# Coastal applications from nadir altimetry: Example of the X-TRACK regional products

F. Birol, N. Fuller, F. Lyard, M. Cancet, F. Niño, C. Delebecque, S. Fleury, F.

Toublanc, A. Melet, M. Saraceno, et al.

► To cite this version:

F. Birol, N. Fuller, F. Lyard, M. Cancet, F. Niño, et al.. Coastal applications from nadir altimetry: Example of the X-TRACK regional products. Advances in Space Research, 2017, 59, pp.936-953. 10.1016/j.asr.2016.11.005. insu-04835210

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Accepted Manuscript

Coastal applications from nadir altimetry: example of the X-TRACK regional products

F. Birol, N. Fuller, F. Lyard, M. Cancet, F. Niño, C. Delebecque, S. Fleury, F. Toublanc, A. Melet, M. Saraceno, F. Léger

PII: S0273-1177(16)30631-7

DOI: http://dx.doi.org/10.1016/j.asr.2016.11.005

Reference: JASR 12962

To appear in: *Advances in Space Research*

Received Date: 12 February 2016

Revised Date: 7 October 2016

Accepted Date: 3 November 2016

Please cite this article as: Birol, F., Fuller, N., Lyard, F., Cancet, M., Niño, F., Delebecque, C., Fleury, S., Toublanc, F., Melet, A., Saraceno, M., Léger, F., Coastal applications from nadir altimetry: example of the X-TRACK regional products, *Advances in Space Research* (2016), doi: http://dx.doi.org/10.1016/j.asr.2016.11.005

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

<figure><img src="image_2.png" /></figure>
Coastal applications from nadir altimetry: example of the X-TRACK
regional products

F. Birol(1), N. Fuller(1), F. Lyard(1), M. Cancet(2), F. Niño(1), C. Delebecque(1), S. Fleury(1), F. Toublanc(1), A. Melet(1,3), M. Saraceno(4), F. Léger(1)

(1) LEGOS, University of Toulouse, IRD, CNES, CNRS, UPS, Toulouse, France

(2) Noveltis, 53, rue du Lac - F-31670 Labège, France

(3) Mercator-Océan, 10 Rue Hermès, 31520 Ramonville-Saint-Agne, France

(4) Centro de Investigaciones del Mar y la Atmósfera, CONICET, Departamento de Ciencias de la Atmósfera y los Océanos, Universidad de Buenos Aires, and UMI/ IFAECI, CONICET-CNRS-UBA, Intendente Guiraldes 2160 - Ciudad Universitaria, Pabellón II - 2do. Piso (C1428EGA) Ciudad Autónoma de Buenos Aires, Argentina

Corresponding author: Florence.Birol@legos.obs-mip.fr

Submitted to Advances in Space Research, 12th of February 2016

Revised, 3rd of June2016
**Abstract**

In the coastal ocean zones, satellite altimetry data processing and interpretation poses specific
difficulties, due to the interaction of the radar signal with land topography, inaccuracies in
some of the geophysical corrections and to the fast changes in the sea level. In order to
optimize the completeness and the accuracy of the sea surface height information derived
from satellite altimetry in coastal ocean areas, a dedicated post-processing software, called X-
TRACK, has been developed by the Center of Topography of the Ocean and Hydrosphere in
Toulouse. It is tailored for extending the use of altimetry data to coastal ocean applications
and provides freely available along-track Sea Level Anomaly time series that cover today all
the coastal oceans. Here, we present the improvements made in version 2016 of X-TRACK
and show the gain in near-coastal data accuracy using *in situ* tide gauge observations. The
correlations between altimeter and tide gauge sea level anomalies are higher (by 15% in
average) compared with the previous version of X-TRACK. Three examples of applications
are shown. The recent evolutions done in the X-TRACK processing result in an improved
observation of the seasonal variations of the boundary circulation in the Bay of Biscay. Along
Western Africa, sea-level variations derived from X-TRACK data are observed closer to land
(5 km) compared to AVISO (10 km), and the sea-level statistics are more robust due to the
larger and more stable data availability. Along-track empirical tidal constants derived from X-
TRACK Sea Level Anomaly time series are also used to validate tidal models. By improving
the altimetric data accuracy in coastal areas, we extend the field of marine applications.

**Keywords:** Radar altimetry; Sea surface height; Database; Ocean dynamics; Coastal observations; CTOH;
# 1. Introduction

Satellite altimetry has monitored the sea surface topography variations from space for more
than two decades, developing into a mature technique that is currently used in operational
applications. Freely available, satellite altimetry data has become a very important component
of the ocean observing system. It has been intensively used for understanding the ocean
dynamics (for example: *Fu and Chelton, 2001; Morrow and Le Traon, 2012*) and is now one
of the primary sources of data for operational oceanography systems and services such as
ocean monitoring, forecasting models and climate change management. As a consequence,
most of these services are directly impacted by the quality and timely availability of the
altimetry data sets (*Le Traon, 2011*).

Several altimetry missions have been launched since the late 1980s. The radar
instruments and the data processing have been optimized for the open oceans, whereas sea
level measurements encounter many problems in coastal environments, resulting in a rapid
degradation of the data accuracy in the near-shore region (i.e 30-50 km from the coast,
*Anzenhofer et al., 1999; Gommenginger et al., 2011; Andersen and Scharroo, 2011; Birol and
Delebecque, 2014*). The radar echo itself interacts with the surrounding land and it becomes
very difficult to interpret the signal within the 5-10 km coastal region (*Deng et al., 2002;
Deng and Featherstone, 2006; Gommenginger et al., 2011*). Other important limitations come
from the geophysical corrections (e.g. wet troposphere, ionosphere, sea state bias, inverse
barometer, high frequency wind effect and tides), which might become inaccurate or incorrect
in shallow waters (e.g. *Andersen and Scharroo, 2011*).

However, monitoring the global coastal oceans is extremely important: as an area of
very high biological productivity and of critical economic importance, the coastal ocean is
directly affected by human activity. Furthermore, climate change is likely to worsen many
problems that coastal environments already face: shoreline erosion, coastal flooding, water
pollution, stress and damage of the coastal biodiversity (Harley et al., 2006). Enhancing the
observation and modelling systems is essential to better understand and predict the behavior
of the coastal ocean (Liu et al., 2015). Improvements in the availability and quality of
altimetry data in shallow waters work towards this goal (Vignudelli et al., 2011).

In recent years, great progress has been made in altimeter instruments, processing
algorithms and corrections (Vignudelli et al., 2011; Passaro et al., 2014; Verron et al., 2015;
Pascual et al., 2015; Cotton et al., 2015). However, the spatial and the temporal resolution of
altimetry data (10 to 35-day orbital cycle, 70 to 350-km inter-track spacing at the equator) is
not sufficient to sample the wide spectrum of length scales observed in the coastal ocean
(from hours to years for time scales, from tens of meters to tens of kilometers for spatial
scales; see for example Robinson and Brink, 2005; Huthnance, 1995). The interpretation of
the near shore sea surface height (SSH) observations in terms of internal coastal ocean
dynamics is also more complicated to analyze than the offshore dynamics because of more
complex processes (Liu and Weisberg, 2007; Bouffard et al., 2010).

To address all the technical and scientific problems mentioned above (i.e. development
of coastal data processing strategies, understanding of the near-shore sea level variations,
analysis of the usefulness and limitations of altimetry data in coastal ocean applications), the
experience gained from the existing altimetry data records is absolutely crucial. A number of
projects have been funded by space agencies to improve conventional satellite radar altimetry
products over coastal areas and continental waters (PISTACH and PEACHI for the French
Centre National d'Etudes Spatiales, COASTALT and eSurge for the European Space
Agency). In parallel, a post-processing algorithm specifically tailored to recover altimetry
data over marginal seas was developed at LEGOS (Laboratoire d'Etudes en Géophysique et
Océanographie Spatiale) more than ten years ago. This software, called X-TRACK, is now
routinely operated at the Center of Topography of the Ocean and Hydrosphere (CTOH,
French Observation Service dedicated to satellite altimetry studies) and a large part of the
archive of altimetry data has been reanalyzed. X-TRACK regional products are freely
distributed, providing Level 3 altimetry products from different altimetry missions for
coastal and regional applications.

The main objective of this article is to demonstrate how the reanalysis of satellite altimetry observations provides new ways of understanding the coastal ocean, focusing on the X-TRACK project. The article is organized as follows: section 2 covers the most recent evolutions of the X-TRACK processing software and products and evaluates the quality of the corresponding data sets, using tide gauges as reference. Section 3 illustrates through some examples how improvements in altimetry products bring valuable information on the coastal ocean dynamics. Finally, section 4 highlights main results and discusses some perspectives.

## 2. A regional post-processing technique for coastal altimetry applications

Several studies have been dedicated to the evaluation and improvement of some altimetry corrections in coastal ocean areas (Andersen and Sharroo, 2011; Obligis et al., 2011; Ray et al., 2011; Fernandes et al., 2015). But very few included an analysis of the different corrective terms as a whole, with the aim to maximize the number of useful near shore sea level data finally available for the user. This is the objective that motivated the development of the X-TRACK algorithm (Roblou et al., 2011), more than ten years ago. In order to compute Level-3 along-track sea level anomalies (SLA), X-TRACK edits the main corrections, looking for the best trade-off between the quantity and the quality of observations finally available (section 2.1). Investigations made apparent that the data editing methodology could be further improved in order to increase the quality of the near-shore SLA data. In this article we present a fully revised version of the X-TRACK algorithm.
In the remainder of this study we use the terms X-TRACK 2011 (in reference to Roblou
et al., 2011, where the X-TRACK post-processing strategy was presented) and X-TRACK
2016, in order to distinguish, respectively, the previous and new versions of the software.
Differences between them are presented in section 2.2, and the SLA data computed with both
algorithms are compared to in situ measurements (section 2.3).

**2.1 An improved strategy to compute sea level anomaly time series**

The X-TRACK software reprocesses in delayed time the Geophysical Data Records provided
by space agencies (GDRs products). Its inputs are the measurements and parameters provided
in the GDRs for a number of altimeter missions plus additional corrections and auxiliary data
specifically distributed by the CTOH. The selected corrections for each X-TRACK-processed
altimetry mission are given in Table 1. The historical processing (before 2016) is described in
Vignudelli et al. (2005) and Roblou et al. (2011) and only a brief summary is presented here.
It starts with the selection of the altimetry measurements located in the region of interest,
defined as a regional polygon based on a precise land mask. A two-step editing strategy is
then used to recover the maximum of meaningful sea level observations. The first step
consists in rejecting potential outliers through the analysis of the different corrective terms.
Abrupt changes in geophysical corrections are assumed to be associated with errors. First,
threshold values are used (see Table 9.1 in Roblou et al., 2011) and then a 3σ or 4σ
(depending on the coastal area) threshold filter is applied along the satellite track (σ is the
standard deviation of the alongtrack record). In addition to outliers in corrections, their
adjoining neighbors, which often contain erroneous values, are also eliminated. This strategy
rejects much more SSH data than the standard procedure used by operational centers (cf
editing criteria in the Aviso User Handbooks). Thus, in a second step, all the correction values
are interpolated from a Bezier curve built from the edited data. One should note that only the corrections are interpolated in X-TRACK, not the altimeter measurements. Examples of this procedure are illustrated in Figure 2 of Vignudelli et al. (2005) and Figure 9.1 of Roblou et al. (2011). The efficiency of this approach was shown in different studies (Vignudelli et al., 2005, Durand et al., 2008, Birol et al., 2010 and Bouffard et al., 2011).

Once the corrected SSHs are derived using Eq. (1), a mean sea surface height (MSSH) is computed along the nominal ground track, at equally sampled reference points (i.e. every 6-7 km for 1-Hz data). This is done using an inverse method, which accounts for the cross track gradient of the SSH values (Lyard and Roblou, 2003). The data are then resampled at the reference points along the nominal track: the SLA time series are obtained by subtracting to the corrected SSH data both the MSSH at the closest reference point and the MSSH difference between the closest reference point and the actual location of the altimeter observation. This procedure is important since it was found that in coastal areas, where the surface topography gradients are large, inaccurate MSSH leads to significant errors in SLAs (Vignudelli et al., 2005).

$$ \text{corrected SSH} = \text{altitude of satellite} - \text{altimeter range corrected from instrumental corrections} - \Sigma \text{propagation corrections} - \Sigma \text{geophysical corrections} $$

(1)

Remaining outliers in SLAs are finally removed by a 3σ threshold filter, where σ is the standard deviation of the SLA time series at the considered reference point. The MSSH (and thus the SLA) precision is further enhanced by combining the SSH time series of different missions that share the same orbit (e.g. Topex/Poseidon, Jason1&2) in order to maximize the
period over which the MSSH is computed. This also provides homogeneous time series from
several altimetry missions over long time periods.

## 2.2 X-TRACK recent updates

The editing strategy of the X-TRACK software has been entirely revisited in the 2016 version. The general principle remains the same but each corrective term is now edited in a different way in order to take into account its individual characteristics (i.e. the corresponding geophysical variations along the track). The processing of the ionospheric, wet tropospheric and sea state bias corrections are presented below. In the other altimeter corrections, usually derived from models, very few values are discarded by the editing process. Flagged corrections are simply replaced by their nearest valid neighbors.

### 2.2.1 Ionospheric correction

The ionospheric correction calculated from the dual frequency altimeter measurements is noisy and has to be spatially filtered before it is removed from the altimeter range (Imel, 1994). A median absolute deviation (MAD) threshold filter is now applied to the original along-track record instead of the 3σ-threshold filter used in X-TRACK 2011. This new filter detects outliers more efficiently since the median operator is much less influenced by extreme values than the standard deviation around the mean. The MAD value is computed using Equation (2). The ionospheric corrections values larger than 3.5*MAD are considered as outliers and rejected.

$$MAD = \frac{1}{n} * \sum_{i=1}^{n} |x_i - R_{med}(X)_i| \quad (2),$$

where n is the number of data in the record and $R_{med}(X)$ is its running median value.
Figure 1 illustrates the corresponding editing process for the example of the cycle 10 of Jason-1 along the ground track 222 in the Mediterranean Sea. After the data cleaning, the along-track ionospheric corrections are smoothed by using a moving average filter. The number of points used in the moving average varies as a function of the measurement time. It is equal to 20 (i.e. ~140 km), except during the 0-6h time period when it is set to 25 (i.e. ~175 km), as the density of free electrons in the ionosphere is lower during this period of the day and induces smaller variations of the ionospheric path delay. In order to keep as much information as possible near the coast, a “mirror image” of the data time series is added onto the end of the series (i.e. near the coastline); this technique is appropriate because the ionospheric path delay exhibits little variation during the transit time between the open to the coastal ocean.

Finally, as in the X-TRACK 2011 processing, the ionospheric correction is interpolated at the times of the valid range measurements using a Bezier curve which further low-pass filters the data and ensures a continuous slope of the signal in the case of data gaps (due to the presence of islands for example), as shown on Figure 1.

Note that the processing method described above is applied only when the ionospheric path delay is derived from the range measurements (dual frequency correction). When it is retrieved from models (i.e. for GFO and Envisat missions), no low-pass filtering is required.

### 2.2.2 Wet tropospheric path delay:

The wet tropospheric correction is known to be a major component of the altimeter sea level error budget in coastal areas (Andersen and Sharroo, 2011). Significant research is put into improving the accuracy of this corrective term in land/sea transition areas (Obligis et al., 2011; Fernandes et al., 2015). One approach aims at removing the contamination coming
from the surrounding land in the radiometer derived correction. In comparison to X-TRACK
2011, an algorithm computing the proportion *p* of land within the radiometer footprint has
been included in X-TRACK 2016, where *p* depends on the onboard radiometer characteristics
of each altimeter mission. For this purpose, the gridded product of distance to nearest
shoreline of Wessel and Smith is used
(ftp://ftp.soest.hawaii.edu/gshhg/dist_to_GSHHG_v2.3.4_1m.nc derived from version 2.3.4
of the GSHHS database). In a conservative approach, all the wet tropospheric correction
values for which *p* > 0 are rejected. Finally, farther from the coast, the other possible outliers
are discarded by an algorithm that detects large differences between the correction values at
two consecutive points along the track for each cycle. The resulting data gaps are either
interpolated (away from the coast) or filled with the closest values qualified as valid (in the
land/sea transition areas). Using a technique based on the discrete wavelet transform (see
Donoho, 1995 for more details), a cleaned and noise-free wet tropospheric correction is then
computed. Figure 2 illustrates the correction editing and reconstruction for the example of the
cycle 8 of Jason-1 along the ground track 222 in the Mediterranean Sea. The rapid variations
in space observed near the coast in the original correction (where its accuracy is expected to
degrade) are largely attenuated after the editing process.

2.2.3Sea-state bias correction

The sea-state bias (SSB) correction is the sum of three components: an electromagnetic bias which accounts for stronger reflection of wave troughs relative to wave crests, the skewness bias related to the difference between the mean and the median of the height of specular facets, and the tracker bias which is the error in the tracker determination of the Significant Wave Height (SWH) (Gaspar et al., 1994; Scharroo and Lillibridge, 2005). The determination of accurate sea state bias corrections is another challenging issue for coastal
altimetry because of the rapidly changing characteristics of the waves and the wind, and
because of noisy waveforms. The SSB correction provided in GDRs products is generally
computed from parametric (BM4; Gaspar et al., 1994) or non-parametric models (Gaspar
and Florens, 1998). Since the altimeter sea level variance is minimized by construction, the
smoothing applied in X-TRACK 2011 on this correction (using Bezier curves) reduced the
correlation between altimeter and tide gauge sea level data as well as it increased the altimetry
SLA variance. In X-TRACK-2016, the SSB correction is filtered in the along-track direction
by using a Loess low-pass filter, and missing values are replaced by the nearest interpolated
data.

2.2.4 SLA editing

Within X-TRACK 2011, the 1-Hz SLA time series computed (see section 2.1), were finally
edited using a 3σ threshold filter (4σ in the Tropical Pacific). In the X-TRACK 2016 strategy,
we consider that the difference in SLA that can be explained by the ocean dynamics between
consecutive 1-Hz measurements should be limited in amplitude. Considering the geostrophic
approximation and a maximum geostrophic current speed of 2 m/s at mid latitudes, the SLA
difference (δSLA) over 7 km should not be higher than 14 cm. After computation of the
corrected SSH (Eq. 1), temporary filtered SLA values SLAL are calculated relative to the
mean sea surface provided in the GDR products, using a low-pass local regression (Loess).
SLA values outside the interval [SLAL ± δSLA] and the corresponding sea level measurements
are not further used in X-TRACK processing (including the computation of the MSSH and
the final SLA data).
## 2.3 Sea level data quality: gain inferred from X-TRACK updates

In order to assess the impact of the X-TRACK updates described in section 2.2 on the data availability, we first compare the quantity of valid altimetry SLA (i.e., not flagged) provided by the two versions of the software (X-TRACK 2011 versus X-TRACK 2016). This comparison is performed in the Mediterranean Sea. All Topex/Poseidon and Jason-1&2 1-Hz measurements are considered from March 1993 to May 2013 (a total of 747 cycles). The 1-Hz along-track regional delayed-time SLA product distributed by the AVISO operational service is also used for information. Figure 3a shows the number of valid SLA measurements over the 20-year period for all the tracks crossing the Mediterranean Sea for AVISO (in green), X-TRACK 2011 (in red) and X-TRACK 2016 (in black); the results are represented as a function of their respective distance to the coast. The thick curves indicate the mean behaviour of the three SLA datasets. The differences between the two X-TRACK and AVISO products are observed not only near the coasts but also offshore, with ~20 cycles less on average for AVISO. For both versions of the X-TRACK products, the data availability is the same at distances larger than 20 km from the coast. It is larger for X-TRACK 2011 at distances shorter than 10 km (7% more cycles on average), whereas the availability of the X-TRACK 2016 data is slightly higher between 10 and 20 km. The standard deviations of the corresponding SLA time series have also been computed. They are also represented as a function of the distance to the coast (Figure 3b). AVISO shows lower standard deviation values than X-TRACK everywhere (~0.5 cm), probably due to a different spatial filter applied on the along-track SLA data and to a long wavelength error correction used to remove correlated noise due to orbit errors or uncertainties in geophysical corrections (not applied in X-TRACK processing). On Figure 3b, we can also observe that in X-TRACK 2016, compared to X-TRACK 2011, the signal variance is significantly reduced and much closer to AVISO at distances shorter than ~40 km from the coast.
In the near-shore area, significant differences are obtained with the two versions of the X-TRACK processing, both in terms of data availability and SLA standard deviation. In order to assess the respective quality of the altimetry datasets computed with X-TRACK 2011 and X-TRACK 2016, they were compared to in-situ "ground truth" sea level measurements. A number of tide gauge stations distributed at very different coastal sites have been used for this purpose. Here we show only the results for the 6 stations listed in Table 2. To make in situ sea level as consistent as possible with the altimeter SLA data, the tidal signals were removed from the hourly tide gauge observations (using harmonic analysis) and the same dynamic atmospheric correction (DAC) was applied. Concurrent time series from tide gauges and altimetry SLA were finally obtained by removal of the mean sea level value computed over the same time period. For each tide gauge station, we considered the 8 nearest 1-Hz altimetry points (i.e. a spatial coverage of about 50 km) and we computed the standard deviations, the root mean square (RMS) of the differences and correlation coefficients between the in-situ and altimetry SLA time series. These statistics are summarized in the Taylor diagrams (Taylor, 2001) shown on Figure 4. The results from X-TRACK 2011 and X-TRACK 2016 are in red and in black, respectively. For X-TRACK 2016, the correlations with the tide gauge observations are better in every case and the cluster of statistical values closer to the reference provided by the tide gauge data. Whatever the distance of the corresponding satellite observations to the tide gauge, the near-shore altimeter SLA values processed with the X-TRACK 2016 software are in better agreement with the equivalent ground truth, which confirms that the recent updates made to the X-TRACK editing strategy lead to the computation of more accurate coastal altimetry SLA data.

In this section we have shown only results for the Topex/Poseidon and Jason-1,2 missions as their combination leads to the longest time series of SLA data. Similar results (not
shown) have been obtained for all the altimeter missions available in the X-TRACK products (Table 3).

## 2.4 Added-value coastal altimetry products: tidal constants

The improvement of the coastal altimetry SLA data has naturally led the CTOH to develop derived regional added-value altimetry products based on the X-TRACK processing software. The T/P-Jason SLA time series are long enough (more than 23 years at a near 10-day sampling) to derive the empirical harmonic constants (amplitude and phase lags) for a number of tidal constituents (Table 4) by using harmonic analysis. In practice, the latter is applied on the SLA record fully corrected except for ocean tides, at each point along the ground-tracks (every 6-7 km). The set of constituents analyzed directly depends on the length and the temporal sampling of the SLA record, as these parameters drive the separation between the tidal constituents. In case of limited time series (such as for the T/P-Jason interleaved track observations), admittance methods are used for astronomical tides to relax the formal Rayleigh criterion (on which separation qualification is based) and a priori tidal correction is performed before analyzing the residuals and finally reforming the full harmonic constants. In addition to the harmonic constants, two associated errors are estimated for each tidal constituent. The first one is directly computed from the inverse method theory (formal error linked with constituent separation issues). The second one is calculated by taking into account the ocean background signal at aliased tidal frequencies (empirical error linked with non-tidal signal contamination). These errors are part of the regional X-TRACK tidal product. More details can be found in Carrere et al. (2004).

As an example, Figure 5a,b shows the results of the harmonic analysis performed on X-TRACK SLA data for the M2 empirical tidal constituent in the southwest Atlantic area. This region includes the Patagonian Shelf, which produces one of the world's largest tidal
amplitudes and complex shallow water tidal components (Le Provost and Lyard, 1997). The root-mean-square of the along-track differences (RMSE) in amplitude derived from altimetry and from the Finite Element Solution (FES2012) global tidal model are also computed for reference (Figure 5c). Results indicate a very good agreement between the model and the altimetry-derived tidal constants over the open ocean (for depth > 200m the RMSE is generally lower than 2 cm for M2). Larger differences are observed in coastal regions, with RMSE values exceeding 10 cm over the Patagonian Shelf. It may be due to either erroneous altimeter data or/and to model errors. However, we know that tidal model performances are weaker in shallow waters because of inaccurate bathymetry, more complex tides and insufficient model resolution (Stammer et al., 2014). This can be further investigated by a comparison to in situ tide gauge measurements. Figure 6 shows the example of the San Matias Gulf (Argentina) tide gauge record. The comparison analyzes the most important tidal constituents released by different global tidal models (FES2004, FES2012 and GOT4.7) as well as by the harmonic analysis of the X-TRACK satellite altimetry data. In this area, as shown in Table 5, X-TRACK tidal constant product clearly presents the best accuracy for the M2, S2 and N2 solutions, which are the dominant tidal constituents at regional scale (Lago et al., in preparation). This example also illustrates the interest of computing tidal constants from altimetry as they represent an independent source of information for the validation of tidal models (see section 3.3).

### 3. Some examples of applications

Many research studies used the X-TRACK regional altimetry products. They concern a wide range of applications: coastal ocean circulation, mesoscale dynamics, hydrological and tidal model validation, tides, development or validation of new altimeter processings (for example Durand et al., 2009; Birol et al., 2010; Cancet et al., 2012; Liu et al., 2012; Passaro et al.,
2014). The examples presented below highlight that the efforts made to improve coastal altimetry products enable significant improvement in the observation of the coastal ocean dynamics and open up the way to new applications.

## 3.1 Boundary current variations: example of the Bay of Biscay

The complex bathymetry of the Bay of Biscay, together with strong tides and high frequency wind fluctuations, induces highly variable currents. The seasonal variability of the regional circulation has been mainly documented from in-situ observations (Pingree and Le Cann, 1990; Pingree, 1993; Pingree et al., 1999; Charria et al., 2013; Le Boyer et al., 2013). The largest currents are observed along the northern Spanish coast. Over the abyssal basin, the mean circulation tends to be anticyclonic. Along the continental slope, it becomes cyclonic in winter, with anticyclonic flow seen in the south-west corner during summer. On the continental shelf, currents are dominated by wind, tides and freshwater inputs. However, a seasonally reversing flow appears, poleward/equatorward in winter/summer. Knowledge about the intra-seasonal and interannual time scales of the current fluctuations is still limited.

Altimetry provides a long time series of data and could be used to further investigate the circulation of the region. However, Dussurget et al. (2011) and Xu et al. (2015) have shown that standard gridded products are too smooth to correctly capture the current characteristics of the Bay of Biscay. Herbert et al., 2011 and Le Hénaff et al., 2011 obtained valuable information on the regional slope current variations using the original along-track data from T/P-Jason1&2 missions. But they also highlighted that errors in near-shore altimetry data significantly impacted their ability to depict part of the current variations. Here, we explore if the recent evolutions done in the X-TRACK processing result in some improvements in the monitoring of the seasonality of the circulation over the continental slope of the Bay of
Biscay. This is done using monthly climatologies of geostrophic current anomalies (GCA) calculated from 20-years of altimetry data obtained with X-TRACK (1994-2013).

Cross-track GCA are first calculated from the low-pass filtered X-TRACK SLA (Loess filter with a 40 km cut-off frequency in order to reduce measurement noise). A monthly climatology of GCA is then computed by averaging the corresponding monthly values of the long time series. The results obtained for November and July are shown in Figure 7 for both X-TRACK 2011 (top) and X-TRACK 2016 (bottom). Before discussing the results, note that previous studies (Pingree and Le Cann, 1990; Le Hénaff et al., 2011; van Acken, 2002) reported that the mean surface current component is weak over the continental slope. It means that the altimeter derived GCA give a good estimation of the absolute surface currents.

The seasonality of the regional surface circulation is clearly captured by both versions of X-TRACK data (Figure 7), in particular along the continental slope and over the shelf. In the southern part of the Bay of Biscay, strong eastward currents are observed in autumn and winter (Figure 7, left), corresponding to the signature of the Iberian Poleward Current (IPC). Strong westward currents are observed in summer (Figure 7, right), in agreement with previous studies (Pingree and Le Cann, 1990; Charria et al., 2013; Solabarrieta et al., 2014). However, differences are observed between X-TRACK 2011 and X-TRACK 2016 in the near-shore currents. In X-TRACK 2011, the spatial variations of the coastal circulation are larger than in X-TRACK 2016. For example, X-TRACK 2011 shows a current inversion along the Spanish continental slope in November along track 213, not consistent with the eastward slope currents observed on the two surrounding tracks. In July, on track 172, the maximum velocities are obtained over the shelf, very close to the coast. By contrast, in X-TRACK 2016 data, the slope current cores are located on the continental slope and the signal remains significantly more consistent between altimeter tracks. The comparison with local in-situ observations (section 2.3, Santander tide gauge) confirms that the improvement in coastal
sea level data accuracy obtained in X-TRACK 2016 results in a better coastal current observation.

A more detailed analysis of the space-time variability of the regional circulation is beyond the scope of the present study and will be included in a forthcoming publication. However, this example illustrates the potential of satellite altimetry to depict and study the variability of coastal current systems (see also Bouffard et al., 2008; Durand et al., 2009; Birol et al., 2010; Melet et al., 2010, Jébri et al., 2016). This is especially true in regions where the ocean circulation is poorly sampled by *in situ* measurements, such as in the southern hemisphere.

## 3.2 Coastal sea level variability

Satellite altimetry data are largely used to study the mean sea level dynamics (see for example: Llovel et al., 2009; Meyssignac and Cazenave, 2012; Carson et al., 2016). Because of the larger data uncertainty when approaching land, their ability to capture coastal sea-level variations (SLV) deserves specific attention. Here, we analyze the changes in SLV from the open to the coastal ocean provided by two different data processings: X-TRACK 2016 and AVISO. Along-track datasets from the T/P, Jason1 and Jason2 missions over the 1993-2012 time period are used. In this example, we focus on the sections of the altimetry tracks that are located less than 200 km off the coast of Western Africa (Figure 8 a, c). Along each coastal section, we computed the SLV changes (in terms of RMS) relative to the "offshore" SLV (defined here as the SLV averaged over the 160-200 km offshore portion of the satellite track, chosen because of the width of the regional continental shelf). Figure 8 (a, c) shows that SLV off the western African coast generally increases from the open to the coastal ocean areas in both AVISO and X-TRACK, especially between 0 and 15°N. The spatial patterns of SLV derived from the two altimetry products are very similar but the corresponding values differ
significantly at specific coastal locations, where this information may be critical for local issues.

In order to further analyse this result, a more synthetic view is provided in Figure 8 (b, d), where the SLV is averaged across all the coastal sections and is represented as a function of the distance to the coast. Mean (blue line) and standard deviation (grey envelope) of SLV values are plotted only when data from at least 10 coastal alongtrack sections are available. A comparison of panels (b) and (d) of Figure 8 shows that, as in Figure 3, more data are available in the X-TRACK product, compared to AVISO (red points). As a result, the SLV evolution from the open to the coastal ocean is observed more than 10 km from the coastline in AVISO (5 km for X-TRACK, i.e closer to land). The data availability remains also more constant in X-TRACK up to 15-20 km from land. The sea level statistics obtained are then more robust and coherent in space for X-TRACK than for AVISO. In both products, on average over the region, the SLV steadily increases toward the coast in the 200 km coastal band (blue lines): it is ~25% larger between 5 to 10 km offshore than between 160 to 200 km offshore. This increase appears to evolve linearly with respect to the distance to the coast in X-TRACK, due to the larger and more stable data availability. This result is encouraging since it illustrates that the efforts made to bring more satellite altimetry data near the coast may allow significant progresses in the observation of the detailed SLV changes along coastlines. In particular, the combination of improvements in processing and new technologies should permit a better study of the long-term evolution of sea level and extreme events (such as storm surges) in the coastal zone.
## 3.3 Tidal model validation

Tide gauge observations are used to estimate the performances of tidal models in the coastal regions and on the shelves. However the spatial coverage of reliable tide gauges is uneven: the largest concentration of tide gauges is located in the Northern Hemisphere, with important gaps in other regions, such as the Indian Ocean. On the other hand, the tidal constants derived from altimetry measurements offer a regular geographical sampling on the whole continental shelves and not only at a single point. Furthermore, because of their coastal location, tide gauges do not sample the same local tidal dynamic systems than global tidal models, even when the latter benefit from a high-resolution mesh. Indeed, the tidal signal recovered by the tide gauges is often affected by the complex morphology of the coast where they are located, the nearby presence of rivers and other geophysical coastal effects (e.g. *Ruiz-Etcheverry et al.*, 2015). In this sense, tidal constants retrieved from near-shore altimetry SLA measurements offer information more representative of the tidal dynamics solved by the models.

Because it is derived from improved near-shore satellite altimetry data, the X-TRACK tidal constant product provides very accurate tidal information over all the continental shelves in Earth, as discussed on section 2.4. Like tide gauge data, the X-TRACK altimetry-derived tidal constant database is consequently a very useful tool as an independent source of information for the validation of tidal models in the coastal zones.

The X-TRACK tidal constants were used for the validation of the FES2012 and FES2014 global tidal atlases (the most recent atlases of a series of global finite element tidal solutions initiated by *Le Provost et al.*, 1994). Here, the performances of seven global tidal models are estimated in the frequency domain by computing the vector differences between each model solution and the X-TRACK tidal product, for each tidal component solved by the models. Figure 9 shows the results for the M2 tidal component, and for each of the regions where the X-TRACK (2011) products are available. The various models show very different
performances in some regions, such as the Amazonian shelf, the Indian Ocean and the
Indonesia region, where the FES2014 model is the closest to the altimeter observations.

Figure 10 shows the vector differences between three of the global models and the X-TRACK tidal constants for the M2 component on the Amazonian shelf. The main differences between the models and the altimetry data are clearly located in the Amazon River estuary, where the tidal signal is complex due to non-linear interactions (Le Bars et al, 2010). The X-TRACK tidal constant product makes possible to easily create a synoptic view of the tidal model performance along the altimeter tracks.

Finally, if the X-TRACK tidal constant dataset can be used to validate tidal models, it
can also be used for data assimilation in regional tidal models as a complement to tide gauge
observations. Some experiments have already been done with very good results, for example
in the North East Atlantic Ocean (see for example the regional tidal models distributed
through the tidal prediction web-service TIPS, http://tips.noveltis.com/).

**4. Conclusion and perspectives**

In this paper we presented the upgrades done in X-TRACK 2016. We estimated their impact
on statistics (spatial and temporal coverage of the computed data sets, sea level variations)
and in terms of comparisons with independent in-situ data. Results clearly show the benefit of
the changes performed in the X-TRACK processing software: a more efficient detection of
outliers in altimetric corrections, a better reconstruction of the missing or rejected correction
values, and a substantial increase in the correlation between near-shore altimetry SLA and
tide gauge records. Acknowledging user needs, a tidal constant product has also been
developed in addition to Level-3 SLA data sets. Three examples of applications based on the
X-TRACK product are shown. They illustrate how a better altimetry data post-processing
technique not only allows a significant increase in the altimetry data accuracy over a large part of the coastal ocean, but also in information on near-shore sea level variations and related ocean dynamics. The seasonal variations of the boundary circulation derived from altimetry in the Bay of Biscay are better resolved. Along Western Africa, the estimation of coastal sea-level variations from space is improved. Along-track empirical tidal constants derived from X-TRACK sea level time series complements the tidal information provided by tide gauge data and tidal models.

For the next few years, the X-TRACK software will continue to evolve. It will include the new generation of Ka-band and SAR altimeter measurements (SARAL/AltiKa, Sentinel-3a&b) and benefit from the improvements in retracking algorithms (Gommenginger et al., 2011; Passaro et al., 2014) and in altimeter corrections (Fernandes et al., 2015; Ray et al., 2011). The X-TRACK products will also be systematically extended to high-sampling rate SLA data (ranging from 10-Hz to 40-Hz, depending on the altimeter mission) in order to improve the resolution of the sea level observations near the coasts (Birol and Delebecque, 2014). The transfer of the product distribution to the operational AVISO+ service has already started (http://aviso.altimetry.fr). All X-TRACK products should be easily accessible from a user-friendly web interface in the near future.

We are entering a new era of satellite altimetry, in which radar instruments are able to provide measurements closer to the coast, with a higher spatial resolution, and a higher precision (Cotton et al., 2015; Birol and Niño, 2015; Verron et al., 2015; Pascual et al., 2015; Cipollini et al., 2015). Future missions include Sentinel-3B (planned for 2017), Sentinel-6, Jason-CS (Jason Continuity of Service, planned for 2020) and SWOT (Surface Water and Ocean Topography, planned for 2020). The SWOT Ka-band SAR interferometry mission (Fu et al., 2012, http://swot.jpl.nasa.gov/), will provide high spatial resolution 2D observations and mark a breakthrough in the measurement of coastal water surface elevations from space.
These new and future altimeter instruments offer completely new perspectives for applications. This study also shows that they should play a major role in coastal observing systems.

## Acknowledgements

This study was funded by the French Institut National des Sciences de l'Univers (INSU/CNRS), in the framework of its observational programs, and by the CNES through its scientific TOSCA/OSTST program. Support from these institutions is gratefully acknowledged. The X-TRACK altimetry data are developed and validated by the CTOH/LEGOS (http://ctoh.legos.obs-mip.fr/) and distributed by the CTOH/LEGOS and by AVISO+ (http://www.aviso.altimetry.fr). The standard altimeter products were produced and distributed by AVISO+, as part of the SSALTO ground processing segment. We acknowledge Laurent Roblou for his support in the early developments of the X-TRACK software. We also wish to acknowledge the contribution of the French services that make the tide gauge data available: SONEL (http://www.sonel.org/) and REFMAR (http://refmar.shom.fr/), as well as support from ECOS-Sud A14U02, EUMETSAT/CNES DSP/ OT/12-2118 and CONICET- YPF PIO 133-20130100242.

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Tables

<table><caption>**Table 1:** List of altimetry corrections used in the computation of X-TRACK SLA data for both X-TRACK 2011 and X-TRACK 2016 versions of the algorithm.</caption><thead><tr><th>Corrections</th><th>Topex/Poseidon</th><th>Jason-1</th><th>Jason-2</th><th>Geosat Follow On</th><th>Envisat</th></tr></thead><tbody><tr><td>Ionosphere</td><td>From dual frequency altimeter range measurements + GDR Correction Product (GCP) corrections</td><td>From dual-frequency altimeter range measurements</td><td></td><td>From the Global Ionospheric Maps (GIM) model (Iijima et al, 1999)</td><td></td></tr><tr><td>Dry troposphere</td><td>From ECMWF model</td><td></td><td></td><td>From NCEP</td><td>From ECMWF</td></tr><tr><td>Wet troposphere</td><td>From radiometer + GCP corrections</td><td>From enhanced radiometer correction (S. Brown)</td><td></td><td>From model</td><td>From radiometer</td></tr><tr><td>Sea state bias</td><td>From non parametric empirical model + GCP corrections</td><td>From non parametric empirical model</td><td></td><td>4.5% of the Significant Wave Height</td><td>From non parametric empirical model</td></tr><tr><td>Solid tides</td><td>From tide potential model</td><td></td><td></td><td></td><td></td></tr><tr><td>Pole tides</td><td>From Wahr, 1985</td><td></td><td></td><td></td><td></td></tr><tr><td>Loading effect</td><td>From FES1999 (Finite Element Solution) global tidal model</td><td></td><td></td><td></td><td></td></tr><tr><td>Atmospheric correction (DAC)</td><td>From T-UGOm 2D global model (Carrere and Lyard, 2003) for periods smaller than 20 days + Inverted Barometer (IB) for periods greater than 20 days, derived from ECMWF surface pressure fields.</td><td></td><td></td><td></td><td></td></tr><tr><td>Ocean tide</td><td>From FES2012 global tidal model (Carrère et al., 2012).</td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><caption>**Table 2:** Location of tide gauge stations, corresponding T/P-Jason1,2 satellite track and distance from the closest altimeter data.</caption><thead><tr><td>Tide gauge</td><td>Latitude</td><td>Longitude</td><td>Corresponding T/P-Jason track</td><td>Distance from the closest altimeter data (km)</td></tr></thead><tbody><tr><td>Nice</td><td>43.69°N</td><td>7.28°E</td><td>9</td><td>26.9</td></tr><tr><td>Sao Tome</td><td>0.35°N</td><td>6.73°E</td><td>33</td><td>24.5</td></tr><tr><td>Esperance</td><td>33.86°S</td><td>121.9°E</td><td>64</td><td>47.4</td></tr><tr><td>Valparaiso</td><td>33.03°S</td><td>238.36°E</td><td>230</td><td>22.9</td></tr><tr><td>Ko Taphao</td><td>7.43°N</td><td>98.42°E</td><td>166</td><td>10.8</td></tr><tr><td>Santander</td><td>43.46°N</td><td>356.23°E</td><td>213</td><td>8.1</td></tr></tbody></table>

<table><caption>**Table 3:** List of X-TRACK SLA products available and the first and last dates of the time series. The corresponding cycle number is indicated in parentheses.</caption><thead><tr><td>Mission</td><td>Start</td><td>End</td></tr></thead><tbody><tr><td>Topex/Poseidon (T/P)</td><td>1992/02/28 (17)</td><td>2002/08/12 (364)</td></tr><tr><td>Jason-1 GDR-C</td><td>2002/01/15 (1)</td><td>2009/01/27 (259)</td></tr><tr><td>Jason-2 GDR-D</td><td>2008/07/12 (1)</td><td>2013/06/10 (181)</td></tr><tr><td>T/P+Jason-1+Jason-2</td><td>1993/02/28 (17 of T/P)</td><td>2013/06/10 (181 of Jason-2)</td></tr><tr><td>T/P interleaved</td><td>2002/09/21 (369)</td><td>2005/10/09 (481)</td></tr><tr><td>Jason-1 interleaved</td><td>2009/02/10 (262)</td><td>2012/02/15 (372)</td></tr><tr><td>Envisat-v2.1</td><td>2002/10/01 (10)</td><td>2010/09/14 (92)</td></tr><tr><td>Geosat Follow On (GFO)</td><td>2000/01/08 (37)</td><td>2008/09/08 (222)</td></tr><tr><td>T/P interleaved+Jason-1 interleaved</td><td>2002/09/21 (369 of T/P)</td><td>2012/02/15 (372 of Jason-1)</td></tr></tbody></table>
<table><caption>**Table 4:** List of the 73 tidal constituents in the X-TRACK tidal constant product.</caption><thead><tr><th>Sa</th><th>Ssa</th><th>MSm</th><th>Mm</th><th>MSf</th></tr></thead><tbody><tr><td>Mf</td><td>MStm</td><td>Mtm</td><td>MSqm</td><td>Mqm</td></tr><tr><td>2Q1</td><td>Sig1</td><td>Q1</td><td>Ro1</td><td>O1</td></tr><tr><td>MP1</td><td>M1</td><td>Ki1</td><td>Pi1</td><td>P1</td></tr><tr><td>K1</td><td>Psi1</td><td>Phi1</td><td>Tta1</td><td>J1</td></tr><tr><td>SO1</td><td>OO1</td><td>KQ1</td><td>OQ2</td><td>MNS2</td></tr><tr><td>E2</td><td>2MK2</td><td>2N2</td><td>Mu2</td><td>N2</td></tr><tr><td>Nu2</td><td>MSK2</td><td>M(SK)2</td><td>M2</td><td>M(KS)2</td></tr><tr><td>MKS2</td><td>La2</td><td>L2</td><td>T2</td><td>S2</td></tr><tr><td>R2</td><td>K2</td><td>MSN2</td><td>KJ2</td><td>2SM2</td></tr><tr><td>2MK3</td><td>M3</td><td>SO3</td><td>MK3</td><td>S3</td></tr><tr><td>SK3</td><td>N4</td><td>3MS4</td><td>MN4</td><td>M4</td></tr><tr><td>SN4</td><td>MS4</td><td>MK4</td><td>S4</td><td>SK4</td></tr><tr><td>2MN6</td><td>M6</td><td>MSN6</td><td>2MS6</td><td>2MK6</td></tr><tr><td>2SM6</td><td>MSK6</td><td>3MS8</td><td></td><td></td></tr></tbody></table>

<table><caption>**Table 5:** Root mean square error (RMSE) in cm obtained from CTOH tidal product and from FES2012, FES2014 and GOT4.7 models for three major tidal constituents in San Matias Gulf (Argentina).</caption><thead><tr><th rowspan="2">Tidal constituent</th><th colspan="4">RMSE</th></tr><tr><th>CTOH</th><th>FES2012</th><th>FES2014</th><th>GOT4.7</th></tr></thead><tbody><tr><td>M2</td><td>5.48</td><td>10.36</td><td>10.36</td><td>30.35</td></tr><tr><td>N2</td><td>1.95</td><td>3.48</td><td>3.48</td><td>8.26</td></tr><tr><td>S2</td><td>2.35</td><td>5.07</td><td>5.07</td><td>7.39</td></tr></tbody></table>
**Figure captions:**

**Figure 1:** Example of the ionospheric correction for the cycle 10 of Jason-1 along the ground track 222 in the Mediterranean Sea, before (yellow circles) and after (red line) X-TRACK edition and filtering. Red circles indicate the flagged values. The dash lines correspond to the MAD filter thresholds.

**Figure 2:** Example of the wet tropospheric correction for the cycle 8 of Jason-1 along the ground track 222 in the Mediterranean Sea, before (yellow circles) and after (red line) X-TRACK edition and filtering. Red circles indicate the flagged values.

**Figure 3.** a) Number of cycles available in the Mediterranean Sea from T/P and Jason1&2 missions over the period 1993-2013 in AVISO (in green), X-TRACK 2011 (in red) and X-TRACK 2016 (in black) products. b) SLA standard deviation (in meters) from T/P and Jason1&2 missions over the period 1993-2013 for AVISO (in green), X-TRACK 2011 (in red) and X-TRACK 2016 (in black). The results are represented as a function of the distance to the coast. The thick curves indicate the mean behaviour of the three samples of SLA data.

**Figure 4:** Normalized Taylor diagrams of tide gauge stations listed in Table 2. The selected X-TRACK 2011 (red) and X-TRACK 2016 (black) data used are the 8 nearest points to the tide gauge location.
**Figure 5:** a) Amplitude and b) phase of M2 from the X-TRACK tidal constant product – example of the southwest Atlantic region. c) Vector differences between FES2012 global tidal model and the X-TRACK tidal constants for M2. The background color map shows the amplitude of the M2 tidal component from the model (in meters). The green dots represent the vector difference: the bigger the dot, the larger the difference.

**Figure 6:** Top) The 11 most important tidal constituents in San Matias Gulf derived from a tide gauge record, altimetry (CTOH) and three global tidal models (FES2004, FES2012 and GOT4.7). Bottom, left) location of the tide gauge station (green dot) and of the two closest T/P-Jason tracks. Bottom, right) Root-mean-square-error (RMSE) and Root-Sum-Square (RSS, defined as the squared sum of the RMSE of the 5 major constituents) in amplitude computed at San Matias Gulf tide gauge station for different tidal constituents released from altimetry and from the three global tidal models.

**Figure 7:** Monthly climatology of the surface geostrophic current anomalies derived from altimetry data in the Bay of Biscay for November (left) and July (right). Top: X-TRACK 2011; Bottom: X-TRACK 2016.

**Figure 8:** (a) and (c): Relative changes in SLA variability (rms) in percentages along coastal sections of altimetry tracks offshore Western Africa for (a) the X-TRACK 2016 and (c) the AVISO alongtrack datasets. Only the sections of altimetry tracks located less than 200 km off the African coast are studied. Changes are relative to the open ocean variability defined here as the variability averaged over the sections of altimetry tracks located from 160 to 200 km offshore the African coast.
(b) and (d): Relative changes in SLA variability (in %) were averaged as a function of the distance to the coast, using 5 km wide bins (blue line, left axis) for (b) the X-TRACK and (d) the AVISO alongtrack datasets. The grey envelope shows plus and minus one standard deviation (σ) from the average. The number of altimetric points used to compute the mean and standard deviation of SLA changes for each bin is shown in red (right axis). As in (a) and (c), changes are relative to the variability over the 160 to 200 km off the coast band (this reference part is shown in light grey shading). Results are based on the Topex/Poseidon, Jason-1 and Jason-2 missions over 1993-2012.

**Figure 9:** Vector differences (in cm) between the most recent global tidal models and the X-TRACK tidal product, for the M2 main tidal constituent, for each region where the X-TRACK data are available.

**Figure 10:** Vector differences between global tidal models (FES2004 (a), DTU10 (b) and FES2014 (c)) and the X-TRACK tidal constituent, for the M2 main tidal component. The background color map shows the amplitude of the M2 tidal component from the model (in cm). The red dots represent the vector difference: the bigger the dot, the larger the difference.
<figure><img src="image_3.png" /><figcaption>Figure 1</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 2</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 3</figcaption></figure>
<figure><img src="image_6.png" /></figure>

Esperance tide gauge
Track: 64

<figure><img src="image_7.png" /></figure>

Valparaiso tide gauge
Track: 230

<figure><img src="image_8.png" /></figure>

Santander tide gauge
Track: 213

<figure><img src="image_9.png" /></figure>

Ko-Taphao tide gauge
Track: 166

<figure><img src="image_10.png" /></figure>

Nice tide gauge
Track: 9

<figure><img src="image_11.png" /></figure>

Sao lome tide gauge
Track: 33

<figure><img src="image_12.png" /><figcaption>Figure 4</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Figure 5</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>Figure 6</figcaption></figure>
November

<figure><img src="image_15.png" /></figure>

July

<figure><img src="image_16.png" /></figure>

X-TRACK 2011

November

<figure><img src="image_17.png" /></figure>

July

<figure><img src="image_18.png" /></figure>

X-TRACK 2016
<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /><figcaption>Figure 8</figcaption></figure>
<figure><img src="image_23.png" /><figcaption>Figure 9</figcaption></figure>
<figure><img src="image_24.png" /><figcaption>Figure 10</figcaption></figure>