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.
Highlights
Increasing demand for accurate sea level anomaly (SLA) data close to the coast has led to a huge development in coastal altimetry and its applications, such as coastal management, long-term monitoring of coastal dynamics and storm surge studies.Satellite radar altimeters measure the range, a distance between satellite and the nadir surface, by retrieving the two-way travel time of radar short pulses sent to and reflected from the ocean surface.The SLA is referenced to a mean sea surface and can be derived from the range and satellite orbit.The reflected signal is called the ‘waveform’ and represents the time evolution of the reflected powerRemote Sens. 2017, 9, 603; doi:10.3390/rs9060603 www.mdpi.com/journal/remotesensingRemote Sens. 2017, 9, 603 as the radar signal hits the surface
When these Coastal Altimetry Waveform Retracking Expert System (CAWRES)-retracked sea surface heightsby (SSHs) are compared with SSHs by other retrackers from the Sensor Geophysical Data Records (SGDR) MLE4 (Table 8) and PISTACH (Table 9), the results show that CAWRES achieves improvements in precision that exceeds the other retrackers in all satellite passes
The novel idea of the system is (1) to reprocess altimeter waveforms using the optimal retracker, which is sought based on the analysis from a fuzzy expert system; and (2) to provide a seamless transition of retracked SLAs when switching from one retracker to another, based on the analysis from a neural network
Summary
Increasing demand for accurate sea level anomaly (SLA) data close to the coast has led to a huge development in coastal altimetry and its applications, such as coastal management, long-term monitoring of coastal dynamics and storm surge studies. Waveforms over a homogeneous ocean surface (e.g., open ocean without land interference) can generally be described by Brown [1] model It features a sharp leading edge up to the maximum value of the amplitude, followed by a gently sloping plateau known as the trailing edge. It is important to minimise the discontinuity of the retrieved geophysical parameters when switching retrackers from the open ocean to the coast, or vice versa. We initiate a novel method to retrieve precise SLAs from multiple retracking solutions through a Coastal Altimetry Waveform Retracking Expert System (CAWRES). The CAWRES is designed to optimise the estimation of SLAs by selecting the optimal retracker via a fuzzy expert system and to provide a seamless transition from the open ocean to coastlines (or vice versa) when switching retrackers via a neural network approach.
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