Ocean Surface Wave‐Current Signatures From Satellite Altimeter Measurements

Quilfen, Yves; Chapron, Bertrand

doi:10.1029/2018gl081029

Cited by 53 publications

(50 citation statements)

References 29 publications

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“…This could have been applied either implicitly within the retracking algorithms as for the STARv2 algorithm (as discussed in Section 2) or explicitly, a-posteriori to the retracked L2 dataset. Examples for explicit techniques are the intra-1-Hz adjustment [35] or high-frequency adjustment (HFA) [36], and an empirical mode decomposition (EMD)-based technique [37].…”

Section: Overview Of Investigated Retracker Datasetsmentioning

confidence: 99%

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Round Robin Assessment of Radar Altimeter Low Resolution Mode and Delay-Doppler Retracking Algorithms for Significant Wave Height

et al. 2020

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Radar altimeters have been measuring ocean significant wave height for more than three decades, with their data used to record the severity of storms, the mixing of surface waters and the potential threats to offshore structures and low-lying land, and to improve operational wave forecasting. Understanding climate change and long-term planning for enhanced storm and flooding hazards are imposing more stringent requirements on the robustness, precision, and accuracy of the estimates than have hitherto been needed. Taking advantage of novel retracking algorithms, particularly developed for the coastal zone, the present work aims at establishing an objective baseline processing chain for wave height retrieval that can be adapted to all satellite missions. In order to determine the best performing retracking algorithm for both Low Resolution Mode and Delay-Doppler altimetry, an objective assessment is conducted in the framework of the European Space Agency Sea State Climate Change Initiative project. All algorithms process the same Level-1 input dataset covering a time-period of up to two years. As a reference for validation, an ERA5-based hindcast wave model as well as an in-situ buoy dataset from the Copernicus Marine Environment Monitoring Service In Situ Thematic Centre database are used. Five different metrics are evaluated: percentage and types of outliers, level of measurement noise, wave spectral variability, comparison against wave models, and comparison against in-situ data. The metrics are evaluated as a function of the distance to the nearest coast and the sea state. The results of the assessment show that all novel retracking algorithms perform better in the majority of the metrics than the baseline algorithms currently used for operational generation of the products. Nevertheless, the performance of the retrackers strongly differ depending on the coastal proximity and the sea state. Some retrackers show high correlations with the wave models and in-situ data but significantly under- or overestimate large-scale spectral variability. We propose a weighting scheme to select the most suitable retrackers for the Sea State Climate Change Initiative programme.

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Section: Overview Of Investigated Retracker Datasetsmentioning

confidence: 99%

“…The influence of wave-current interactions means that different wave spectra may be encountered in regions with pronounced current variability [Villas-Boas et al, in prep.]. The dashed lines indicate slopes in the 50-100 km range of k −2 and k −3 , as aids for comparison as been done in[37].…”

mentioning

confidence: 99%

Round Robin Assessment of Radar Altimeter Low Resolution Mode and Delay-Doppler Retracking Algorithms for Significant Wave Height

et al. 2020

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“…Small-scale currents with scales of 10-100 km have also been found to have a large impact on wave heights which encounter them (Ardhuin et al, 2017). This was recently overcome by Quilfen and Chapron (2019) by denoising the wave height measurements from altimeters, revealing overlooked statistical properties of small-scale effects of currents on waves. The Agulhas system itself has also recently been studied with respect to wave-current interactions.…”

Section: Journal Of Geophysical Research: Oceansmentioning

confidence: 99%

Toward Operational Wave‐Current Interactions Over the Agulhas Current System

Barnes

Rautenbach

2020

JGR Oceans

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A computationally inexpensive operational wave forecasting system taking wave‐current interactions over the fast‐flowing Agulhas Current into account is explored. Spectral wave model hindcasts at 1/16 of a geographical degree are employed using the Simulating Waves in the Nearshore (SWAN) wave model. Unidirectional wave‐current coupling, with currents affecting waves, is performed using both Operational Mercator 1/12 degree (ORCA12) and GLOBCURRENT 1/4 degree currents. Comparisons between the coupled and uncoupled simulations indicate that wave amplification or lowering is highly dependent on the current magnitude and wave incident angle. In opposing current scenarios, the significant wave height can increase by 20–40% and by 60% in extreme cases. Model validation is performed using Jason‐3 satellite altimetry measurements and in situ directional wave buoy measurements. The nearshore wave directional dispersion is found to be significantly influenced by the core of the Agulhas Current. This is especially prominent in current‐following cases with wave‐current direction differences of up to 20°. The findings are all corroborated by simplistic, idealized models.

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“…It therefore provides the basis for a noise elimination approach with results often superior to those of waveletbased techniques (Kopsinis and McLaughlin, 2009). Recently, EMD analysis has been successfully applied to altimeter data to analyze wave-current interactions known to predominate at scales below 100 km (Quilfen et al, 2018;Quilfen and Chapron, 2019). For reference the method is fully described in (Quilfen and Chapron, 2020).…”

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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Ocean Surface Wave‐Current Signatures From Satellite Altimeter Measurements

Cited by 53 publications

References 29 publications

Round Robin Assessment of Radar Altimeter Low Resolution Mode and Delay-Doppler Retracking Algorithms for Significant Wave Height

Round Robin Assessment of Radar Altimeter Low Resolution Mode and Delay-Doppler Retracking Algorithms for Significant Wave Height

Toward Operational Wave‐Current Interactions Over the Agulhas Current System

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

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