Application of the Ensemble Kalman Filter to a One-Dimensional Linear Advection Model

Somsiri Payakkarak; Dusadee Sukawat

doi:10.4028/www.scientific.net/AMM.804.287

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 804Application of the Ensemble Kalman Filter to a...

Application of the Ensemble Kalman Filter to a One-Dimensional Linear Advection Model

Abstract:

Data assimilation is used in numerical weather prediction to improve weather forecasts by incorporating observation data into the model forecast. The Ensemble Kalman Filter (EnKF) is a method of data assimilation which updates an ensemble of states to provide a state estimate and associated error at each step. The atmospheric model that is used in this research is a one-dimensional linear advection model. This model describes the motion of a scalar field as it is advected by a known speed field. The result shows that by selecting appropriate initial ensemble, model noise and measurement perturbations, it is possible to achieve a significant improvement in the EnKF results. The accuracy of the EnKF increases when the number of ensemble member grows. That is, the larger ensemble sizes perform better than those of smaller sizes.

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Periodical:

Applied Mechanics and Materials (Volume 804)

Pages:

287-290

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.804.287

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Online since:

October 2015

Authors:

Somsiri Payakkarak*, Dusadee Sukawat

Keywords:

Ensemble Kalman Filter, Linear Advection Model

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