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From the ensemble Kalman Filter to the particle filter: A comparative study in rainfall-runoff models

Abstract:

Rainfall-runoff models are hydrologic models and they play a very important role in ood forecasting. Hydrologic systems are featured by a non-linear behaviour, non-Gaussian distributions and the presence of large uncertainties in the model itself and in the input data. Therefore, the use of non-linear/non-Gaussian state estimation or data assimilation techniques is important in order to improve the model pbkp_redictions. The objective of this paper is to present a comparative study between the performances of two sequential data assimilation techniques, which are based on Monte Carlo (MC) methods. The advantages and drawbacks of the well known ensemble Kalman Filter (EnKF) and the standard particle filter will be discussed in this study. Results show that both filters perform similar with the surface soil moisture as the estimated variable and using synthetic generated data. © 2011 IFAC.