Abstract's details

Improvements and limitations of recent mean sea surface models: importance for Sentinel-3.

Marie Isabelle Pujol (CLS, France)

CoAuthors

Yannice Faugère (CLS, France); Gérald Dibarboure (CNES, France); Nicolas Picot (CNES, France)

Event: 2019 Ocean Surface Topography Science Team Meeting

Session: The Geoid, Mean Sea Surfaces and Mean Dynamic Topography

Presentation type: Type Oral

Contribution: PDF file

Abstract:

Previous studies underlined the improved accuracy of the recent Mean Sea Surface (MSS) models. Despite the improvement, residual errors were shown to be significant for wavelengths shorter than ~100km. The MSS errors represent nearly 30% of the SLA variance along the Sentinel-3A tracks and are on the same order of magnitude as the instrumental noise floor of this altimeter (Pujol et al, 2018).
In that context, reducing the MSS model errors at short wavelengths remains necessary to improve the quality of sea level anomalies along new repeat ground tracks. This is particularly important to take full benefit of the noise-reducing SARM technology (and in the future of SWOT).
In this study, we nearly 2 years of Sentinel-3A measurements to build a new local MSS (also known as mean profile) along the new ground track. We also combine this data with the gridded CNES/CLS model to ensure both large scale accuracy and multi-mission consistency. The result is a so-called hybrid mean profile. The high precision of SARM data makes it possible to yield improve the smaller scales even with only 2 years of data. The MSS-related error is reduced by more than 50% for critical wavelengths ranging from 15 to 100 km (Dibarboure et al, 2019). The hybrid mean profile defined with 1Hz sampling is now currently used in the Sentinel-3A altimeter processing and contributes to reduce the errors of the along-track Level-3 products available on CMEMS.
 

Oral presentation show times:

Room Start Date End Date
The Monroe Hub Thu, Oct 24 2019,10:00 Thu, Oct 24 2019,10:15
Marie Isabelle Pujol
CLS
France
mpujol@groupcls.com