Toward a preconditioned scalable 3DVAR for assimilating Sea Surface Temperature collected into the Caspian Sea

R. Arcucciab1, L.Carracciuoloc and R.Toumid
a: University of Naples Federico II, Naples, Italy
b: Euro Mediterranean Center on Climate Change, Italy
c: National Research Council, Naples, Italy
d: Imperial College London, London, United Kingdom

Received 1 February, 2017; accepted in revised form 03 April, 2018

Abstract: Data Assimilation (DA) is an uncertainty quanti cation technique used to
incorporate observed data into a prediction model in order to improve numerical forecasted
results. As a crucial point into DA models is the ill conditioning of the covariance matrices
involved, it is mandatory to introduce, in a DA software, preconditioning methods. Here
we present rst results obtained introducing two di erent preconditioning methods in
a DA software we are developing (we named S3DVAR) which implements a Scalable
Three Dimensional Variational Data Assimilation model for assimilating sea surface
temperature (SST) values collected into the Caspian Sea by using the Regional Ocean
Modeling System (ROMS) with observations provided by the Group of High resolution
sea surface temperature (GHRSST). We present the algorithmic strategies we employ
and the numerical issues on data collected in two of the months which present the most
signi cant variability in water temperature: August and March.

c 2018 European Society of Computational Methods in Sciences and Engineering

Keywords: Data Assimilation, oceanographic data, Sea Surface Temperature, Caspian sea,
ROMS
Mathematics Subject Classi cation: 65Y05, 65J22, 68W10, 68U20
PACS: 02.70.-c

 

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