Data Assimilation by Ensemblekalman filter With the Lorenz Equations

Regis Sperotto de Quadros, Fabrício Pereira Harter, Daniela Buske, Larri Silveira Pereira


Data Assimilation is a procedure to get the initial condition as accurately as possible, through the statistical combination of collected observations and a background field, usually a short-range forecast. In this research a complete assimilation system for the Lorenz equations based on Ensemble Kalman Filter is presented and examined. The Lorenz model is chosen for its simplicity in structure and the dynamic similarities with primitive equations models, such as modern numerical weather forecasting. Based on results, was concluded that in this implementation, 10 members is the best setting, because there is overfitting for ensembles with 50 and 100 members. It was also examined whether the EnKF is effective to track the control for 20% and 40% of error in the initial conditions. The results show a disagreement between the "truth" and the estimation, especially in the end of integration period, due to the chaotic nature of the system. It was also concluded that EnKF has to be performed frequently in order to produce desirable results.


Data assimilation. Ensemble Kalman filter. Lorenz model.

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