large gap in 2014. As a consequence, the results will only be discussed in terms of seasonal tendencies.
Seasonal variations of the maximum current amplitude derived from
the
In Fig. 5a and b, except altimetry, all the climatologies show a clear and
coherent seasonal cycle of the NC amplitude, with a stronger–lower flow in
winter–summer. As already seen in the previous section, compared to the other
datasets, the HF radars capture a faster NC south of Toulon. Higher NC
velocities are expected in this location (Ourmières et al., 2011). The
corresponding amplitude of the seasonal variations is 0.32 m s
List of the cases of relative co-localization in time between the glider, ADCP and altimetry current data, with the corresponding dates of observations.
Cross-shore sections of currents deduced from the glider (blue), ADCP (red), SARAL (green) and J2 (black) altimetry data for the seven individual cases identified in Table 3. Overlapping periods between the different observations are also indicated.
For further analysis, we consider the dispersion of individual current values
for each month (Fig. 5a, b, envelopes around the curves). We observe
significantly different date-to-date variability for each month: between 0.03
and 0.15 m s
Two physical processes can explain the fact that the differences between the different types of current measurements vary as a function of season. First, the stronger mesoscale variability associated with the NC during winter and spring makes the space and time sampling of the current measurements a more critical issue for the study of this current system at that particular time of year. Second, the strong Tramontane and Mistral winds are more frequent in winter and spring. Then, the differences between the glider and the ADCP current measurements, very close in location, may be more important when the non-geostrophic dynamics (in particular the Ekman flow) produced by the strong winds are more important. The closest seasonal variations to the ones observed by altimetry are found for the glider. It is not surprising since the currents derived from this instrument are also the closest in terms of physical content (see Sect. 2.3). Despite the spatial resolution of the altimetry data and the width and very coastal location of the NC, the amplitude of its seasonal variations captured by the Jason-2 track 222 along the French coast is 55 %–60 % of the amplitude captured by both the gliders and ADCPs.
To learn more about the similarities and differences between the currents derived from the different instruments, as well as their causes, we now analyze the observations on particular dates. In order to minimize, as far as possible, the differences due to distances in space and time between observations, we focus here on the region near Nice (i.e., on the ADCP and glider data, as well as on SARAL track 887 and Jason-2 track 222) and consider only observations that are close in time. For each day of the 2010–2016 study period, we used a time window for each dataset: 5 days for Jason-2, 10 days for the glider and ADCP data, and 22 days for SARAL. We selected only the dates for which the four types of observations are available and finally obtained seven cases that are reported in Table 3. The corresponding cross-track currents are shown in Fig. 6 (by season) as a function of the distance to the point at which the corresponding transect intersects the coastline. For each case and each dataset, we have computed the maximum NC amplitude, following the same method as in Sect. 3.2, and the corresponding location. The latter is expressed in terms of distance to the coast. The results are provided in Table 4.
Figure 6 highlights very different NC situations. Here, the largest coastal
current velocities are observed in spring and not in winter as expected from
Sect. 3.2. Case 1 (Fig. 6a), the only one in this season, shows by far the
strongest NC amplitudes in ADCP and glider data (
Beyond the large variations in the NC characteristics from one case to another, an
interesting feature in Fig. 6 is the presence of an eastward flow
located south of the NC (i.e., 100–150 km to the coast) in altimetry data in
different cases (cases 4, 5 and 6 in particular). The ADCP transect is too
short to capture this current vein and it is not observed in the glider data,
located further east compared to SARAL track 887 and Jason-2 track 222. The
latter rather depict the WCC on the southern edge of its section. To our
knowledge, this offshore eastward flow is not documented in the literature
but its signature also seems to be observed in Figs. 2a and 3a (around
42.5
Finally, what is illustrated in Fig. 6 is that, because of the large short-term changes in the NC circulation system, each snapshot of observations differs significantly from the corresponding seasonal average. It highlights the strong interest in long-term and regular altimetry data to study the persistent components of the NC circulation system, as well as its seasonal variations and possible longer-term changes.
Same as Fig. 6 but for HF altimetry data.
In this section we consider the improvement that is possible to obtain in terms of current derivation with the use of high-rate altimetry measurements, compared to the conventional 1 Hz data used above. However, if research coastal altimetry products that are calibrated, validated, and cover different regions and missions are now available at 1 Hz, this is not the case for high-rate altimetry products. Even if some studies have shown the better performance of 20 and 40 Hz altimeter measurements in observing coastal circulation (Birol and Delebecque, 2014; Gomez-Enri et al., 2016), they are much noisier and there is no consensus yet concerning their (post-)processing. Here, we used an experimental version of high-rate X-TRACK SLA data for both Jason-2 and SARAL, for which original measurements are at 20 and 40 Hz, respectively. Since a lot of erroneous data remained in the coastal area, we applied a 2-sigma filter on the resulting SLA fields along each individual track and cycle in order to edit the data before filtering and the computation of the current estimates (Sect. 2.1).
In order to analyze if we can expect a better observation and understanding of NC variations from high-rate altimetry measurements, they have been used to compute the same diagnostics as in Sect. 3.1, 3.2 and 3.3. Only the results for the individual snapshots will be illustrated here (Fig. 7) since, even if the major difference with the current fields derived from 1 Hz altimetry is that the larger number of coastal data points allows us to estimate currents closer to the coast and then to better resolve the NC flow (see Fig. 7), we did not find significant differences in the NC statistics (i.e., mean current and standard deviation values) or the amplitude of the seasonal cycle computed from 20 and 40 Hz SLA compared to the 1 Hz solutions.
Maximum NC value and distance to the coast of this maximum deduced from the glider, ADCP and altimetry current data for the seven individual cases listed in Table 3.
In Fig. 7, the same color code as in Fig. 6 is used. For each case, as in
Sect. 3.3, the maximum NC amplitude and corresponding location are reported
in Table 4 for both SARAL and Jason-2. In case 1 (Fig. 7a), the gain obtained
with the use of HF data is very clear. On this date the NC vein is narrow and
located near the coast. Contrary to the 1 Hz solution, the NC is better
resolved by both SARAL and Jason-2 high-rate altimetry. It is especially true
for SARAL with NC characteristics that are almost identical to the ones
derived from the gliders. In Jason-2, the NC core is also close to the glider
solution but its amplitude is
As already shown in previous work (Birol and Delebecque, 2014; Gomez-Enri et al., 2016), high-rate altimetry allows us to derive significantly more sea level data near the coast. Here we observe that the coastal circulation derived is better resolved in space, both in terms of horizontal resolution and distance to the coast of the current estimates. However, the resulting current fields depend crucially on the strategy followed for data processing, including retracking, corrections, screening and filtering.
Seasonal climatology maps of cross-track geostrophic currents (in
m s
Here we use only 1 Hz altimetry data. In order to separate the seasonal component of the surface circulation from the mesoscale variations, along each pass of Jason-2 and SARAL located in the area of interest, we have computed a seasonal “climatology” of the cross-track surface geostrophic currents captured by these two altimetry missions (Fig. 8). It was done by simply averaging the corresponding seasonal velocity values for the common 3-year period: April 2013–April 2016. Note that this type of analysis can also be found in Birol et al. (2010) with a much longer period of altimetry data, but with Jason measurements only. The need to use multi-mission observations was incidentally pointed out in this study. Here, indeed, the combination with SARAL data largely improves the spatial resolution of the regional circulation, enabling us to capture the main current veins at many more locations along their path (see Fig. 9 of Birol et al., 2010, for comparison).
In Fig. 8, all the structures of the standard circulation scheme in the NW
Mediterranean Sea (Fig. 1) are observed: the NC, the WCC, the Balearic
Current, the Balearic Front and the TC. What can also be noticed first is the
very good coherence and complementarity between the SARAL and Jason-2
climatologies, especially at crossover points. The seasonal variations in the
regional circulation system, already discussed in detail in Birol et
al. (2010), are confirmed from this different and shorter period of altimetry
observations. In particular, if a stronger and unique southwestward flow is
observed along the Italian, French and Spanish coasts from autumn to spring,
it is not so clear during summer. During this season, the NC does not seem to
continue west of 4
More generally, compared to Birol et al. (2010), the better spatial coverage
obtained by combining both SARAL and Jason-2 reveals a circulation scheme
that could be much more complex than the one classically proposed in the
literature. In summer and autumn (Fig. 8a, d), between 3 and 9
The characteristics of the dynamics, as well as the diverse arrays of in situ instrumentation in the NWMed, offer the possibility to evaluate in detail the complementarity between different types of measurements to monitor coastal ocean circulation. In this study, a systematic comparison of current data derived from different platforms provides new insights into the biases that their differences cause in estimations of NC characteristics. Compared to previous studies comparing altimetry and in situ observations, the originality of this study comes from the number of instruments and observations used, as well as from the long time period addressed and the area covered. It demonstrates that altimetry can be integrated into multi-platform coastal current monitoring systems and enables us to analyze the relative capability of each type of instrument.
The HF radars provide a good daily view of the NC but only for a small area
(
If we consider a reasonably long time series of observations including enough
data samples for each instrument (see Sect. 3.2), in the northern Ligurian
Sea, the average NC value derived from altimetry is
This study also enables us to compare the relative performance of two generations of altimetry missions and of both 1 Hz and high-rate measurements. It confirms that the standard 1 Hz along-track altimetry products derived from Ku-band radars provide meaningful estimations of the NC (as already shown in Birol et al., 2010, and Birol and Delebecque, 2014). The new Ka-band SARAL altimeter data tend to give estimations of the NC characteristics that are closer to in situ data in a number of cases but its 35-day cycle is clearly a strong limitation for the study of this coastal current system. The use of 20 and 40 Hz altimetry measurements significantly improves the number of near-coastal sea level data points and the resolution of the NC. However, the currents derived are still relatively noisy, meaning that their (post-)processing is still at an experimental stage and needs to be improved.
Not surprisingly, another conclusion of this study is that data resolution and sampling are clearly issues in terms of capturing the large range of frequencies found in the NWMed coastal ocean (and we can easily assume that it is true for many other coastal ocean areas). In particular, the temporal data coverage is a large source of differences between NC statistics computed from different observing systems. A second cause of differences in estimations of NC characteristics appears to be ageostrophic flow, principally the Ekman and inertial currents as measured by the ADCP and HF radars but not represented by the glider (even if they can be partially included through the correction of depth-averaged currents), and altimeter-derived geostrophic currents. Clearly, a multi-data combined approach is a unique way to obtain a complete picture of a dynamical system as complex as the NC.
Finally, it is important to note that improved altimetry data processing and corrections as well as technical innovations lead to an ever increasing number of coastal data points ever closer to the coastline. It raises the question of the calibration and validation of these new data against independent in situ observations. How can we robustly quantify the evolution of the new processing and products? We benefit from the long experience of nadir altimetry technology, widely based on tide gauge sea level observations taken as an independent reference. However, a full understanding and exploitation of the new performances allowed by Ka-band, SAR and SAR-in altimetry techniques, as well as by the use of high-rate altimetry measurements, requires new methods and validation means. We advocate for the fact that only a combination of in situ instruments providing regular cross-shore information along altimetry tracks will allow us to understand and exploit the full capability of altimetry in coastal observing systems and guide its evolution.
Altimetry data used in this study were developed, validated
by the CTOH/LEGOS, France, and distributed by Aviso
This work was carried out by AC as part of her PhD thesis under the supervision of FB and CE. BZ processed the HF radar data, and PT provided the glider data. The paper was prepared with contributions from all coauthors.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Hydrological cycle in the Mediterranean (ACP/AMT/GMD/HESS/NHESS/OS inter-journal SI)”. It is not associated with a conference.
This study was done with the financial support of the Region Occitanie and
the CNES through their PhD funding programs. The ship-mounted ADCP data were
kindly provided by the DT-INSU, who conducted the acquisition, management and
processing. Altimetry data used in this study were developed, validated and
distributed by the CTOH/LEGOS, France. Glider data were collected and made
freely available by the Coriolis project (