High-performance altimeter Doppler processing for measuring sea level height under varying sea state conditions

Moreau, Thomas; Cadier, Emeline; Boy, F.; Aublanc, J.; Rieu, Pierre; Raynal, Matthias; Labroue, S.; Thibaut, P.; Dibarboure, Gérald; Picot, Nicolas; Phalippou, L.; Demeestere, F.; Borde, Franck; Mavrocordatos, C.

doi:10.1016/j.asr.2020.12.038

Cited by 16 publications

(21 citation statements)

References 43 publications

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“…The average distance between the 20 Hz S3A data points is 0.34 km. The LR-RMC method is one of the most promising altimeter data processing solutions to deal with the inhomogeneous sea state issue while keeping the high precision of delay/Doppler measurements (Boy et al, 2017;Moreau et al, 2021).…”

Section: Datasets and Methodsmentioning

confidence: 99%

Sea Surface Height Variability in the 30–120 km Wavelength Band From Altimetry Along‐Track Observations

Chen

Qiu

2021

JGR Oceans

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Satellite altimetry sea surface height (SSH) observations have greatly advanced studies on ocean dynamics from global to mesoscale scales (see Fu et al., 2019 for a comprehensive review). The global multi-mission mapped SSH with a uniform longitude/latitude grid, known as the Archiving, Validation and Interpretation of Satellite Oceanographic Data (AVISO) product, is the most used SSH product. Due to the wide spacing between satellite ground tracks that requires optimal interpolation in time and space during the gridding process (Ducet et al., 2000), its spatial spectral resolution is ∼200 km in midlatitudes (Ballarotta et al., 2019;Chelton et al., 2011). The along-track SSH data, on the other hand, provides us with opportunities to explore the SSH variability with the spatial scales shorter than ∼200 km. Recently great effort has been put into processing and reprocessing all nadir altimetry missions to increase the quality of data through reducing errors and improving homogeneity, for example, with the continuously improving processing algorithms and calibration datasets, such as tide models, applied to all past and present missions (Morrow et al., 2018). In this study, we use the reprocessed along-track data to examine the SSH variability in the 30-120 km wavelength band. This wavelength band is selected because it includes the SSH signals that are missing in

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Section: Datasets and Methodsmentioning

confidence: 99%

Sea Surface Height Variability in the 30–120 km Wavelength Band From Altimetry Along‐Track Observations

Chen

Qiu

2021

JGR Oceans

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“…A reduction threefold larger (6%) in the variance of the filtered SLA with respect to the SLA without filtering is observed. This underscores the expected larger measurement noise in the unfiltered SLA from the Jason-3 Low Resolution Mode mission compared to the SAR mission [37,38]. As a result, a reduction of 2.3% in the RMSD was obtained when using filtered data.…”

Section: Impact Of the Measurement Noise On The Retrieval Of Sla In T...mentioning

confidence: 71%

Assessment of DUACS Sentinel-3A Altimetry Data in the Coastal Band of the European Seas: Comparison with Tide Gauge Measurements

et al. 2020

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The quality of the Data Unification and Altimeter Combination System (DUACS) Sentinel-3A altimeter data in the coastal area of the European seas is investigated through a comparison with in situ tide gauge measurements. The comparison was also conducted using altimetry data from Jason-3 for inter-comparison purposes. We found that Sentinel-3A improved the root mean square differences (RMSD) by 13% with respect to the Jason-3 mission. In addition, the variance in the differences between the two datasets was reduced by 25%. To explain the improved capture of Sea Level Anomaly by Sentinel-3A in the coastal band, the impact of the measurement noise on the synthetic aperture radar altimeter, the distance to the coast, and Long Wave Error correction applied on altimetry data were checked. The results confirmed that the synthetic aperture radar altimeter instrument onboard the Sentinel-3A mission better solves the signal in the coastal band. Moreover, the Long Wave Error processing contributes to reduce the errors in altimetry, enhancing the consistency between the altimeter and in situ datasets.

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“…For outliers associated with sigma-0 blooms and rain events, there is no simple relation between the noise associated with the hump artifact and sigma-0 that would allow for such a practical rule. EMD denoising of real SLA measurements is therefore likely to still be contaminated by outliers in limited portions of a number of along-track data segments, and future improvements will depend on better data editing and implementation of the latest retracking algorithms (Passaro et al, 2014;Thibaut et al, 2017;Moreau et al, 2021).…”

Section: Emd Denoising Calibration By Psd Adjustment In the 30-100 Km...mentioning

confidence: 99%

“…This suggests the absence of seasonality, in agreement with the results of Rocha et al (2016), who used acoustic Doppler current profile (ADCP) measurements in the Drake Passage, and disagrees with Vergara et al (2019) and Chen and Qiu (2021), who used the standard approach applied to altimeter data. As mentioned, high sea conditions make it difficult to assess the results obtained by the different approaches, and better data editing and retracking algorithms (Passaro et al, 2014;Thibaut et al, 2017;Moreau et al, 2021) would improve the current analysis. The Agulhas region, number 5, which experiences mixed sea conditions, shows no seasonality, which is in agreement with the results of Chen and Qiu (2021).…”

Section: Spectral Slopes Seasonalitymentioning

confidence: 99%

Towards improved analysis of short mesoscale sea level signals from satellite altimetry

Quilfen

Piollé

Chapron

2022

Earth Syst. Sci. Data

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Abstract. Satellite altimeters routinely supply sea surface height (SSH) measurements, which are key observations for monitoring ocean dynamics. However, below a wavelength of about 70 km, along-track altimeter measurements are often characterized by a dramatic drop in signal-to-noise ratio (SNR), making it very challenging to fully exploit the available altimeter observations to precisely analyze small mesoscale variations in SSH. Although various approaches have been proposed and applied to identify and filter noise from measurements, no distinct methodology has emerged for systematic application in operational products. To best address this unresolved issue, the Copernicus Marine Environment Monitoring Service (CMEMS) actually provides simple band-pass filtered data to mitigate noise contamination of along-track SSH signals. More innovative and suitable noise filtering methods are thus left to users seeking to unveil small-scale altimeter signals. As demonstrated here, a fully data-driven approach is developed and applied successfully to provide robust estimates of noise-free sea level anomaly (SLA) signals (Quilfen, 2021). The method combines empirical mode decomposition (EMD), used to help analyze non-stationary and non-linear processes, and an adaptive noise filtering technique inspired by discrete wavelet transform (DWT) decompositions. It is found to best resolve the distribution of SLA variability in the 30–120 km mesoscale wavelength band. A practical uncertainty variable is attached to the denoised SLA estimates that accounts for errors related to the local SNR but also for uncertainties in the denoising process, which assumes that the SLA variability results in part from a stochastic process. For the available period, measurements from the Jason-3, Sentinel-3, and SARAL/AltiKa missions are processed and analyzed, and their energy spectral and seasonal distributions are characterized in the small mesoscale domain. In anticipation of the upcoming SWOT (Surface Water and Ocean Topography) mission data, the SASSA (Satellite Altimeter Short-scale Signals Analysis, https://doi.org/10.12770/1126742b-a5da-4fe2-b687-e64d585e138c, Quilfen and Piolle, 2021) data set of denoised SLA measurements for three reference altimeter missions has already been shown to yield valuable opportunities to evaluate global small mesoscale kinetic energy distributions.

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High-performance altimeter Doppler processing for measuring sea level height under varying sea state conditions

Cited by 16 publications

References 43 publications

Sea Surface Height Variability in the 30–120 km Wavelength Band From Altimetry Along‐Track Observations

Sea Surface Height Variability in the 30–120 km Wavelength Band From Altimetry Along‐Track Observations

Assessment of DUACS Sentinel-3A Altimetry Data in the Coastal Band of the European Seas: Comparison with Tide Gauge Measurements

Towards improved analysis of short mesoscale sea level signals from satellite altimetry

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