In classical data assimilation using sequential Monte Carlo methods, a physical model is run at each time steps to simulate members corresponding to different forecast scenarios. In this paper, we propose to use statistical analogs provided by observational or model-simulated data to emulate the dynamical model and generate relevant forecast members. This new methodology is called AnEnKF/AnEnFS...

A state space model for multivariate longitudinal count data driven by a latent gamma Markov process is proposed, the observed counts being conditionally independent and Poisson distributed given the latent process. We consider regression analysis for this model with time-varying covariates entering either via the Poisson model or via the latent gamma process. We develop the Kalman lter and smo...

We present telescoping recursive representations for both continuous and discrete indexed noncausal Gauss-Markov random fields. Our recursions start at the boundary (for example, a hypersurface in R, d ≥ 1) and telescope inwards. Under appropriate conditions, the recursions for the random field are differential/difference representations driven by white noise, for which we can use standard recu...

Kalman-Bucy smoothers are often used to estimate the state variables as a function of time in a system with stochastic dynamics and measurement noise. This is accomplished using an algorithm for which the number of numerical operations grows linearly with the number of time points. All of the randomness in the model is assumed to be Gaussian. Including other available information, for example a...

Abstract An ensemble-based method for seismic inversion to estimate elastic attributes is considered, namely the iterative ensemble Kalman smoother. The main focus of this work challenge associated with waveform data. amount data large and, depending on size, it cannot be processed in a single batch. Instead solution strategy partitioning recordings time windows and processing these sequentiall...

We explore the use of a fixed-lag Kalman smoother for sequential estimation of atmospheric carbon dioxide fluxes. This technique takes advantage of the fact that most of the information about the spatial distribution of sources and sinks is observable within a few months to half of a year of emission. After this period, the spatial structure of sources is diluted by transport and cannot signifi...

A rapidly melting ice sheet produces a distinctive geometry, or fingerprint, of sea level (SL) change. Thus, a network of SL observations may, in principle, be used to infer sources of meltwater flux. We outline a formalism, based on a modified Kalman smoother, for using tide gauge observations to estimate the individual sources of global SL change. We also report on a series of detection exper...

This article presents an estimation method of neuronal activity into the brain using a Kalman smoother approach that takes into account in the solution of the inverse problem the dynamic variability of the time series. This method is applied over a realistic head model calculated with the boundary element method. A comparative analysis for the dynamic estimation methods is made up from simulate...