Post-processing Altimeter Data Towards Coastal Applications and Integration into Coastal Models

Altimetry missions in the last 16 years (TOPEX/Poseidon, ERS-1/2, GFO, Jason-1 and ENVISAT) and the recently-launched Jason-2 mission have resulted in great advances in deep ocean research and operational oceanography. However, oceanographic applications using satellite altimeter data have become very challenging over regions extending from near-shore to the continental shelf and slope (Cipollini et al. 2008). In these regions, intrinsic difficulties in the corrections (e.g., the high frequency ocean response to tidal and atmospheric loading, the mean sea level, etc.) and issues of land contamination in the radar altimeter and radiometer footprints result in systematic flagging and rejection of these data. Forthcoming altimeter missions (SARAL/AltiKa, SWOT, Sentinel-3, etc.) are designed to be better-suited for use in the coastal ocean. However, a number of studies have dealt with the problem of re-analysing, improving and exploiting the existing archive to monitor coastal dynamics. The early encouraging results (Vignudelli et al. 2005; Bouffard et al. 2008, Birol et al. submitted J Mar Syst 2009) support the need for continued research in coastal altimetry, with the opportunity of providing input and recommendations for future missions.This chapter reviews the current status of the X-TRACK processing application (Roblou et al. 2007), whose objectives are to improve both the quantity and quality of altimeter sea surface height (SSH) estimates in coastal regions by reprocessing a posteriori (the standard Geophysical Data Records) (GDR) as delivered by operational centres, i.e. by improving the post-processing stage. Latest improvements on along-track spatial resolution (high rate data streams and removal of large-scale errors) that promise improved monitoring of coastal dynamics are also detailed. In addition, with a view to integrating coastal-oriented altimeter datasets into models for coastal ocean state analysis, methodologies for matching models with observations are discussed.pp 217-246