Design, validation and application of a new coastal altimetry strategy

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. This thesis addresses the first issue, i.e. altimeter footprint contamination, presenting ALES, the Adaptive Leading Edge Subwaveform retracker. ALES is potentially applicable to all the pulse-limited altimetry missions and its aim is to process both open ocean and coastal data with the same accuracy using just one algorithm. ALES uses only a portion of the returned echo to estimate sea level and sea state, adapting the width of the estimation window according to the significant wave height. The sea level and the significant wave height estimated by ALES are validated regionally for three different missions: Envisat, Jason-1 and Jason-2. Validation is performed by comparison with in-situ data, i.e. tide gauges and buoys. 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. The ALES data are applied to improve the analysis of the annual cycle of the sea level in the North Sea-Baltic Sea transition area. The coastal amplitude of the annual cycle estimated from ALES altimetry data is in better agreement with estimations derived from in situ data than the one from the reference data set. In the Indonesian Seas, ALES data are used to cross-calibrate the SAR altimetry product of Cryosat-2 and to derive an empirical sea state bias correction to the SAR altimetry estimations (equal to 5% of the significant wave height), still missing in the official product.

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