Efficient methods for computing observation impact in 4D-Var data assimilation

Simple item page
dc.contributor.authorCioaca, Alexandruen
dc.contributor.authorSandu, Adrianen
dc.contributor.authorde Sturler, Ericen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2016-12-05T07:33:47Zen
dc.date.available2016-12-05T07:33:47Zen
dc.date.issued2013-12-01en
dc.description.abstractThis paper presents a practical computational approach to quantify the effect of individual observations in estimating the state of a system. Such an analysis can be used for pruning redundant measurements, and for designing future sensor networks. The mathematical approach is based on computing the sensitivity of the reanalysis (unconstrained optimization solution) with respect to the data. The computational cost is dominated by the solution of a linear system, whose matrix is the Hessian of the cost function, and is only available in operator form. The right hand side is the gradient of a scalar cost function that quantities the forecast error of the numerical model. The use of adjoint models to obtain the necessary first and second order derivatives is discussed. We study various strategies to accelerate the computation, including matrix-free iterative solvers, preconditioners, and an in-house multigrid solver. Experiments are conducted on both a small-size shallow-water equations model, and on a large-scale numerical weather prediction model, in order to illustrate the capabilities of the new methodology.en
dc.description.versionPublished versionen
dc.format.extent975 - 990 (16) page(s)en
dc.identifier.doihttps://doi.org/10.1007/s10596-013-9370-2en
dc.identifier.issn1420-0597en
dc.identifier.issue6en
dc.identifier.urihttp://hdl.handle.net/10919/73561en
dc.identifier.volume17en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000328319900008&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectTechnologyen
dc.subjectComputer Science, Interdisciplinary Applicationsen
dc.subjectGeosciences, Multidisciplinaryen
dc.subjectComputer Scienceen
dc.subjectGeologyen
dc.subjectVariational data assimilationen
dc.subjectSensitivity analysisen
dc.subjectObservation impacten
dc.subjectMatrix-free solversen
dc.subjectPreconditionersen
dc.subjectVARIATIONAL DATA ASSIMILATIONen
dc.subjectENSEMBLE DATA ASSIMILATIONen
dc.subjectADJOINT SENSITIVITYen
dc.subjectADAPTIVE OBSERVATIONSen
dc.subjectTARGETED OBSERVATIONSen
dc.subjectKALMAN FILTERen
dc.subjectSYSTEMen
dc.subjectMODELSen
dc.subjectFORECASTSen
dc.subjectMETRICSen
dc.titleEfficient methods for computing observation impact in 4D-Var data assimilationen
dc.title.serialComputational Geosciencesen
dc.typeArticleen
dc.type.dcmitypeTexten
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen
pubs.organisational-group/Virginia Tech/Scienceen
pubs.organisational-group/Virginia Tech/Science/COS T&R Facultyen
pubs.organisational-group/Virginia Tech/Science/Mathematicsen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
1305.5796v2.pdf
Size:
1.37 MB
Format:
Adobe Portable Document Format
Description:
Submitted Version
Download
License bundle
Now showing 1 - 1 of 1
Name:
VTUL_Distribution_License_2016_05_09.pdf
Size:
18.09 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

All Faculty Deposits
Scholarly Works, Computational Science Laboratory
Scholarly Works, Computer Science