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Regional validation of the Coastal Altimetry Waveform Retracking Expert System (CAWRES) over the largest archipelago in Southeast Asian seas

ABSTRACT This paper presents the latest finding on the validation of the novel Coastal Altimetry Waveform Retracking Expert System (CAWRES) that was previously developed for optimizing the sea level anomaly… Click to show full abstract

ABSTRACT This paper presents the latest finding on the validation of the novel Coastal Altimetry Waveform Retracking Expert System (CAWRES) that was previously developed for optimizing the sea level anomaly (SLA) over coastal oceans. The work is meant for the continuation of our previous works that have been successfully tested at two coastal sites in Australia and the United States. To further examine the potential of CAWRES of being applied to coastal region elsewhere, we extend the works to the largest archipelago at the Southeast Asia regions where complicated coastal topographies are experienced. The Jason-2 altimetric waveforms have been reprocessed using CAWRES to retrieve the retracked SLAs. Results in this study indicate that over coastal oceans, CAWRES records much higher (>80%) percentage of data than the standard Maximum Likelihood Estimator-4 (MLE4), <20%) retracker. The SLAs minimum distance of CAWRES is found to be similar to those of the Ice-1 ranging from 0.6 to 9 km. Comparison with tide gauge indicates that in six out of nine cases, the correlation of CAWRES is superior to those of the other retrackers with correlation ranging from 0.48 to 0.90. However, the Root Mean Square Error (RMSE) indicates no significant difference among all retrackers. Based on the results, it can be concluded that the performance of CAWRES is nearly similar to those of the Ice-1 retracker, except that CAWRES has a better correlation with tide gauge data. This remarks the needs of embedding the Ice-1 retracker in the CAWRES system.

Keywords: waveform retracking; expert system; system; retracking expert; altimetry waveform; coastal altimetry

Journal Title: International Journal of Remote Sensing
Year Published: 2019

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