Assessing the effects of sea-state related errors on the precision of high-rate Jason-3 altimeter sea level data

Tran, N.; Vandemark, Doug; Zaron, Edward D.; Thibaut, P.; Dibarboure, Gérald; Picot, Nicolas

doi:10.1016/j.asr.2019.11.034

Cited by 24 publications

(24 citation statements)

References 31 publications

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“…In a preliminary study, Smith et al [12] examined the cross-spectra of sea surface height and SWH, noting that the magnitude squared coherence (equivalent to the correlation coefficient, r 2 ) was high (around 0.4) for all scales smaller than 50 km. These results have been corroborated more recently by Tran et al [13] in a detailed analysis of the MLE-4 retracker (4-parameter Maximum Likelihood Estimator) on Jason-3.…”

Section: Covariant Errors Of Properties Of Leading Edgesupporting

confidence: 66%

“…In this paper, we demonstrated the implementation of an "adjustment" to Hs using a constant value of Γ as an approximation to the variation shown in Figure 3a. In a similar analysis, which only focused on MLE-4 applied to Jason-3, Tran et al [13] modelled the variation as a simple linear function of Hs. With their adjustment, they achieved a reduction in σ Hs of 38% at Hs = 2 m, which is larger than the 22% achieved here (see Figure 6, for example).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Variability in Hs estimates is known to be greater in high wave height conditions, suggesting that the flagging of Hs data based on σ Hs should be done using a threshold that varies with Hs- Figure 6b shows this variation to be common to both MLE-4 and MLE-3 retrackers and the adjusted versions of them. (Large values of coherence between range and Hs have been reported at all scales below ∼50 km [12,13], so we have investigated calculating δζ relative to a 101-point running median filter, corresponding to ∼33 km along track. However, the results were not appreciably different.)…”

Section: Implementation Of Swh Adjustment and Its Evaluationmentioning

confidence: 99%

“…Flowchart describing the implementation. In this work, Γ for MLE-4 is −4.26 (with other values for other retrackers); alternatively, this could be a lookup table as a function of Hs, as chosen by Tran et al[13] and discussed in the Appendix of this paper.…”

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confidence: 99%

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Removal of Covariant Errors from Altimetric Wave Height Data

Quartly

2019

Remote Sensing

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The echo waveforms received by conventional radar altimeters are interpreted by retracking algorithms to give estimates of range, wave height, and backscatter strength. However, in response to fading noise on the waveform leading edge, common retrackers, such as MLE-3 and MLE-4, show correlated errors in wave height and range. This correlation is used to develop a correction to the wave height data that reduces the high-frequency variability by ∼22%, without affecting the global distribution of values. This correction also results in a closer matchup of Jason-2 and Jason-3 data during their tandem phase. Although the correction is quite straightforward in practice, the appropriate conversion factor has to be determined for each combination of altimeter and retracker. There are also remaining open questions concerning the needed low-pass filtering.

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Section: Covariant Errors Of Properties Of Leading Edgesupporting

confidence: 66%

Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Implementation Of Swh Adjustment and Its Evaluationmentioning

confidence: 99%

mentioning

confidence: 99%

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Removal of Covariant Errors from Altimetric Wave Height Data

Quartly

2019

Remote Sensing

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“…spectral ringing), and requires setting of a cut-off wavelength or filter window length that is difficult to determine adequately for a global data set. As for approaches that infer a correction to eliminate correlated errors from other aspects of the waveform data (Quartly, 2019;Tran et al, 2019), it also leaves a substantial amount of low-and medium-frequency noise in the data. To overcome these difficulties, an adaptive noise elimination method is used, based on the non-parametric Empirical Mode Decomposition (EMD) method developed to analyze non-stationary and non-linear signals (Huang et al, 1998).…”

Section: Data Denoisingmentioning

confidence: 99%

The Sea State CCI dataset v1: towards a Sea State Climate Data Record based on satellite observations

Dodet¹,

Piollé²,

Quilfen³

et al. 2020

Preprint

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Abstract. Sea state data are of major importance for climate studies, marine engineering, safety at sea, and coastal management. However, long-term sea state datasets are sparse and not always consistent, and sea state data users still mostly rely on numerical wave models for research and engineering applications. Facing the urgent need for a sea state Climate Data Record, the Global Climate Observing System has listed Sea State as an Essential Climate Variable (ECV), fostering the launch in 2018 of the Sea State Climate Change Initiative (CCI). The CCI is a program of the European Space Agency, whose objective is to realize the full potential of global Earth Observation archives established by ESA and its member states in order to contribute to the ECV database. This paper presents the implementation of the first release of the Sea State CCI dataset, the implementation and benefits of a high-level denoising method, its validation against in-situ measurements and numerical model outputs, and the future developments considered within the Sea State CCI project. The Sea State CCI dataset v1 is freely available on the ESA CCI website (http://cci.esa.int/data) at ftp://anon-ftp.ceda.ac.uk/neodc/esacci/sea_state/data/v1.1_release/. Three products are available: a multi-mission along-track L2P product (https://doi.org/10.5285/f91cd3ee7b6243d5b7d41b9beaf397e1, Piollé et al., 2020a), a daily merged multi mission along-track L3 product (https://doi.org/10.5285/3ef6a5a66e9947d39b356251909dc12b, Piollé et al., 2020b) and a multi-mission monthly gridded L4 product (https://doi.org/10.5285/47140d618dcc40309e1edbca7e773478, Piollé et al., 2020c).

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On denoising satellite altimeter measurements for high-resolution geophysical signal analysis

Quilfen

Chapron

2021

Advances in Space Research

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Satellite radar altimeter observations are key to advanced studies in ocean dynamics, particularly those focusing on mesoscale processes. To resolve scales below about 100 km, because altimeter measurements are often characterized by a low signal-to-noise ratio (SNR), low-pass filtering or leastsquares curve fitting is generally applied to smooth the data before analysis. Here, we present an alternative method. It is based on Empirical Mode Decomposition (EMD) developed to analyze nonstationary and non-linear processes, which adaptively projects a signal on a basis of empirical AM/FM functions called Intrinsic Modulation Functions (IMFs). Applied to a Gaussian noise signal, the EMD provides a set of IMFs with a predictable distribution of noise energy that can be exploited by waveletinspired threshold methods to provide an efficient approach to data denoising. The EMD method performs a local analysis of the SNR, does not require a priori assumptions about the underlying geophysical signal, e.g., its degree of smoothness or its compliance with a particular mathematical framework. The signal is simply assumed to be the sum of a piecewise-smooth deterministic part and a stochastic part. The proposed EMD-based denoising approach is therefore well suited for mapping non-linear features, such as strong gradients, and extreme values without significant smoothing. Using Jason-2, Cryosat-2, and Saral/AltiKa significant wave height measurements, the method provides an effective means of mapping overlooked geophysical variability of sea state at scales between about 100 km and 25 km, a range largely impacted by low SNR. Below 25 km, a spectral hump caused by inhomogeneities in the altimeter footprint essentially dominates the signal. In addition, the EMD method provides a consistent approach for longterm noise analysis and monitoring under global and local conditions. The proposed method is a step forward that will enable better exploitation of the unique set of altimeter observations that now covers more than 25 years.

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Assessing the effects of sea-state related errors on the precision of high-rate Jason-3 altimeter sea level data

Cited by 24 publications

References 31 publications

Removal of Covariant Errors from Altimetric Wave Height Data

Removal of Covariant Errors from Altimetric Wave Height Data

The Sea State CCI dataset v1: towards a Sea State Climate Data Record based on satellite observations

On denoising satellite altimeter measurements for high-resolution geophysical signal analysis

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