Two classes of state estimation schemes, variational (4DVar) and ensemble Kalman (EnKF), have been developed and used extensively by the weather forecasting community as tractable alternatives to the standard matrix-based Kalman update equations for the estimation of high-dimensional nonlinear systems with possibly nongaussian PDFs. Variational schemes iteratively minimize a finite-horizon cost...

This paper focuses on sequential Monte Carlo approximations of smoothing distributions in conditionally linear and Gaussian state spaces. To reduce Monte Carlo variance of smoothers, it is typical in these models to use Rao-Blackwellization: particle approximation is used to sample sequences of hidden regimes while the Gaussian states are explicitly integrated conditional on the sequence of reg...

In this paper, we describe a new adaptive system for the enhancement of autoregressive (AR) signals which are disturbed by additive broadband noise. The system is comprised of an adaptive Kalman filter operating as a fixed-lag smoother and a subsystem for AR parameter estimation. As opposed to the conventional approach of employing an extended Kalman filter, we estimate the Kalman filter parame...

We demonstrate a three-step method for estimating time-resolved velocity fields using time-resolved point measurements and non-time-resolved particle image velocimetry data. A variant of linear stochastic estimation is used to obtain an initial set of time-resolved estimates of the flow field. These estimates are then used to identify a linear model of the flow dynamics. The model is incorporat...

In this paper, finite impulse response (FIR) smoothers are proposed for discrete-time systems. The proposed FIR smoother is designed under the constraints of linearity, unbiasedness, FIR structure, and independence of the initial state information. It is also obtained by directly minimizing the performance criterion with unbiased constraints. The approach to the MVF smoother proposed in this pa...

Abstract. Ensemble variational methods form the basis of state art for nonlinear, scalable data assimilation, yet current designs may not be cost-effective real-time, short-range forecast systems. We propose a novel estimator in this formalism that is designed applications which error dynamics weakly such as synoptic-scale meteorology. Our method combines 3D sequential filter analysis and retro...