Search Results

You are looking at 1 - 10 of 17 items for

  • Author or Editor: P. Y. Le Traon x
  • Refine by Access: All Content x
Clear All Modify Search
P. Y. Le Traon

Abstract

No abstract available

Full access
P. Y. Le Traon
and
G. Dibarboure

Abstract

The purpose of this paper is to quantify the contribution of merging multiple-satellite altimeter missions to the mesoscale mapping of sea level anomaly (H), and zonal (U) and meridional (V) geostrophic velocities. A space/time suboptimal interpolation method is used to estimate the mean and standard deviation of the H, U, and V mapping errors (as a percentage of signal variance) for different orbit configurations. Only existing or planned orbits [TOPEX/Poseidon (T/P), Jason-1, ERS-1/2–ENVISAT, Geosat–GFO] are analyzed. Jason-1 and T/P orbits are assumed to be interleaved. A large number of simulations are performed, including studies of sensitivity to a priori space scales and timescales, noise, and latitude. In all simulations, the Geosat orbit provides the best sea level and velocity mapping for the single-satellite case. In most simulations, the Jason-1–T/P orbit provides the best two-satellite mapping. However, the gain from an optimized two-satellite configuration (Jason-1 + T/P) compared to a nonoptimized configuration (T/P + ERS or T/P + Geosat) is small. There is a large improvement when going from one satellite to two satellites. Compared to T/P, the combination of T/P and ERS, for example, reduces the H mean mapping error by a factor of 4 and the standard deviation by a factor of 5. Compared to ERS or even Geosat, the reduction is smaller but still by a factor of more than 2. The H mapping improvement is not as significant when going from two to three or three to four satellites. Compared to the Geosat, ERS, and T/P mean mapping errors, the Jason-1 + T/P mean mapping error is, respectively, reduced by 5%, 9%, and 17% of the signal variance. The reduction in mean mapping error by going from two to three and from three to four satellites is, however, only 1.5% and 0.7% of the signal variance, respectively. These results differ from Greenslade et al. mainly because of the definition of resolution adopted in their study. The velocity field mapping is also more demanding in terms of sampling. The U and V mean mapping errors are two to four times larger than the H mapping error. Only a combination of three satellites can actually provide a velocity field mean mapping error below 10% of the signal variance. The mapping of V is also less accurate than the mapping of U but by only 10%–20%, even at low latitudes. These results are confirmed using model data from the Parallel Ocean Climate Model (POCM). POCM H, U, and V are thus very well reconstructed from along-track altimeter data when at least two satellites are used. The study also shows that the Jason-1–T/P orbit tandem scenario has to be optimized taking into account the other satellites (GFO and ENVISAT). It also confirms the usually agreed upon main requirement for future altimeter missions: at least two (and preferably three) missions (with one very precise long-term altimeter system to provide a reference for the other missions) are needed.

Full access
P. Y. Le Traon
and
G. Dibarboure

Abstract

A detailed analysis of the velocity field mapping capabilities from existing and future multiple altimeter missions is carried out using the Los Alamos North Atlantic high-resolution model. The velocity mapping errors on the instantaneous fields and on 10-day averaged fields are systematically computed for all analyzed configurations. The T/P+ERS (Jason-1+ENVISAT) mapping error on the velocity remains acceptable (20%–30%) relative to the ocean signal. Mapping errors of 10-day averaged fields are twice as small, which shows that this configuration has a good potential for mapping lower frequencies of the velocity field. Compared to T/P+ERS, T/P+Jason-1 has a smaller error by about 20%–30% mainly because it is less sensitive to the aliasing of high-frequency signals. The mapping errors are twice as small with a three interleaved Jason-1 configuration. One of the main findings of this study is the role of high-frequency signals that strongly limit the velocity mapping accuracy. The high-wavenumber high-frequency signals contribute to the total velocity variance by up to 20% in high eddy energy regions. This explains why the velocity mapping errors remain larger than about 15%–20% of the signal variance even for the four satellite configurations. This also explains why they do not decrease with the number of satellites as rapidly as expected. The aliasing of high-frequency signals is also a very serious issue. The high-frequency signals can induce large erroneous or inconsistent gradients between neighboring or crossing tracks. This strongly impacts the velocity estimation and explains why the meridional velocity mapping errors are larger than the zonal velocity mapping errors for the T/P+ERS configuration. However, it is shown that these aliasing problems can be partly reduced if they are properly taken into account in the mapping procedure.

Full access
J. Dorandeu
and
P. Y. Le Traon

Abstract

The authors used meteorological pressure fields from the European Centre for Medium-Range Weather Forecasts to calculate a mean global pressure to serve as a reference for an improved inverse barometer correction of altimeter data. These global pressure fields, available every 6 h on a ½ degree grid, enabled the extraction of the dominant mean pressure signals. Then, the effect of an improved inverse barometer correction on TOPEX/Poseidon mean sea level variation was estimated. Different low-pass smoothings of global mean pressure were used with cutoff frequencies ranging from (40 to 2 days)−1. Best results were obtained with the (2 days)−1 cutoff frequency, which was then used for an improved inverse barometer correction. The improved correction reduces the standard deviation of mean sea level variations (relative to an annual cycle and slope) by more than 20% when compared with standard inverse barometer correction and no correction at all. It also slightly reduces the variance of sea surface height differences at crossover points. The impact of the improved correction on the mean sea level annual cycle and slope is also not negligible.

Full access
P. Y. Le Traon
and
F. Hernandez

Abstract

This study aims to show that Lagrangian surface drifters are a suitable means of validating the mapping of oceanic mesoscale circulation by satellite altimetry. Tests are done using Geosat data to simulate drifter trajectories in the Azores-Madeira area. Multivariate objective analysis is then done to estimate the dynamic topography and its associated formal error using the velocity measurements obtained along drifter trajectories. This dynamic-topography field is compared with the reference field as given by Geosat data. Sensitivity to drifter number and energy level is studied. It is shown that with 25 drifters in a 500-km × 500-km area, the dynamic topography is obtained to within a formal accuracy of around 10%–20%. The difference between the estimated and reference fields is below 2 cm rms. These errors are smaller than the mapping errors induced by the space-time sampling of ERS-1 or TOPEX-POSEIDON satellites. According to these preliminary results, surface drifters are an efficient tool for validating mesoscale mapping by altimetry. More generally, the study shows that by comparing satellite altimetry with data from sufficient surface drifters, the differences between the signals can be estimated.

Full access
P. Y. Le Traon
,
P. Gaspar
, and
C. Boissier

Abstract

Among the various sources of error on altimetric sea surface height variability, the orbit error has the largest amplitude. However, since orbit error is mostly at long wavelengths, it can theoretically be distinguished from the mesoscale signal, characterized by wavelengths of a few hundred kilometers. The most commonly used technique to subtract this long-wavelength error is polynomial adjustment (zero, first or second degree) over distances of a few thousand kilometers. This paper examines the error on estimating the polynomial, which directly impacts the mesoscale signal obtained after the adjustment. We demonstrate how it can be estimated in theory and how it varies according to the spatial and energetic mesoscale characteristics (variability level, nonhomogeneities). These results are checked against simulated data and validated using actual Geosat data. The error is far from negligible: for a first-degree fit over 1500 km or a second-degree fit over 2500 km, its amplitude is typically 30% to 50% of the total mesoscale signal amplitude at the profile center and ends, respectively. In certain cases, where nonhomogeneity is significant, it can be greater than the total signal amplitude. We show that in such cases, a polynomial adjustment that takes amount of the statistics of mesoscale signal is a considerably better method. However, in the longer term, more global techniques such as inverse methods should be used so that the mesoscale signal can be extracted with the fewest possible errors.

Full access
F. Blanc
,
P. Y. Le Traon
, and
S. Houry

Abstract

The variable ocean dynamic topography is generally estimated from the satellite altimeter signal once the orbit error has been removed. To compute the orbit error, the most conventional technique is to fit a polynomial function (zeroth, first, or second degree) over lengths of several thousand kilometers to each altimetric profile. However, the method induces significant errors. To reduce them, one needs a more detailed representation of the orbit error spectrum and to take account of the spatial and temporal characteristics of the signal and noise. This can be achieved by the form of optimal analysis known as “inverse theory.” If a realistic statistical description of the altimeter signal components (i.e., oceanic variability and orbit error) is provided, the inverse formalism optimally separates the components. Although the whole set of altimeter data is reduced to the data at the intersections of ascending and descending ground tracks (crossover points), the method remains quasi-optimal.

The authors highlight the effectiveness of the method by applying it to the altimeter data for the Brazil-Malvinas confluence area, a few thousand kilometers wide. The authors compare the orbit error estimates to those of the most conventional method that is a method set to a similar environment (short-are analyses). With a homogeneous oceanic variability of 15 cm rms and a nominal orbit error of 30 cm rms, the error on the estimation is reduced to 2 cm all along the altimetric profiles. Taking into account the nonhomogeneous characteristics of the variability signal improves the estimation. It can he further improved simply by adding to the selected altimeter dataset the crossover points one orbital revolution away. For the Geosat satellite, they are at the same latitude but 25°25;prime; farther west or cast. The results encourage the use of the inverse method for orbit error reduction. The method is good at separating signals once the a priori parameters are well defined. Unlike polynomial fits, it does not remove other residual environmental terms.

Full access
P. Y. Le Traon
,
G. Dibarboure
, and
N. Ducet

Abstract

The contribution of merging multiple-satellite altimeter missions to the mapping of sea level is analyzed from a North Atlantic high-resolution (1/10°) numerical simulation. The model is known to represent the mesoscale variability quite well and offers a unique opportunity for assessing the mapping capability of multiple-altimeter missions. Several existing or planned orbits [TOPEX/Poseidon (T/P), Jason-1, ERS-1/2–ENVISAT, GEOSAT-GFO] are analyzed, and Jason-1 and T/P orbits are assumed to be interleaved. The model sea level anomaly fields are first subsampled along T/P, ERS, GFO, and Jason-1 tracks and a random noise of 3-cm rms is added to the simulated altimeter data. A suboptimal mapping method is then used to reconstruct the 2D sea level anomaly from alongtrack data and the reconstructed fields are compared with the reference model fields. Comparisons are performed in the North Atlantic and over a complete year. These results confirm the main conclusions of the Le Traon and Dibarboure study based on formal error analysis. There is, in particular, a large improvement in mapping capability when going from one to two satellites. Mapping errors (in percentage of the signal variance) are, however, larger than the ones derived from formal error analysis (by a factor between 1.5 and 2) and do not decrease as rapidly. This is mainly due to the high-frequency (periods < 20 days) and high-wavenumber signals of the Los Alamos model, which cannot be resolved with any of the analyzed multiple-satellite configurations.

Full access
J. Dorandeu
,
M. Ablain
, and
P-Y. Le Traon

Abstract

A new technique is developed and tested to correct for cross-track geoid gradients in altimeter data. The proposed method is based on direct estimations of geoid variations around nominal tracks and on knowledge of ocean signal variability. Apart from measurement errors, ocean variability is demonstrated to be the major source of error in cross-track geoid estimations using altimeter measurements. The method thus uses the outputs of multimission ocean signal mapping procedures to improve the estimation of geoid features. A detailed error analysis shows that such a technique allows reduction of the estimation error by a factor of 2. Therefore, the method is applied taking advantage of the unprecedented TOPEX/Poseidon mission length. It provides a gain of 50%, in terms of sea level anomaly (SLA) variance reduction, in the cross-track geoid gradient correction used in collocating the repeat-cycle data. It also improves the estimation of altimetric mean profiles. From this study, local mean sea surface estimates can be inferred and applied to present and future altimetric missions, since they can be easily updated using more data. New altimetric missions like Jason-1 and Envisat, with the same ground track as the former TOPEX/Poseidon and European Remote Sensing Satellite (ERS) missions, make the method even more relevant.

Full access
P. Y. Le Traon
,
F. Nadal
, and
N. Ducet

Abstract

Objective analysis of altimetric data (sea level anomaly) usually assumes that measurement errors are well represented by a white noise, though there are long-wavelength errors that are correlated over thousands of kilometers along the satellite tracks. These errors are typically 3 cm rms for TOPEX/Poseidon (T/P), which is not negligible in low-energy regions. Analyzing maps produced by conventional objective analysis thus reveals residual long-wavelength errors in the form of tracks on the maps. These errors induce sea level gradients perpendicular to the track and, therefore, high geostrophic velocities that can obscure ocean features. To overcome this problem, an improved objective analysis method that takes into account along-track correlated errors is developed. A specific data selection is used to allow an efficient correction of long-wavelength errors while estimating the oceanic signal. The influence of data selection is analyzed, and the method is first tested with simulated data. The method is then applied to real T/P and ERS-1 data in the Canary Basin (a region typical of low eddy energy regions), and the results are compared to those of a conventional objective analysis method. The correction for the along-track long-wavelength error has a very significant effect. For T/P and ERS-1 separately, the mapping difference between the two methods is about 2 cm rms (20% of the signal variance). The variance of the difference in zonal and meridional velocities is roughly 30% and 60%, respectively, of the velocity signal variance. The effect is larger when T/P and ERS-1 are combined. Correcting the long-wavelength error also considerably improves the consistency between the T/P and ERS-1 datasets. The variance of the difference (T/P–ERS-1) is reduced by a factor of 1.7 for the sea level, 1.6 for zonal velocities, and 2.3 for meridional velocities. The method is finally applied globally to T/P data. It is shown that it is tractable at the global scale and that it provides an improved mapping.

Full access