Abstract's details
FastAdaptiveS6 : an optimal retracking solution for the analysis of Sentinel-6 LRM data
CoAuthors
Event: 2023 Ocean Surface Topography Science Team Meeting
Session: Instrument Processing: Measurement and Retracking
Presentation type: Type Oral
Contribution: PDF file
Abstract:
The optimal and efficient estimation of the geophysical parameters is one of the key goals of the retracking analysis of radar altimetry data. Major efforts have been put in the development of new retracking solutions towards this goal, leading to a continuous improvement of the existing retracking algorithms. These efforts are indeed crucial for an accurate estimation of Essential Climate Variables as the mean sea level, which is a key issue for the robust assessment of climate change at local and global scales.
To ensure the continuity and the robustness of the parameter estimation, a retracking algorithm should account for a realistic waveform noise characterisation and for the real time evolution of instrumental properties, as the Point Target Response (PTR), as this could impact the parameter estimation leading to biases that are difficult to correct and sub-optimality (increase of the parameters error bars). These aspects become more critical with the high demanding requirements in terms of data quality and resolution as of the current reference mission Sentinel-6 and upcoming missions.
Because the baseline solution currently implemented in the ground segments of many missions, the MLE4 retracker, does not meet these goals, a new solution, the numerical retracker (Buchhaupt et al 2018, Dinardo et al. 2023), has been developed and recently implemented in the Sentinel-6 ground segment. Yet, while accounting for the real PTR, this solution does not include a realistic waveform noise and therefore it is not optimal. An accurate noise characterisation is particularly important for S6 as the pulse-to-pulse correlations linked to the S6 PRF configuration leads to a variation of the Effective Number of Looks (ENL) with respect to both the range gate and the SWH which may impact the parameter estimation if not correctly accounted for.
In this talk we present the FastAdaptiveS6, an optimal and efficient retracker for the analysis of S6 LRM data. The retracking is based on the FastAdaptive solution (Mangilli et al OSTST 2022) which includes the Adaptive model with numerical PTR convolution (Tourain et al 2021, Thibaut et al. OSTST 2017 & 2021) and a realistic waveform (multiplicative) noise characterisation that allows for an optimal and unbiased parameter estimation. In the talk we will present the formalism of the FastAdaptiveS6, the validation on realistic Sentinel-6 simulations and the results on S6 data. The waveform simulations are generated with a multiplicative noise with Gamma distribution and S6-like ENL variations to evaluate the impact of the noise properties on the parameters estimation. We will show the results obtained on representative S6 datasets over ocean, also in comparison with the baseline results of S6 products. We will demonstrate that the FastAdaptiveS6 is unbiased and optimal and allows for a significant improvement of the parameter estimation, in particular the SWH and range, with respect to existing retracking algorithms, making it a suitable solution for the analysis of S6 LRM data.
To ensure the continuity and the robustness of the parameter estimation, a retracking algorithm should account for a realistic waveform noise characterisation and for the real time evolution of instrumental properties, as the Point Target Response (PTR), as this could impact the parameter estimation leading to biases that are difficult to correct and sub-optimality (increase of the parameters error bars). These aspects become more critical with the high demanding requirements in terms of data quality and resolution as of the current reference mission Sentinel-6 and upcoming missions.
Because the baseline solution currently implemented in the ground segments of many missions, the MLE4 retracker, does not meet these goals, a new solution, the numerical retracker (Buchhaupt et al 2018, Dinardo et al. 2023), has been developed and recently implemented in the Sentinel-6 ground segment. Yet, while accounting for the real PTR, this solution does not include a realistic waveform noise and therefore it is not optimal. An accurate noise characterisation is particularly important for S6 as the pulse-to-pulse correlations linked to the S6 PRF configuration leads to a variation of the Effective Number of Looks (ENL) with respect to both the range gate and the SWH which may impact the parameter estimation if not correctly accounted for.
In this talk we present the FastAdaptiveS6, an optimal and efficient retracker for the analysis of S6 LRM data. The retracking is based on the FastAdaptive solution (Mangilli et al OSTST 2022) which includes the Adaptive model with numerical PTR convolution (Tourain et al 2021, Thibaut et al. OSTST 2017 & 2021) and a realistic waveform (multiplicative) noise characterisation that allows for an optimal and unbiased parameter estimation. In the talk we will present the formalism of the FastAdaptiveS6, the validation on realistic Sentinel-6 simulations and the results on S6 data. The waveform simulations are generated with a multiplicative noise with Gamma distribution and S6-like ENL variations to evaluate the impact of the noise properties on the parameters estimation. We will show the results obtained on representative S6 datasets over ocean, also in comparison with the baseline results of S6 products. We will demonstrate that the FastAdaptiveS6 is unbiased and optimal and allows for a significant improvement of the parameter estimation, in particular the SWH and range, with respect to existing retracking algorithms, making it a suitable solution for the analysis of S6 LRM data.