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@inproceedings{Arcucci:2017:10.1063/1.4981942,
author = {Arcucci, R and D'Amore, L and Toumi, R},
doi = {10.1063/1.4981942},
publisher = {AIP Publishing},
title = {Preconditioning of the background error covariance matrix in data assimilation for the Caspian Sea},
url = {http://dx.doi.org/10.1063/1.4981942},
year = {2017}
}

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TY  - CPAPER
AB  - Data Assimilation (DA) is an uncertainty quantification technique used for improving numerical forecasted results by incorporating observed data into prediction models. 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 first studies concerning the introduction of two different 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 also present the algorithmic strategies we employ.
AU  - Arcucci,R
AU  - D'Amore,L
AU  - Toumi,R
DO  - 10.1063/1.4981942
PB  - AIP Publishing
PY  - 2017///
SN  - 1551-7616
TI  - Preconditioning of the background error covariance matrix in data assimilation for the Caspian Sea
UR  - http://dx.doi.org/10.1063/1.4981942
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000409539000002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/54204
ER  -

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