CAWRES: A Waveform Retracking Fuzzy Expert System for Optimizing Coastal Sea Levels from Jason-1 and Jason-2 Satellite Altimetry Data

Abstract

This paper presents the Coastal Altimetry Waveform Retracking Expert System (CAWRES), a novel method to optimise the Jason satellite altimetric sea levels from multiple retracking solutions. CAWRES' aim is to achieve the highest possible accuracy of coastal sea levels, thus bringing measurement of radar altimetry data closer to the coast. The principles of CAWRES are twofold. The first is to reprocess altimeter waveforms using the optimal retracker, which is sought based on the analysis from a fuzzy expert system. The second is to minimise the relative offset in the retrieved sea levels caused by switching from one retracker to another using a neural network. The innovative system is validated against geoid height and tide gauges in the Great Barrier Reef, Australia for Jason-1 and Jason-2 satellite missions. The regional investigations have demonstrated that the CAWRES can effectively enhance the quality of 20 Hz sea level data and recover up to 16% more data than the standard MLE4 retracker over the tested region. Comparison against tide gauge indicates that the CAWRES sea levels are more reliable than those of Sensor Geophysical Data Records (SGDR) products, because the former has a higher (≥0.77) temporal correlation and smaller (≤19 cm) root mean square errors. The results demonstrate that the CAWRES can be applied to coastal regions elsewhere as well as other satellite altimeter missions.

Validation and Quality Assessment of Sea Levels from SARAL/ AltiKa Satellite Altimetry over the Marginal Seas at the Southeast Asia

“…Waveform retracking can be conducted based on the physical (e.g., MLE4, OCE3, and Red3) or empirical (e.g., Ice1 and Offset Centre of Gravity (OCOG)) retrackers. The former fits the waveforms to an ocean surface model (e.g., [10]) to retrieve the optimized parameters (e.g., [10][11][12][13]14]), and the latter retrieves the parameters based on the empirical assumption about the signals (e.g., [15,16]).…”

Sea level anomalies using altimetry, model and tide gauges along the African coasts in the Eastern Tropical Atlantic Ocean: Inter-comparison and temporal variability

“…Similar methods for the SLA estimation have already been used in previous studies (e.g., Fenoglio-Marc et al, 2015;Jebri et al, 2016;Gómez-Enri et al, 2016, 2018Birol et al, 2017;Salazar-Ceciliano et al, 2018;Taburet et al, 2019). Some are studies also compare the different products of DT by applying an additional offset to each sea level time series, chosen so that the average value is zero for the strating year (e.g., Risien and Strub, 2016;Idris et al, 2017). This allows to estimate sea level rise with respect to the reference year (in our case, the reference year would be 2008).…”

“…Bonnefond et al, 2015;Jebri et al, 2016;Marijan et al, 2017;Birol et al, 2017), Asia and Australia (e.g. Liu and Huang 2019;Peng and Deng, 2018;Kumar et al, 2017;Idris et al, 2017), where there are high quality TGs, covering the entire altimetry period and equipped with GPS to correct the effect of local vertical ground motions (Simon et al, 2013;Le Cozannet et al, 2015;Melet et al, 2016). Only few sea level anomalies (SLA) validation studies have been conducted along the coast of the Eastern Tropical Atlantic Ocean (ETAO: 35°S -25°N; 25°W -African coasts).…”

Waveform Classification and Retracking of Jason-2 and Jason-3 in Halmahera Sea

“…This is a ground processing technique to re-calculate the altimetric geophysical parameters, particularly the SSHs, by fitting and/or applying the waveforms to the appropriate retrackers [11]. Many studies have succeeded in increasing the accuracy of SSHs estimation in coastal areas with waveform retracking e.g., [11,12,16,17,18,19,20,21]. [12] succeeded in increasing the accuracy of Geosat altimeter data waveform in Taiwan waters up to 20%.…”