Abstract
Satellite altimetry has revolutionised our understanding of ocean dynamics thanks to frequent sampling and global coverage. Nevertheless, coastal data have been flagged as unreliable due to land and calm water interference in the altimeter and radiometer footprint and uncertainty in the modelling of high-frequency tidal and atmospheric forcing.Our study addresses the first issue, i.e. altimeter footprint contamination, via retracking, presenting ALES, the Adaptive Leading Edge Subwaveform retracker. ALES is potentially applicable to all the pulse-limited altimetry missions and its aim is to retrack both open ocean and coastal data with the same accuracy using just one algorithm.ALES selects part of each returned echo and models it with a classic “open ocean” Brown functional form, by means of least square estimation whose convergence is found through the Nelder–Mead nonlinear optimisation technique. By avoiding echoes from bright targets along the trailing edge, it is capable of retrieving more coastal waveforms than the standard processing. By adapting the width of the estimation window according to the significant wave height, it aims at maintaining the accuracy of the standard processing in both the open ocean and the coastal strip.This innovative retracker is validated against tide gauges in the Adriatic Sea and in the Greater Agulhas System for three different missions: Envisat, Jason-1 and Jason-2. Considerations of noise and biases provide a further verification of the strategy. The results show that ALES is able to provide more reliable 20-Hz data for all three missions in areas where even 1-Hz averages are flagged as unreliable in standard products. Application of the ALES retracker led to roughly a half of the analysed tracks showing a marked improvement in correlation with the tide gauge records, with the rms difference being reduced by a factor of 1.5 for Jason-1 and Jason-2 and over 4 for Envisat in the Adriatic Sea (at the closest point to the tide gauge).
Highlights
Adaptive Leading Edge Sub-waveform (ALES) selects part of each returned echo and models it with a classic ”open ocean” Brown functional form, by means of least square estimation whose convergence is found through the Nelder-Mead nonlinear optimization technique
For Jason-2 pass 198 (J-2 198) and Env 687 in Mossel Bay, since there was no independent estimate of TG height relative to the ellipsoid, root mean square (RMS) values correspond to the median value of the along-track RMS of the difference between Total Water Level Envelope (TWLE) and TG sea level height anomaly, while for Jason-2 pass 196 (J-2 196), J-1 161 and Envisat pass 416 (Env 416) in the Adriatic Sea we report the RMS of the difference of the absolute sea level heights above the ellipsoid at the closest point between TG and satellite tracks
The present study aimed at the development and validation of ALES, the Adaptive Leading Edge Subwaveform retracker, which is capable of retrieving useful sea level infor[660] mation both in the open ocean and in the coastal zone
Summary
ALES selects part of each returned echo and models it with a classic ”open ocean” Brown functional form, by means of least square estimation whose convergence is found through the Nelder-Mead nonlinear optimization technique. The residual noise of real waveforms, evident along the trailing edge, can influence the correct retrieval of the parameters of interest in the retracking process, since the waveforms deviate from the theoretical open ocean shape This is known to happen in particular in the last 10 km from the coastline: at this distance, both coastal waters and raised land can give returns within the altimeter’s range window. Land returns could still appear in the trailing edge, even if the surface is located outside the expected footprint, because their location could be equidistant with the ocean surface near nadir This would produce a more predictable hyperbolic feature than what is shown in the radargrams, where bright targets are not seen constantly at every cycle and their location and extent varies. 87 the adaptation of a different functional form for every kind of characteristic shape that the waveforms can assume (Berry et al, 1997; Andersen et al, 2010)
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