Removal of Covariant Errors from Altimetric Wave Height Data

Quartly, Graham D.

doi:10.3390/rs11192319

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…The proposed method is free of systematic artifacts, preserves the amplitude of spatial gradients and extreme values, and eliminate the noise over the whole frequency range, which is not the case for the boxcar or low-pass filters currently applied to altimetry products distributed to users. Indeed, such filters only remove some of the high-frequency noise, which is also the result of approaches that infer a correction to eliminate correlated errors from other aspects of the waveform data (Zaron and de Carvalho, 2015;Quartly, 2019, Tran et al, 2019, leaving a substantial amount of low-frequency noise in the data. Low-frequency noise remaining in the signals after smoothing will still interfere with the precise measurement of geophysical peaks height, width, and position (O'Haver, 2019).…”

Section: -Summarymentioning

confidence: 99%

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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Section: -Summarymentioning

confidence: 99%

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

Quilfen

Chapron

2021

Advances in Space Research

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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%

“…To address these limitations, new retracking techniques have been developed, which generally involve one or more of the following features: numerical solution of the radar equation (as opposed to using an analytical model), fitting of a selected portion of the waveform (Passaro et al, 2014;Thibaut et al, 2017;Peng and Deng, 2018), simultaneous multi-waveform processing (Roscher et al, 2017), and post-processing aimed at reducing correlated errors among consecutive estimations (Quilfen and Chapron, 2020;Quartly et al, 2019;Quartly, 2019). On top of this, several flavours exist of an analytical model to describe the viewing geometry of the DDA acquisitions (Moreau et al, 2018;Buchhaupt et al, 2018;Ray et al, 2015).…”

Section: Assessment and Implementation Of New Retracking Algorithmsmentioning

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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“…All of the above have individually treated each waveform; however it may be expected that successive estimates, based on largely overlapping instrument footprints, should have little change between them. Sandwell and Smith [23] had shown there is significant cross-talk between the errors in range and SWH, as both are derived from the leading edge; this can be quantified and utilised to reduce the noise in SWH [24]. This "high-frequency adjustment" was applied to output from the WHALES, WHALES_PTR and adaptive retrackers.…”

Section: Altimeter Datamentioning

confidence: 99%

Sensitivity of Altimeter Wave Height Assessment to Data Selection

Quartly¹,

Kurekin²

2020

Remote Sensing

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This paper addresses the issue of how the selection of buoys and the calculation of altimeter averages affect the metrics characterising the errors of altimetric wave height estimates. The use of a 51-point median reduces the sensitivity to occasional outliers, but the quality of this measure can be improved by demanding that there is a minimum number of valid measurements. This had a marked impact in both the open ocean and the coastal zone. It also affected the relative ordering of algorithms’ performances, as some fared poorly when a representative value was gleaned from a single waveform inversion, but had a much better ranking when a minimum of 20 values were used. Validation procedures could also be improved by choosing altimeter-buoy pairings that showed a good consistency. This paper demonstrated an innovative procedure using the median of the different retrackers analysed, which can be easily extended to other data validation exercises. This led to improved comparison statistics for all algorithms in the open ocean, with many showing errors less than 0.2 m, but there was only one strong change in the relative performance of the 11 Jason-3 retrackers. For Sentinel-3A, removing the inconsistent coastal buoys showed that all of the new algorithms had similar errors of just over 0.2 m. Thus, although improvements were found in the procedure used for the Sea State Round Robin exercise, the relative rankings for the buoy calibrations are mostly unaffected.

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Removal of Covariant Errors from Altimetric Wave Height Data

Cited by 10 publications

References 18 publications

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

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

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

Sensitivity of Altimeter Wave Height Assessment to Data Selection

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