Using high resolution altimetry to observe mesoscale
and sub-mesoscale signals
M.-I. Pujol(1), F. Briol (1), G. Dibarboure (1), P.-Y. Le Traon(2)
(1) CLS, Toulouse, France
(2) IFREMER, Brest, France
1 - Introduction
During the last 15 years, multi-satellite altimetry data was largely shown to be able to
observe a significant fraction of sea surface height variability. Past altimeters constellations
ranged from one to four satellites. It allowed to better assess the performances of the global
altimetry observing system (multi-mission merged maps), and it underlined the limits of
spatial and temporal sampling for observing smaller scales and high frequency signals.
In order to better observe sea surface variability, new technologies and new altimeter
constellations are considered, and their sampling capability is assessed and compared to
historical scenarios. The focus is on high resolution altimetry: large swath altimetry (SWOT)
or large altimeter constellations (20+ altimeters). In this study, an OSSE baseline was used to
underline the observing capability of old and new altimeter systems to better sample
mesoscale and sub-mesoscale signal in a mapping (objective analysis) context.
This work is carried out as part of Ifremer Jason-2 and alti-ka/SARAL scientific
investigations supported by CNES (Ocean3D project).
2 - Data and method
2.1 - Model data:
50 days of Sea Surface Height (SSH) from two different models are used as reference data set.
Earth Simulator model (ES) :
It allows to simulate mesoscale and sub-mesoscale activity. Actually, a large part of the scales
in output of this model are < 100 km and high frequency variability (< 10 days) explains a
large part of the signal (> 30%). 12-hour model output are exploited on a regular grid with
0.017°x0.032° spatial resolution. The model area was arbitrary positioned in the Pacific
Ocean.
POP model (POP):
The model simulates the circulation in the North and tropical Atlantic. However, only the
Gulf Stream area was considered for this study. With a 3-day and 0.1°x0.1° temporal and
spatial resolution, the sub-mesoscale structures are absent in the model. Actually, typical
scales restored with POP are > 100 km.
2.2 - The Altimeters and the different constellations studied:
Three kind of altimeter were considered:
 The historical altimeters: Jason-2 (J2), Envisat (EN), Geosat Follow On (GFO) and
Jason-1 tandem (J1N). They were combined in three different constellations including
two to four satellites: J2+EN; J2+EN+GFO and J2+EN+GFO+J1N.
 The Iridium constellation: it consists in a 24-satellite (nadir) constellation. They are
distributed in 6 plans (4 satellites per plan) and the orbit is deriving.
 The SWOT mission: with a 22-day repetitivity, it measures the sea level height on a
wide swath (~140 km wide).
2.3 - The measurement noise:
An OSSE method was used to restore the sea level anomalies (SLA) with the different
constellations and using the two models, POP and ES, as reference data set.
Different residual noise were introduced on the along track data in order to be the closest as
possible to the reality.
 The measurement noise: it was fixed to 3 cm rms for the nadir measurement. This
corresponds to the noise measured on the historical altimeters in high variability areas
such as considered in this study. The measurement noise for the wide swath SWOT
was fixed to 0.45 cm rms, corresponding to a 1 cm.km-1 error balance.
 The orbit error noise: the residual error after correction was estimated to be 1 cm rms.
It was considered as long wavelength signal signing on the orbit and semi-orbit
lengths.
 The residual roll error: for the SWOT wide swath, the residual error after correction
was estimated to be 0.1 arcsec. This corresponds to a maximal variability of ~3.5 cm.
3 - Sampling effects
The altimeter sampling has a direct effect on SLA restoration. The more temporal and spatial
sampling are optimised, the best SLA is restored. For historical constellations, as observed
before by Pascual and al. (2008), when more satellites are merged, the results are more
precise (Fig. 1). It also clearly appears that for the latitudes considered (around 38°N), the
SWOT wide swath alone give similar results than the historical 4-altimeter constellation
(J2ENGFOJ1N). The error done on SLA restoration was estimated for the high variability
areas (Table 1). For these two constellations, it is less than 20% of the reference signal
variance for ES model, and around 3-4% for POP model. With an optimum sampling, the
Iridium constellation performances are greater with near 4% error on ES model and less than
0.5% for POP. These results must be modulated by the latitude considered since, especially
for SWOT, the inter-track space is bigger at other latitudes and thus performances are
degraded (Fig. 1).
Decomposition of the SLA signal showed that the different components are not restored with
the same precision (Table 1 and Fig. 2). The high-frequency signal (< 10 days) especially, is
badly restored with error on SLA signal ranging more than 90% for the J2EN constellation, to
30% (ES) and 23% (POP) for Iridium. On the other hand, low frequency signal (> 10 days) is
correctly restored. The error on this signal is maximum for J1EN merging (~25% for ES;
6.5% for POP) and minimal for Iridium (1.3% for ES; <0.1% for POP). With SWOT, it
reaches 12% and 2% respectively for ES and POP.
Goods results are also obtained on long wave signal (> 100 km). At the opposite, results on
short wavelength signal are degraded, especially for ES model. Actually, for this model, large
part of short wavelength signal is correlated with high frequency signal. The error reaches
more than 80% for the 2-satellite combination and 20% for the Iridium constellation. It is near
60% for SWOT, and 63% for the 4-satellite combination. For POP model, short wavelength
signal explain a low part of the total variability and the error on this signal are lower than
observed for ES. The minimum is observed with Iridium and reaches 3.5%.
Analysis of velocity signal shows bigger errors than observed with SLA, especially with ES
model (Table 1). Minimal error observed with Iridium reaches ~30% on ES. With this model,
short wave and high frequency signal mainly contribute to the error observed on velocity
fields. However, results obtained are also limited by the spatial resolution of the grid
produced (1/8°) with respect to the reference grid.
Error observed on POP are smaller and underline important differences between zonal and
meridional velocity errors. Theses differences traduce the different zonal and meridional
sampling of the signal. With a quasi meridional track, zonal velocities are better restored
whereas meridional velocities are impacted by local inter-track gaps.
Vorticity analysis clearly shows that historical conventional nadir measurement don’t allows
to correctly access to this signal (error > 100%). SWOT and Iridium constellation show lower
errors, especially for large scale structures like simulated by POP model.
4 - Noise measurement and mapping parameterisation effects
Taking into consideration the noise measurement underlines the importance of mapping
process parameterisation. Actually, error done on short wavelength structures is bigger when
taking into account the measurement noise (Fig. 2). If part of these errors is directly linked to
the noise signal, it can also traduce the bad parameterisation of objective analysis process.
Actually, part of the short wave signal could be corrected like measurement noise signal. It is
especially the case with ES model where short wave signal is important. Iridium results also
revels that negative impact of measurement noise on high frequency signal restoration.
J2EN
30.9 / 8.3
J2ENGFO
25.2 / 5.6
J2ENGFOJ1N
18.2 / 3.9
SWOT
18.1 / 3.1
IRIDIUM
3.6 / 0.4
SLA total
SLA low-frequency part
23.8 / 6.5
18.3 / 4.2
11.8 / 2.9
12.5 / 1.9
1.3 / 0.1
(> 10 days)
SLA high-frequency part
90.9 / 101.9
86.5 / 84.3
77.3 / 68.8
72.4 / 71..2
31.2 / 23.5
(< 10 days)
SLA long wavelength part
17.8 / 4.2
13.6 / 2.4
8.6 / 1.6
9.2 / 1.7
2.3 / 0.2
(> 100 km)
SLA short wavelength
82.3 / 45.5
73.3 / 36.2
62.6 / 28.0
58.0 / 17.7
20.0 / 3.5
part (< 100 km)
81.5 / 27.4
74.9 / 23.9
67.3 / 19.8
61.2 / 10.3
30.5 / 2.6
Zonal velocity
78.8 / 46.6
69.1 / 36.0
61.2 / 28.0
55.2 / 13.7
28.4 / 3.6
Meridional Velocity
101.2 / 128.4 100.4 / 115.7
98.0 / 99.0
93.1 / 40.8
80.4 / 17.2
Vorticity
Table 1: Error measured on SLA, U/V velocity and vorticity signal (in % of signal variance) for the
diffrent constellations and when the different noises on the signal were not considered. Results obtained
with ES/POP.
Figure 1: Mean formal mapping error (in % f the signal variance) observed for the ES model (centered
around 38°N) for the diffrent constellations studied.
Figure 2: Error on SLA signal (in % of signal variance) observed in high variability areas and when
considering the diffrents parts of the signal (high and low frequency with 10-day cut frequency; short and
long wavelength with 100 km cut wavelength).