A new mean sea surface (MSS) from the Technical University of Denmark (DTU) called DTU21MSS for
referencing sea-level anomalies from satellite altimetry is introduced in
this paper, and a suite of evaluations are performed. One of the reasons for
updating the existing mean sea surface is the fact that during the last 6
years, nearly 3 times as many data have been made available by space
agencies, resulting in more than 15 years of altimetry from long-repeat
orbits (LROs) or geodetic missions. This includes the two interleaved long-repeat
cycles of Jason-2 with a systematic cross-track distance as low as 4 km.
A new processing chain with updated filtering and editing has been
implemented for the DTU21MSS. This way, the DTU21MSS has been computed from 2 Hz
altimetry in contrast to the former DTU15MSS and DTU18MSS which were computed
from 1 Hz altimetry. The new DTU21MSS is computed over the same 20-year
averaging time from 1 January 1993 to 31 December 2012 with a well-specified central
time of 1 January 2003 and is available from 10.11583/DTU.19383221.v1 (Andersen, 2022).
Cryosat-2 employs synthetic aperture radar (SAR) and SAR interferometric (SARin) modes in a large part of the Arctic Ocean
due to the presence of sea ice. For SAR- and SARin-mode data we applied the
SAMOSA+ physical retracking to make it compatible with the physical
retracker used for conventional low-resolution-mode data in other parts of
the ocean.
National Natural Science Foundation of China41804002Introduction
Satellite altimetry provides highly accurate measurements of the ocean
topography along the ground tracks of the satellite (Fu and Cazenave, 2001;
Stammer and Cazenave, 2017). For oceanography, the anomalous sea level about
a mean reference surface is of primary interest. During the last 2
decades, a mean sea surface (MSS) as a reference surface has been developed
with increasing accuracy (Pujol et al., 2017; Yuan et al., 2023).
Mean sea surface models are increasingly used as vertical offshore reference
surfaces for offshore operations (e.g., dredging, wind farms, bathymetry
surveys).
To develop a MSS it would be optimal if observations were available on all
temporal and spatial scales. The challenge is to derive an MSS given limited
sampling in both time and space using satellite observations. Another
challenge is to merge repeated observations along coarse ground tracks with
high spatial data from geodetic missions (GMs).
Thanks to new altimeter instruments and processing technology, the accuracy
of observed sea surface height (SSH) has increased dramatically over the
last decade. Sea-level anomalies (SLAs) are referenced to a global MSS. It is
consequently important that the MSS is as accurate as possible when
investigating smaller mesoscale features (e.g., Dufau et al., 2016).
The paper is structured in the following way. Section 2 presents the details
of the derivation of the new DTU21MSS from the Technical University of Denmark (DTU) with a focus on the improvement in
data, retracking, processing and filtering. The chapter is concluded with a
subsection on the potential use of synthetic aperture radar (SAR) altimetry from Sentinel 3A and 3B for the
DTU21MSS. Section 3 highlights various comparisons ranging from global
comparison to regional evaluations in the Arctic Ocean and for coastal
regions illustrating the improvement in the DTU21MSS model.
Computation of the DTU21MSS
The DTU21MSS is based on satellite altimetry data from frequently repeating
exact-repeat missions (ERMs) and infrequent missions with a long or drifting
repeat – called a geodetic mission (GM). The MSS is determined from a
sophisticated combination of the coarse ERM with the high-density GM data as
described in Andersen and Knudsen (1998, 2009; Andersen et al., 2010). ERM data are used to derive the coarse MSS. Subsequently, the GM data
are introduced to derive the fine-scale features in the MSS.
The long-wavelength MSS was derived using the highly accurate nearly
uninterrupted mean profiles derived using TOPEX (TP), Jason-1 (J1) and Jason-2 (J2). These data were taken
from the 1 Hz data from the Radar Altimetry Database System (RADS; Scharroo et
al., 2013). To extend the MSS into the polar regions outside the
66∘ parallel and to enhance the spectral resolution the
other mean profiles shown in Table 1 from other exact-repeat satellites
were fitted to the TOPEX, J1 and J2 profiles. The differences were found by
computing crossover differences between the ERM datasets. The crossover
residuals were expanded into spherical harmonic degrees and order 2 to 4, and
this surface was used to correct the ERM datasets. This methodology was
similarly applied to derive the DTU15MSS and DTU18MSS. Hence as a prior long-wavelength model, we used a filtered version of the DTU18MSS for wavelengths
greater than 100 km. For reference, the filtered versions of the DTU18MSS and
DTU15MSS are virtually identical inside the 66∘ parallel.
Before the MSS is computed, the averaging period, and consequently the center
time, for the MSS was selected. We used an averaging period from 1 January 1993
to 31 December 2012. Hence the center time for the DTU21MSS and previous DTU models
will be 1 January 2003.
There has been a significant focus on the accuracy of MSS models (Pujol et
al., 2017) in the preparation for the Surface Water and Ocean Topography
(SWOT) mission, launched recently. We consequently decided to keep the same
20-year averaging period for the DTU21MSS to be able to validate the MSS
directly with other MSS models. Changing the averaging period by as little
as 3 years will change the mean by 1 cm as well as the spatial pattern due
to ongoing sea-level change (Veng and Andersen, 2021).
Table 1 shows all altimetry used for the computation of the DTU21MSS and its
predecessors: the DTU15MSS and DTU18MSS. Whereas the DTU15MSS was based on
roughly 5 years of GM observations, the DTU21MSS is based on nearly 3
times as many data or more than 15 years of GM due to the recent focus on
prioritizing long-repeat orbits (LROs).
Satellite observations from the four newer GMs (Cryosat-2, Jason-1, Jason-2 and SARAL) have a range precision around 1.5 times higher than the
old ERS-1 and Geosat GM (Garcia et al., 2014). Consequently, it was decided
to retire the older ERS1 and Geosat GM data for the DTU21MSS.
Satellite altimetry used for the DTU15, DTU18 and DTU21MSS models.
SatelliteDTU15MSSDTU18MSSDTU21MSSERMTP+Jason-1+Jason-2Jan 1993–Dec 2012Jan 1993–Dec 2012Jan 1993–Dec 2012ERS2+ENVISATMay 1996–Oct 2011May 1996–Oct 2011May 1996–Oct 2011TP+Jason-1 interleavedSep 2002 to Oct 2005Sep 2002 to Oct 2005Sep 2002 to Oct 2005Feb 2009 to Mar 2012Feb 2009 to Mar 2012Feb 2009 to Mar 2012Geosat Follow-On (GFO)Jan 2001 Aug 2008Jan 2001 Aug 2008Jan 2001 Aug 2008GMERS1 (two interleaved cycles of 168 d)April 1994–May 1995April 1994–May 1995Not usedCryosat-2 (368.25 d repeatOct 2010–July 2014Oct 2010–July 2017Oct 2010–Oct 2019Jason-1 LRO (one cycle of 404 d)April 2012–Jun 2013April 2012–Jun 2013April 2012–Jun 2013Jason-2 LRO (two cycles of 371 d)Not usedNot usedAug 2017–Sept 2019SARAL/AltiKa (drifting phase)Not usedNot usedJuly 2016–Dec 2020
The following sections describe the theoretical and practical advances
leading up to the release of the DTU21MSS. The next section describes the
short-wavelength improvement and the subsequent section the improvement to
the long-wavelength part in the polar regions.
The short-wavelength MSS from geodetic mission altimetry
The short-wavelength part of the MSS is derived from geodetic mission
(GM) data. The Sensor Geophysical Data Record (SGDR) products for Jason-1
GM, Jason-2 GM and SARAL/AltiKa GM are obtained from the Archiving,
Validation and Interpretation of Satellite Oceanographic (AVISO) data
service. The L1b-level products for the CryoSat-2 low-resolution mode (LRM) are acquired through the
data distribution service of the European Space Agency (ESA). All these
products include along-track 20 Hz waveforms for all missions except for 40 Hz waveforms for SARAL/AltiKa.
All environmental and geophysical corrections of the altimeter range
measurements have been applied to calculating SSH (Andersen and Scharroo, 2011). These corrections include
dry and wet tropospheric path delay, ionospheric correction, ocean tide,
solid earth tide, pole tide, high-frequency wind effect, and inverted
barometer correction. The most recent FES2014 ocean tide model has been used
for all missions (Lyard et al., 2021). All corrections are provided to 1 Hz.
Hence, these were interpolated into 20 or 40 Hz using piecewise cubic
spline interpolation.
All satellites except for CryoSat-2 operate in the traditional LRM, where the along-track resolution is limited to 2–3 km. Cryosat-2 also operates in LRM over most of the oceans.
In regions where sea ice is prevailing, Cryosat-2 operates in synthetic
aperture radar (SAR) mode. In this mode, the returning echoes are processed
coherently, resulting in a footprint of 290 m. Over steeply varying
terrain and in some coastal regions, the SAR interferometric mode (SARin) is
used where the instrument receivers on two antennas are used. A mode mask
controls the availability of three Cryosat-2 data types (web1, 2023). The
advantage of SAR processing is an improvement of nearly 2 times the range precision (Raney, 2011). Due to the burst structures of Cryosat-2, the improvement
found is only around 1.5 times the range precision of LRM data (Raney,
2011; Garcia et al., 2014).
Waveform retracking is an effective strategy to improve the range precision
of altimeter echoes (Gommenginger et al., 2011). There are two strategies.
Empirical retrackers have the advantage of providing a valid and robust
estimation of arrival time used to determine the SSH over almost all types
of surfaces (e.g., sea ice leads, coastal). The disadvantage is that
empirical retrackers only provide SSH and not rise time, used to determine
significant wave height and wind speed. Hence it is not possible to determine
the sea state bias correction to the SSH observations (Fu and Cazenave,
2001).
Physical retrackers generally apply the Brown model for LRM data (Brown,
1977) or the SAMOSA model for SAR and SAR-in observations (Ray et al.,
2015). These retrackers estimate three or more parameters and enable corrections
and sea state conditions, through the determination of significant wave
height and wind speed. Hence these enable the determination of, and
subsequent correction for, sea state bias correction.
Two-pass retracking for range precision
Over the ocean, the waveforms from all four GM satellite missions are
well modeled and retracked using the Brown-type model. In the first step,
the waveforms are fitted by the three-parameter Brown model (arrival time,
rise time, and amplitude).
Maus et al. (1998) and Sandwell and Smith (2005) demonstrated the presence
of a strong coherence between the estimation errors in the arrival time and
rise time parameters, resulting in a relatively noisy estimate of arrival
time and hence sea surface height. Consequently, Sandwell and Smith (2005)
suggested the use of a second step where the rise time parameter is
smoothed. In the derivation of the DTU21MSS, we applied the same two-step
retracking and fixed the along-track smoothing at 40 km before retracking
the waveforms again using a two-parameter waveform model (fitting only
arrival time and amplitude).
For all four recent GM missions (Jason-1, Jason-2, SARAL/AltiKa and
CryoSat-2/LRM), this approach has been proved effective (Garcia et al., 2014;
Zhang and Sandwell, 2017; Andersen et al., 2021). Figure 1 illustrates the gain in range precision
using the two-pass retracking. The improvement for all four LRM datasets is
dependent on the SWH but is on average of the order of 1.5, similar to
other studies (Sandwell et al., 2014; Zhang et al., 2020).
The standard deviation of retracked height with respect to
the DTU15MSS for cycle 500 (corresponds to the first 11 d of the Jason-1 GM).
The upper figure illustrates the statistics for individual points. The lower
figure illustrates the median averaged over 0.5 m SWH intervals. Red:
height from sensor geophysical data record. Green: height from the first
step of two-pass retracking. Blue: height from the second step of the
two-pass retracking. Modified from Andersen et al. (2021).
Whereas two-pass retracking is very efficient for improving the range
precision for the LRM data, we did not apply the two-pass retracking for the
CryoSat-2 SAR- and SARin-mode data as there is no gain in range precision
from the second step of the retracking for SAR and SARin data. This was
first documented by Garcia et al. (2014).
2 Hz sea surface height data
The 20 or 40 Hz double-retracked SSH data are edited for outliers, and
subsequently, an along-track low-pass filter is applied before generating
the 2 Hz SSH data used for the subsequent MSS determination.
The along-track low-pass filter uses the Parks–McClellan algorithm which has
a cut beginning at 10 km wavelength and zero gain at 5 km; thus the filter
has 0.5 gain at 6.7 km, which is approximately the along-track resolution of
1 Hz data (Sandwell and Smith, 2009). The filter had to be designed for each
satellite mission to match the 0.5 gain at 6.7 km due to the different
along-track sampling rates. After this filter is applied the data were
downsampled to a 2 Hz sampling rate, which corresponds to an along-track
spacing of around 3.3 km.
For the previous DTU15MSS, we used 1 Hz SSH data from the Radar Altimetry
Database System (RADS; Scharroo et al., 2013). In RADS, the 1 Hz data are
computed as the average of all 20 Hz data, which is equivalent to using a
boxcar filter. The disadvantage of this filer is that spectral leakage in
the 10–40 km wavelength, which will remain as high-frequency noise in the
filtered dataset, contributes to the spectral hump of conventional LRM data
(Dibarboure et al., 2014; Garcia et al., 2014). The advantage of using the
Parks–McClellan algorithm over the boxcar filter is that this filter has
better spectral gain. The filter characteristics are illustrated in Fig. 2
for both filters.
Illustration of Parks–McClellan filter weights (blue) and the
boxcar filter (red) to derive the 1 or 2 Hz SSH data spatial filter (a). Panel (b) illustrates the frequency response of the two
filters. Sidelobes and spectral leakage in the 10–40 km wavelength can be
seen for the boxcar filter, which will remain as high-frequency noise in the
filtered dataset.
Long-wavelength polar-region MSS improvements
For the polar regions, we used the filtered version of the DTU15MSS as a prior
long-wavelength reference. The reason is that the DTU18MSS was based on
empirical retracked height in the polar regions. Frequently, physical and
empirical retrackers differ in their height estimation in polar regions
(Rose et al., 2019). The DTU15MSS was based on sparse physical retracked data
from RADS. However, it was found to be a more consistent prior choice for
the DTU21MSS where physical retracking is used.
Cryosat-2 provides observations all the way to 88∘ N. A closer inspection of
the Cryosat-2-mode mask (web1, 2022) shows that polar regions (outside the
66∘ parallels) are largely measured in the SAR and SARin
modes due to the presence of sea ice. This is with the exception of the
Barents Sea, north of Norway.
For SAR- and SARin-mode data, we applied the SAMOSA+ physical
retracking (Dinardo et al., 2018). SAMOSA+ adapts the SAMOSA retracking
model (Ray et al., 2015) to operate over specular scattering surfaces
as ice-covered polar oceans by involving the mean square slope as an additional
parameter in the retracking scheme and by implementing a more sophisticated
choice of the fitting initialization, resulting in greater robustness to
strong off-nadir returns from land. The SAMOSA+ retracker
even discriminates between return waveforms from diffusive and specular
scattering surfaces, ensuring the continuity in the sea-level retrieval
going from the open ocean and into the leads in the sea ice.
The DTU21MSS and DTU15MSS for the Southern Ocean (a) and the Arctic
Ocean (b). The color scale ranges up to ±5 cm for the Southern
Ocean and ±10 cm for the Arctic Ocean.
With the assistance of the European Space Agency (ESA) Grid Processing
On Demand (GPOD), we have processed a total of 9 years of Cryosat-2 (October 2010
to October 2019) for both the Arctic and the Southern Ocean using this SAMOSA+
retracker. Observations over the sea ice–open-ocean interface were removed
in the processing, and only observations over leads (ocean surface between
the ice floes) were selected similar to Rose et al. (2019).
Upon computing the mean profiles of Cryosat-2 observations, the center time
for the Cryosat-2 data was April 2015. It was found that it was necessary to
correct for sea-level rise to consolidate these data from the January 2003 center
period of the DTU21MSS following the methodology by Rio and Andersen (2009).
This was performed in the 65–66∘ border
zone as the reprocessing of Cryosat-2 with SAMOSA+ is limited to outside
the 65∘ parallels. This resulted in a correction of a few
centimeters.
The difference between the DTU21MSS and DTU15MSS is shown in Fig. 3 for
both the Southern Ocean and Arctic Ocean. For nearly all ice-covered regions, the
DTU15MSS is higher than the DTU21MSS. We expect this to be due to the fact
that the DTU15MSS was derived from 1 Hz RADS data, which was very sparse in both
time and space. The few data in RADS are a consequence of tight editing and
the fact that RADS converts the SAR data to pseudo LRM (Scharroo et al.,
2013) and performed physical retracking on these data using a modified Brown
model. In RADS, we nearly only found data during the ice-free summer month
when the annual signal causes the sea level to be higher, so it is
expected that the DTU15MSS could be biased high due to this.
Mean sea surface computation
The details of the computation technique of the DTU21MSS follow the
development of former DTU MSS models (Andersen and Knudsen, 2009), where the
ERM tracks are first used to compute the long-wavelength part of the MSS as
shown in Sect. 2.2. Hereafter the GM data are introduced to compute the
fine-scale structures of the MSS. The fine-scale computation is done in
small tiles of 1∘×3∘, with a 0.5∘
boundary to parallelize the computation process. As all
wavelengths longer than the size of the tiles are removed in this process
(roughly 200 km), we found that there was no need to adjust the period of
the GM data to the MSS averaging period (1993–2012).
The final step to close the polar gap is to fill in MSS proxy data north of
88∘ N where no altimetry is available. This was done by feathering the EGM08
geoid (Pavlis et al., 2012) across the pole in the following way: the
preliminary MSS was calculated up to 88∘ N using the satellite
altimetry data alone. Subsequently, the difference between the MSS and the
EGM08 geoid was computed longitude-wise in the 87–88∘ N region, and a mean offset was estimated and removed. The residual grid was
transformed into a regular grid in polar stereographic projection enabling
interpolation across the North Pole using a second-order Gauss–Markov
covariance function with a correlation length of 400 km. This makes the DTU
MSS models truly global.
The DTU21 mean sea surface from the Technical University of
Denmark (DTU) in meters.
The DTU21MSS as its predecessors are all given on a 1 min global
resolution grid. A closer examination of the MSS in Fig. 4 illustrates
that the height of the ocean's mean sea surface relative to the mathematical
best-fitting rotational symmetric reference system (GRS80) has magnitudes of
up to 100 m.
Sentinel 3A and 3B SAR altimetry
The European Space Agency (ESA) launched Sentinel 3A on the 16 February 2016 and Sentinel 3B on 25 April 2018. These satellites
operate as SAR altimeters everywhere with the benefit of increased range
precision compared with conventional LRM altimetry. Both the increased
along-track resolution and more importantly the improved cross-track
resolution of 35 km for the combined Sentinel 3A and 3B dataset would make these
important contributors to the DTU21MSS. However, two problems prevented the
use of these data for the time being.
The first relates to the fact that mean profiles could only be computed over
5 and 3 years from Sentinel 3A and B, respectively. As the Sentinel 3
satellites operate in a 27 d repeat orbit, this resulted in as few as 66 and 40
cycles, making these mean profiles considerably noisier compared with other
mean profiles. Secondly, the center times of Sentinel 3A and 3B are 2019 and 2020,
which means that the mean profiles are more than 15 years away from the
center time of the TOPEX, J1 and J2 mean profiles. We illustrate the problem in
Fig. 5 from a section of the Gulf Stream. The mean of S3A is 8 cm, but the
standard deviation of the spatial variation with respect to the DTU15MSS is
as high as 13 cm (Fig. 5 left panel). We show the mean profile from
Sentinel 3A along track 719 (located between the blue arrows in the left panel)
across the Gulf Stream going from south to north (right panel of Fig. 5).
Between 26 and 32∘ N, the difference corresponds
closely to the expected sea-level rise of a little more than 8 cm. However,
as the track crosses the Gulf Stream, the signal increases to nearly 60 cm.
The mean dynamic topography associated with the Gulf Stream causes the mean
sea level to drop by around a meter as one moves from the center of the
northwestern Atlantic toward the coast. Due to the north–south meandering of
the Gulf Stream, it creates the observed sea-level residual seen when the
averaging period changes (Zlotniki, 1991).
As Sentinel 3A and 3B are both outside the 1993–2012 averaging period and as
the meandering of the Gulf Stream is profound over the last 15 years, it was
not possible to ingest the S3A and B mean profiles without degrading the
DTU21MSS in this region.
There is no doubt about the importance of Sentinel 3A and 3B for future MSS
models, but to ingest Sentinel 3A and 3B in future MSS models, we found that
we will need to extend the averaging period to 30 years (1993–2022). We
consequently decided only to use the Sentinel 3A and 3B for the evaluation of the
various MSS models.
Sentinel 3A 5-year mean sea-level anomaly along track 791 in the Gulf
Stream area relative to the DTU15MSS (a). Sentinel 3A track 791 is
located between the blue arrows in (a). The S3A mean
anomalies relative to the DTU15MSS, CLS15MSS and DTU21MSS (b).
Evaluation
In this section, we perform three different evaluations of the MSS. These
evaluations supplement the global evaluation of previous MSS models
performed by Pujol et al. (2017) and serve the purpose of indicating the
improvements going from the DTU15MSS to the DTU21MSS globally, in the Arctic Ocean
and in coastal regions. The CLS15MSS is an improvement of the CLS11MSS
(Schaeffer et al., 2012) and is given on a similar 1/60∘
resolution with a similar averaging period to the DTU MSS models (Pujol et
al., 2017). Hence the various MSS models can be directly compared.
Global evaluation with mean profiles
In the global evaluation, we used data from the 1 Hz RADS data archive. The
global comparison in Table 2 illustrates the mean difference and the spatial
variation when the mean profiles are spline-interpolated onto the various
MSS models. The zero offset and small standard deviation for the TP, J1 and J2
mean profile are because all MSS models are fitted to this profile in its
derivation. The small offset for the other mean profiles corresponds to the
fact that the averaging of these profiles is not centered directly on
January 2003. The increased spatial standard deviation for other mean tracks is a
consequence of fewer repeat cycles available for these missions, fewer than
200 cycles versus 1000 repeat cycles for the TP, J1 and J2 mean profiles.
Comparison with mean profiles given as mean difference and standard
deviation (in parentheses) of spatial variations. All values are in centimeters.
The Sentinel 3A and 3B mean profiles are independent of existing MSS models,
but only 66 and 40 cycles have been used, respectively. In the comparison
with the Sentinel 3A and 3B mean profiles, we limited the comparison to within
the 65∘ parallels. For all comparisons, the number of repeat
cycles can be seen through increased standard deviation with decreasing
number of repeat cycles. This illustrates the effect of natural variability
of the sea surface and how this is gradually averaged out with an increasing
number of repeats. The roughly 5 cm mean difference between S3A and 3B mean
profiles and the MSS models directly illustrates the effect of global sea-level rise during the altimetric era. A measurement of 5 cm roughly corresponds to the
well-known 3 mm yr-1 sea-level rise accumulated between the center period of
January 2003 for the MSS and the averaging period of S3A and 3B some 15 years later.
All comparisons indicate that the DTU21MSS performs slightly superiorly
compared with all older models.
Arctic evaluation
Within the ESA Cryo-TEMPO project, we evaluated the impact of the use of a
physical retracker and an empirical retracker on the retrieval of sea-level
anomalies in the polar ocean. We used the state-of-the-art empirical
retracker called the Threshold First Maximum Retracker Algorithm (TFMRA)
(Helm et al., 2014) and the SAMOSA+ physical retracker. In
the evaluation, we also compared the state-of-the-art MSS models,
the DTU15MSS and DTU21MSS. It was not possible to include the CLS15MSS as
this model only covers up to 84∘ N and has several voids in
the Arctic Ocean (Pujol et al., 2017). The use of the physical retracker
allows us to estimate the sea state bias (SSB). This sea
state bias correction was subsequently applied to both the SAMOSA+
physical SLA and the empirical TFMRA SLA.
A total period of 7 months of Cryosat-2 was used between October 2013 and April 2014.
The results are shown in Fig. 6, where the upper panels show the spatial
variation in the mean (two left panels for the TFMRA and SAMOSA+ retracked
SLA) and the corresponding standard deviation of SLA (two right panels). The
lower panels highlight the time evolution of the monthly SLA anomalies
averaged with the monthly mean given in the left panel and the standard
deviation given in the right panel.
Comparison of retrackers and MSS models over the Arctic Ocean from
October 2013–April 2014. Upper panels: mean SLA using the empirical TFMRA
retracker and the DTU15MSS (a) and mean SLA using SAMOSA+ and the DTU21MSS (b). The standard deviation of SLA using the empirical TFMRA
retracker and the DTU15MSS (c) and standard deviation of SLA using
SAMOSA+ and the DTU21MSS (d).
Lower panels: evolution of SLA in time. The mean (e) and standard
deviation (f) are shown as monthly values. Heavy lines correspond to
using DTU21, and thin lines correspond to using DTU15. The dotted lines
correspond to using the TFMRA retracker and the solid lines to the SAMOSA+
retracker. The red lines have the sea state bias correction applied, whereas
the blue lines have not.
This study shows an improved measurement of SLA using the physical SAMOSA+
retracker, and in all cases, the DTU21MSS delivers better results than the
DTU15MSS. When using the physical SAMOSA+ retracker, we can see that
there is a clear effect of the ability to determine and correct for the sea
state bias (SSB). With SAMOSA+ sea state bias applied referenced to
the DTU21MSS, we obtain a mean SLA of -1.5cm±12cm instead of
-5.4cm±22cm over the October 2013–April 2014 period when using an empirical
retracker and the DTU15MSS.
To illustrate the difference between various MSS models across the Arctic
Ocean, we computed the difference between the DTU21MSS and the DTU15MSS and
CLS15MSS, respectively.
Sea-level anomalies (in m) between the 5-year S3A mean
profile along track 497–498 and various MSS models in the Arctic Ocean (a). (b) Mean sea surface difference between the DTU21MSS and the CLS15MSS
dark-blue regions north of Canada are voids in the CLS15MSS. The color scale
ranges from -15 to +15 cm.
To illustrate the differences between the various MSS models, we computed the
difference between a Sentinel 3A 5-year mean profile and the various MSS models.
Figure 7 shows this difference along the Sentinel 3A track 497–498. The
track transits from Russia at 68∘ N, 54∘ E,
passing to the east of Novaya Zemlya, and continues up to 82∘ N
(at 120∘ E). From here it descends towards the Aleutian
Trench at 57∘ N, 204∘ E. The standard
deviations with the S3A mean profiles are 6.1, 5.7 and 8.1 cm respectively for
the DTU15MSS, DTU21MSS and CLS15MSS. Data are missing around latitude
90∘ E because of the crossing of the Russian island of
Komsomolets. Data are missing around 120∘ E because of voids
in the CLS15MSS causing the S3A data to be removed by the space agencies. The
color scale ranges from -15 to +15 cm. The increase in the S3A residuals
around 190∘ E is associated with the transition of the Bering
Strait and the in- and outflow through the strait (Woodgate and Peralta-Ferriz,
2021).
Difference between the DTU21MSS and the DTU15MSS in the Baltic Sea (a). Standard deviation (m) relative to the Danish vertical
reference model, DVR90, as a function of distance to coast (b).
Coastal evaluation
The difference between the DTU21MSS and the DTU15MSS was evaluated in the
Baltic Sea as part of the Baltic+ SEAL project (http://balticseal.eu/, last access: 25 August 2023).
Differences are presented in Fig. 8 (left) panels and range up to 8 cm in the coastal zone and inside the narrow Danish Straits as well as the
Bay of Bothnia and the Swedish archipelago. In all locations, we found that
the former DTU15MSS is unreasonably high near the coastline. Around the
coast of Denmark, we further compared with the vertical reference frame
model of Denmark called DVR90 (Web2, 2023). DVR90 is fitted to 14 Global Navigation Satellite System (GNSS)
stations along the coastline of Denmark. The right panel illustrates
that the DTU21MSS has a lower standard deviation close to the coast compared
with the DTU15MSS, which independently verifies that the DTU21MSS is superior in
fitting mean sea level close to the coast.
Data availability
The DTU21MSS is available from 10.11583/DTU.19383221.v1 (Andersen, 2022). The high-resolution MSS
model is available in several formats and relative to various reference
ellipsoids (TOPEX and WGS84) 10.11583/DTU.19383221.v1 (Andersen, 2022).
Conclusions
A new mean sea surface (MSS) called DTU21MSS for referencing sea-level
anomalies from satellite altimetry has been presented, along with the first
evaluations. We have presented the updated processing chain with updated
editing and data filtering. The updated processing filters the double
retracked 20 Hz sea surface height data using the Parks–McClellan filter to
derive 2 Hz sea surface anomaly. This Parks–McClellan filter has a clear
advantage over the 1 Hz boxcar filter used for older DTU models in enhancing
the MSS in the 10–40 km wavelength band. Similarly, the use of the FES2014
ocean tide model improves the usage of sun-synchronous satellites in high
latitudes in the new MSS.
Cryosat-2 employs SAR and SARin modes in a large part of the Arctic Ocean
due to the presence of sea ice. For SAR- and SARin-mode data we applied the
SAMOSA+ physical retracking (Dinardo et al., 2018) to make it compatible
with the physical retracker used for conventional low-resolution-mode data
in other parts of the global ocean.
We initially performed global comparisons with the mean profile from various
available satellites using data from the RADS data archive as these have
only been used in the DTU15MSS and not any of the other MSS models. The
comparison with the independent 5- and 3-year S3A and S3B mean profiles show
a relatively clear improvement for the DTU21MSS. This was also expected as
the S3A and 3B satellites employs SAR altimetry and hence should compare better
with the MSS derived using the two-pass altimetry due to the enhanced
modeling of the 10–30 km wavelength (Garcia et al., 2013).
The evaluation in the Arctic Ocean indicates an improved measurement of SLA
using SAMOSA+ with the DTU21MSS. In conjunction with this physical
retracker, the correction of the sea state bias (SSB) further improves the
results. In all evaluations, the DTU21MSS delivers better results than the
DTU15MSS. With SAMOSA+, SSB and the DTU21MSS, we obtain a mean SLA of -1.5cm±12cm instead of -5.4cm±22cm over the October 2013–April 2014 period.
Coastal evaluation of the new DTU21MSS was performed in the Baltic Sea. The
evaluation in the Baltic Sea confirms that the DTU15MSS is frequently several centimeters
too high in the coastal zone. This was further demonstrated in an evaluation
with the Danish Vertical Reference model based on GNSS observations, where
the DTU21MSS showed superior comparison close to the coast.
For the DTU21MSS we found that the 5-year Sentinel 3A mean profiles
(May 2016–May 2020) were too problematic to consolidate onto the 1993–2012
averaging period without degrading the MSS model, particularly in large
current regions. Consequently we omitted these data in the DTU21MSS but
also found that we will need to extend the averaging period to 30 years
soon to enable the use of the important new Sentinel 3A and 3B data in the
next-generation MSS models.
Author contributions
OBA wrote the manuscript and performed the computation of the DTU21MSS. SZ
performed the two-pass retracking of all 20 Hz geodetic mission data. AA
developing the software for producing 2 Hz and performed the MSS
computations in coastal regions. SKR performed the data processing for SAR
and SARin data for the polar regions. SF contributed to the MSS validation
in the Arctic Ocean.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors are thankful to the space agencies for considering the geodetic
or long-repeat missions as part of mission operations and for providing
these high-quality data to the users. We would like to acknowledge ESA-RSS
(Research and Service Support)
for their assistance in processing the data with G-POD, now migrated to Earth Console (http://earthconsole.eu; last accessed: 25 August 2023).
Financial support
The project contributes to the Cryo-TEMPO and Baltic+ SEAL project and has been supported through the following contracts: AO/1-10244/20/I-NS and 4000126590/19/I-BG. Shengjun Zhang worked at DTU during 2020, supported by the
National Nature Science Foundation of China, grant no. 41804002; by the
State Scholarship Fund of China Scholarship Council, grant no. 201906085024; and by the Fundamental Research Funds for the Central Universities.
Review statement
This paper was edited by Giuseppe M.R. Manzella and reviewed by two anonymous referees.
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