On the potential of data assimilation to generate SMOS-Level 4 maps of sea surface salinity

• Published in: Remote Sensing of Environment
• Main Authors: Hoareau, Nina, Umbert, Marta, Martínez, Justino, Turiel, Antonio, Ballabrera-Poy, Joaquim
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Elsevier 2014
• Subjects: Data assimilation; Nudging; Eastern subtropical North-Atlantic Ocean; SMOS; Singularity analysis; Sea surface salinity; SSS; North Atlantic
• Online Access: http://hdl.handle.net/10261/97546; https://doi.org/10.1016/j.rse.2013.10.005

13 pages, 11 figures, 2 tables The Soil Moisture/Ocean Salinity (SMOS) satellite, launched in November 2009, measures visibilities at L-band, from which brightness temperatures are computed. This information is used to retrieve values of the sea surface salinity (SSS) and soil moisture; two variables whose observation is a key to better understand the oceanic component of the water cycle. A hierarchy of SSS products has been defined in the SMOS data processing chain. This work focuses on the so-called Level 3 (binned maps of SSS) and Level 4 (products combining SMOS data with any other source of information). The objective is to illustrate the feasibility of using data assimilation to produce Level 4 maps of sea surface salinity. The numerical model will increase the geophysical coherence of SMOS data as a dynamical interpolator. Here, the employment of data assimilation differs from its usual applications (improving model outputs for example). Indeed, the numerical model will interpolate the observations according to the general laws of fluid mechanics and, if possible, reduce the error contained in the original observations. The data assimilation method analyzed is a nudging algorithm. The domain of application for this feasibility study is the Northeast subtropical Atlantic gyre, a challenging region due to the presence of a large amount of noise that deteriorates the SMOS data. The main sources of errors are the vicinity of large landmasses that introduce a spurious bias, and the presence of a significant amount of artificial radio frequency interferences (RFI). While the Quality Controls already set up in the SMOS processing chain do filter the retrievals containing too large errors, wrong data are still present in Level 3 maps. Despite this difficulty, the results provide meaningful SMOS SSS Level 4 products in terms of their geophysical coherence (estimated using singularity analysis) and better agreement with in-situ data than Level 3 product. © 2013 Elsevier Inc. This study is supported by the ...