Effect of High-Frequency Sea Waves on Wave Period Retrieval from Radar Altimeter and Buoy Data

Wang, Xifeng; Ichikawa, Kaoru

doi:10.3390/rs8090764

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…It is still unclear to what extent SSB depends on errors in parameter estimations or to the wave physics. The wave periods measured by altimeters are generally shifted from that measured by surface buoys due to different cut-off frequencies [25], which could result in a discrepancy between wave models such as WAVEWATCH III and ERA-Interim. The inaccuracy of numerical wave model outputs and wind fields may cause inaccurate theoretical modelling of SSB, especially in tropical regions.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, poor SWH estimation in TOPEX retracking due to poor downlinked waveform data, and instrumental differences (e.g., footprint sizes) between TOPEX and Jason altimeters could be responsible for the SSB differences in the missions. To correct for the physical effects of the interaction between the radar signal and the waves, it is necessary to work on a retracked dataset that is free from the retracker-related SSB noise [25].…”

Section: Discussionmentioning

confidence: 99%

“…Considering the effects of SSB on GMSL trend [7], we further analyzed the spatial and temporal variability of state-of-the-art SSB models used in various altimetry missions, to show its effects on global and regional sea level trend estimation, and compared with results from several recent studies on the GMSL trend drift in TOPEX (1993-2002) missions (phases A and B) [25][26][27]. This work improved our understanding on the determination of GMSL trend in TOPEX and Jason-2 era.…”

Section: Introductionmentioning

confidence: 99%

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Sea State Bias Variability in Satellite Altimetry Data

Cheng

Gao

et al. 2019

Remote Sensing

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Sea State Bias (SSB) contributes to global mean sea level variability and it needs cm-level range adjustment due to the instrumental drift over time. To investigate its variations and correct the global and regional sea level trend precisely, we calculate the temporal and spatial variability of the SSB correction in TOPEX, Jason-1, Jason-2 and Jason-3 missions, separately, as well as in the combined missions over the period 1993–2017. The long-term trend in global mean operational 2D non-parametric SSB correction is about −0.03 ± 0.03 mm/yr, which accounts for 1% of current global mean sea level change rate during 1993–2016. This correction contributes to sea level change rates of −1.27 ± 0.21 mm/yr and −0.26 ± 0.13 mm/yr in TOPEX-A and Jason-2 missions, respectively. The global mean SSB varies up to 7–10 mm during the very strong ENSO events in 1997–1998 and 2015–2016. Furthermore, the TOPEX SSB trend, which is consistent with recently reported sea level trend drift during 1993–1998, may leak into the determined global sea level trend in the period. Moreover, the Jason-1/2 zonal SSB variability is highly correlated with the significant wave height (SWH). On zonal average, SSB correction causes about 1% uncertainty in mean sea level trend. At high SWH regions, the uncertainties grow to 2–4% near the 50°N and 60°S bands. This should be considered in the study of regional sea level variability.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sea State Bias Variability in Satellite Altimetry Data

Cheng

Gao

et al. 2019

Remote Sensing

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“…Sea states are often characterized by their statistical properties, where three of the most commonly used properties are significant wave height (H s ), mean wave period (T 1 ), and mean wave heading [20]. Significant wave height represents the average height of the tallest one-third of wave heights, while the mean wave period is the average period, and mean wave heading is the average heading, of all waves measured.…”

Section: Wave Modelingmentioning

confidence: 99%

Response Component Analysis for Sea State Estimation Using Artificial Neural Networks and Vessel Response Spectral Data

Long¹,

Sgarioto²,

Garratt³

et al. 2022

Preprint

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The use of the 'ship as a wave buoy analogy' (SAWB) provides a novel means to estimate sea states, where relationships are established between causal wave properties and vessel motion response information. This study focuses on a model-free machine learning approach to SAWB-based sea state estimation (SSE), using neural networks (NNs) to map vessel response spectral data to statistical wave properties.Results showed a strong correlation between heave responses and significant wave height estimates, whilst the accuracy of mean wave period and wave heading predictions were observed to improve considerably when data from multiple vessel degrees of freedom (DOFs) was utilized. Overall, 3-DOF (heave, pitch and roll) NNs for SSE were shown to perform well when compared to existing SSE approaches that use similar simulation setups. Given the information-dense statistical representation of vessel motion responses in spectral form, as well as the ability of NNs to effectively model complex relationships between variables, the designed SSE method shows promise for future adaptation to mobile SSE systems using the SAWB approach.

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“…Barrick's method, which utilizes a weighted second-order energy integral, showed marginally higher accuracy than alternative approaches [52][53][54][55][56]. The comparison of wave heights (m) computed through Barrick's method and buoy-based data revealed that RMSE varies from 0.20 to 0.70 m, as indicated by the study of Wang and Ichikawa [57].…”

Section: Introductionmentioning

confidence: 97%

On the Indirect Estimation of Wind Wave Heights over the Southern Coasts of Caspian Sea: A Comparative Analysis

Lama

Sadeghifar

Azad

et al. 2022

Water

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The prediction of ocean waves is a highly challenging task in coastal and water engineering in general due to their very high randomness. In the present case study, an analysis of wind, sea flow features, and wave height in the southern coasts of the Caspian Sea, especially in the off-coast sea waters of Mazandaran Province in Northern Iran, was performed. Satellite altimetry-based significant wave heights associated with the period of observation in 2016 were validated based on those measured at a buoy station in the same year. The comparative analysis between them showed that satellite-based wave heights are highly correlated to buoy data, as testified by a high coefficient of correlation r (0.87), low Bias (0.063 m), and root-mean-squared error (0.071 m). It was possible to assess that the dominant wave direction in the study area was northwest. Considering the main factors affecting wind-induced waves, the atmospheric framework in the examined sea region with high pressure was identified as the main factor to be taken into account in the formation of waves. The outcomes of the present research provide an interesting methodological tool for obtaining and processing accurate wave height estimations in such an intricate flow playground as the southern coasts of the Caspian Sea.

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Effect of High-Frequency Sea Waves on Wave Period Retrieval from Radar Altimeter and Buoy Data

Cited by 8 publications

References 17 publications

Sea State Bias Variability in Satellite Altimetry Data

Sea State Bias Variability in Satellite Altimetry Data

Response Component Analysis for Sea State Estimation Using Artificial Neural Networks and Vessel Response Spectral Data

On the Indirect Estimation of Wind Wave Heights over the Southern Coasts of Caspian Sea: A Comparative Analysis

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