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dc.contributor.author
Lguensat, Redouane
dc.contributor.author
Tandeo, Pierre
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Ailliot, Pierre
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Pulido, Manuel Arturo
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Fablet, Ronan
dc.date.available
2018-05-08T18:10:41Z
dc.date.issued
2017-10
dc.identifier.citation
Lguensat, Redouane; Tandeo, Pierre; Ailliot, Pierre; Pulido, Manuel Arturo; Fablet, Ronan; The analog data assimilation; American Meteorological Society; Monthly Energy Review; 145; 10; 10-2017; 4093-4107
dc.identifier.issn
0027-0644
dc.identifier.uri
http://hdl.handle.net/11336/44461
dc.description.abstract
In light of growing interest in data-driven methods for oceanic, atmospheric, and climate sciences, this work focuses on the field of data assimilation and presents the analog data assimilation (AnDA). The proposed framework produces a reconstruction of the system dynamics in a fully data-driven manner where no explicit knowledge of the dynamical model is required. Instead, a representative catalog of trajectories of the system is assumed to be available. Based on this catalog, the analog data assimilation combines the nonparametric sampling of the dynamics using analog forecasting methods with ensemble-based assimilation techniques. This study explores different analog forecasting strategies and derives both ensemble Kalman and particle filtering versions of the proposed analog data assimilation approach. Numerical experiments are examined for two chaotic dynamical systems: the Lorenz-63 and Lorenz-96 systems. The performance of the analog data assimilation is discussed with respect to classical model-driven assimilation. A Matlab toolbox and Python library of the AnDA are provided to help further research building upon the present findings.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
American Meteorological Society
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Data Assimilation
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Ensembles
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Kalman Filters
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Statistical Forecasting
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Meteorología y Ciencias Atmosféricas
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Ciencias de la Tierra y relacionadas con el Medio Ambiente
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CIENCIAS NATURALES Y EXACTAS
dc.title
The analog data assimilation
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-04-25T21:32:17Z
dc.identifier.eissn
1520-0493
dc.journal.volume
145
dc.journal.number
10
dc.journal.pagination
4093-4107
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Boston
dc.description.fil
Fil: Lguensat, Redouane. Université Bretagne Loire; Francia
dc.description.fil
Fil: Tandeo, Pierre. Université Bretagne Loire; Francia
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Fil: Ailliot, Pierre. University of Western Brittany. Laboratoire de Mathématiques de Bretagne Atlantique; Francia
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Fil: Pulido, Manuel Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; Argentina
dc.description.fil
Fil: Fablet, Ronan. Université Bretagne Loire; Francia
dc.journal.title
Monthly Energy Review
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1175/MWR-D-16-0441.1
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://journals.ametsoc.org/doi/10.1175/MWR-D-16-0441.1
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