A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The unbounded error growth found in the extended Kalman filter, which is caused by an overly simplifie...

This paper proposes an extension of the Weighted Ensemble Kalman filter (WEnKF) proposed by Papadakis et al. (2010) for the assimilation of image observations. The main contribution of this paper consists in a novel formulation of the Weighted filter with the Ensemble Transform Kalman filter (WETKF) incorporating directly as a measurement model a nonlinear image reconstruction criterion. This t...

Many geophysical problems are characterized by high-dimensional, nonlinear systems and pose difficult challenges for real-time data assimilation (updating) and forecasting. The present work builds on the ensemble Kalman filter (EnsKF) with the goal of producing ensemble filtering techniques applicable to non-Gaussian densities and high-dimensional systems. Three filtering algorithms based on re...

In the ensemble Kalman filter (EnKF), uncertainty in the state of a dynamical model is represented as samples of the state vector. The samples are propagated forward using the evolution model, and the forecast (prior) mean and covariance matrix are estimated from the ensemble. Data assimilation is carried out by using these estimates in the Kalman filter formulas. The prior is given in the subs...

Milad Hooshyar Department of civil, Environmental, and construction Engineering, University of Central Florida. [email protected] Stephen C. Medeiros Department of civil, Environmental, and construction Engineering, University of Central Florida. Dingbao Wang Department of civil, Environmental, and construction Engineering, University of Central Florida. Scott C. Hagen Department o...