Artículo
The analog data assimilation
Fecha de publicación:
10/2017
Editorial:
American Meteorological Society
Revista:
Monthly Energy Review
ISSN:
0027-0644
e-ISSN:
1520-0493
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
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.
Palabras clave:
Data Assimilation
,
Ensembles
,
Kalman Filters
,
Statistical Forecasting
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Articulos(IMIT)
Articulos de INST.DE MODELADO E INNOVACION TECNOLOGICA
Articulos de INST.DE MODELADO E INNOVACION TECNOLOGICA
Citación
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
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