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

Abstract

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.

Introducing the Azimuth Cutoff as an Independent Measure for Characterizing Sea-State Dynamics in SAR Altimetry

“…Explicit evidence of this is the primary error source in sea surface height, referred to as sea-state bias [3][4][5][6]. Several methods rely on parametric models expressing this bias as a function of wind speed and significant wave height [6][7][8][9][10][11][12][13][14]. Although significant improvements have been reported over the years, persisting errors still exist, typically a few percentages of the significant wave height, a magnitude considered substantial within the sea level error budget [15].…”

Applying Parametric Model Correction on Sea State Bias to Estimate Sea Surface Height over the Savu Sea

“…In recent decades, tide gauges have been the primary method used to acquire SSH. Satellite altimetry is the most widely used technology in the past three decades [2,3,4]. Exploring ocean circulation and observing the topograhy of the seabed, is particularly advantageous when using satellite altimetry [5].…”

“…Theoretically, sea surface height (SSH) can be calculated by deducting the height of the satellite's ellipsoid from the recorded instantaneous distance. However, altimeters are complex measurement systems that are subject to many errors [2]. The largest error is known as sea state bias (SSB).…”

“…Based on empirical estimates, this contribution is part of the overall SSB correction [8]. This is one of the largest sources of altimeter error and must be corrected [2,4,12,13]. Therefore, estimating the SSB becomes an important project to correct the SSH measurement [11].…”

“…The combination of parametric model and SSB was estimated by the BM4 empirical model [15], which expresses the relationship between SSB, SWH, and wind speed at 10 m (U10) height or radar cross section σ0 [16]. The BM4 model was chosen because from several studies conducted by Gaspar et al, [15], Guo et al, [2], and Guoshou et al, [4], it showed good results using the crossover differences method which resulted in an increase to the correlation coefficient between SSH without SSB correction and SSH using correction.…”