The Analog Ensemble Kalman Filter and Smoother

Abstract : The amount of observational and model-simulated data in geosciences has grown rapidly since the early 1980s. These data, still widely underexploited, has a unique potential for the modeling and prediction of geophysical space-time dynamics. Here, we show how a statistical emulator, based on a catalog of historical datasets, and a sequential Monte Carlo filter and smoother, provide a relevant data-driven analog assimilation of complex dynamics. As an illustration, we consider the chaotic Lorenz-63 model.
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Pierre Tandeo, Pierre Ailliot, Ronan Fablet, Juan Ruiz, François Rousseau, et al.. The Analog Ensemble Kalman Filter and Smoother. CI 2014 : 4th International Workshop on Climate Informatics, Sep 2014, Boulder, United States. ⟨hal-01188825⟩

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