SDUST2020 MSS: A global 1′×1′ mean sea surface model determined from multi-satellite altimetry data

Yuan, Jiajia; Guo, Jinyun; Zhu, Chengcheng; Li, Zhen; Liu, Xin; Gao, Jinyao

doi:10.5194/essd-2022-178

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Data editing and quality control were carried out for 1-Hz GDR data, which were obtained from cycle 201 to cycle 300, from the Ku-band radar altimeter of Jason-2 and the wind speeds in the ERA5 reanalysis data. Next, the SSHs, SWHs and the wind speeds at crossover points without SSB correction were calculated by using the latitude difference method [41].…”

Section: Ssb Parameter Model For Jason-2 Altimetermentioning

confidence: 99%

On Modelling Sea State Bias of Jason-2 Altimeter Data Based on Significant Wave Heights and Wind Speeds

Guo

Zhang

et al. 2023

Remote Sensing

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Altimeter data processing is very important to improve the quality of sea surface height (SSH) measurements. Sea state bias (SSB) correction is a relatively uncertain error correction due to the lack of a clear theoretical model. At present, the commonly used methods for SSB correction are polynomial models (parametric models) and non-parametric models. The non-parametric model usually was constructed by collinear data. However, the amount of collinear data was enormous, and it contained redundant information. In this study, the non-parametric regression estimation model was optimized by using the parameter replacement method of ascending and descending tracks based on the crossover data. In this method, significant wave heights from the Jason-2 altimeter data during cycles 200–301 and wind speed from the ERA5 reanalysis data were used. The non-parametric regression estimation model of Jason-2 was constructed by combining it with local linear regression, Epanechnikov kernel function and local window width. At the same time, based on the significant wave height and wind speed at the crossover points, the SSB polynomial model containing six parameters was constructed by using the Taylor series expansion, and the model was optimized. By comparing polynomial model construction with different parameters, the optimized model was obtained. The SSH of the crossover points and the tide gauge records were used to validate these results derived from two models and GDR. Compared with the crossover discrepancies of SSH corrected by the polynomial model, the RMS of the crossover discrepancies of SSH corrected by the non-parametric regression estimation model was reduced by 7.9%. Compared with the crossover discrepancies of SSH corrected by the conventional non-parametric model from GDR, the RMS of the crossover discrepancies of SSH corrected by the non-parametric regression estimation model was reduced by 4.1%. This shows that the precision of the SSHs derived by after the SSB correction, as calculated by the non-parametric regression estimation model, was better than that of the polynomial model and the SSB correction from GDR. Using the Jason-2 altimeter data, the along-track geoid gradient and the sea level change rate of the global ocean were determined by using two models to correct the SSB. By comparing the results of the two models, the accuracy of the geoid gradient along the orbit that was obtained by the non-parametric regression estimation model was better than that of the polynomial model and GDR. The global average sea level change rate after the non-parametric regression estimation model correction was 3.47 ± 0.09 mm/y, which was the closest to the average sea level change rate that has been published in the international literature within this field.

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Section: Ssb Parameter Model For Jason-2 Altimetermentioning

confidence: 99%

On Modelling Sea State Bias of Jason-2 Altimeter Data Based on Significant Wave Heights and Wind Speeds

Guo

Zhang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

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SDUST2020 MSS: a global 1′ × 1′ mean sea surface model determined from multi-satellite altimetry data

Yuan

Guo²,

Zhu³

et al. 2023

Earth Syst. Sci. Data

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Abstract. This study focuses on the determination and validation of a new global mean sea surface (MSS) model, named the Shandong University of Science and Technology 2020 (SDUST2020), with a grid size of 1′×1′. This new model was established with a 19-year moving average method and fused multi-satellite altimetry data over a 27-year period (from January 1993 to December 2019). The data of HaiYang-2A, Jason-3, and Sentinel-3A were first ingested in the SDUST2020 MSS but not in any other global MSS model, such as the CLS15 and DTU18 MSS models. Validations, including comparisons with the CLS15 and DTU18 MSS models, GPS-leveled tide gauges, and altimeter data, were performed to evaluate the quality of the SDUST2020 MSS model, all of which showed that the SDUST2020 MSS model is accurate and reliable. The SDUST2020 MSS dataset is freely available at the site (data DOI: https://doi.org/10.5281/zenodo.6555990, Yuan et al., 2022).

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SDUST2020 MSS: A global 1′×1′ mean sea surface model determined from multi-satellite altimetry data

Cited by 2 publications

References 15 publications

On Modelling Sea State Bias of Jason-2 Altimeter Data Based on Significant Wave Heights and Wind Speeds

On Modelling Sea State Bias of Jason-2 Altimeter Data Based on Significant Wave Heights and Wind Speeds

SDUST2020 MSS: a global 1′ × 1′ mean sea surface model determined from multi-satellite altimetry data

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