Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/5803
AuthorsStorto, A.* 
Dobricic, S.* 
Masina, S.* 
Di Pietro, P.* 
TitleGlobal oceanographic variational data assimilation of in-situ observations and space-borne altimeter data for reanalysis applications
Issue Date5-Oct-2009
URIhttp://hdl.handle.net/2122/5803
Keywordsocean modelling
data assimilation
reanalysis
upper ocean variability
sea level height
temperature
salinity
Subject Classification03. Hydrosphere::03.01. General::03.01.04. Ocean data assimilation and reanalysis 
AbstractThe study of global climatological trends requires the accurate analysis surface and sub-surface ocean state. In the last two decades, altimetric satellite missions have been launched with the aim of monitoring the sea level height variability, in time and space. This information may in turn be used, within data assimilation systems, for adjusting the column-integrated density fields in synergy with in-situ observations. The impact of the Sea Level Anomaly (SLA) data has been recently proved positive in many regional and global data assimilation system. However, gaining a positive impact from altimetric data needs i) the establishment of a correct strategy for updating temperature and salinity fields accordingly; ii) the correct assessment of the Mean Dynamic Topography to add to the anomaly data; iii) the consistency between the scales represented by the SLA data and those resolved by the ocean model. At the National Institute for Geophysics and Volcanology (INGV) and the Euro-Mediterranean Centre for Climate Change (CMCC), the former reduced-rank Optimal Interpolation (OI) analysis system (Bellucci et al., 2007) was used to produce ocean reanalysis for the last four decades. It has recently been replaced with a three-dimensional variational data assimilation system, which uses a First Guess at appropriate Time (FGAT) algorithm. The 3DVAR/FGAT formulation is adapted from the one operationally used for producing daily analysis in the Mediterranean basin (Dobricic et al., 2008), and is able to successfully assimilate satellite sea-level anomaly observations.
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