The nonlinear filtering problem occurs in many scientific areas. Sequential Monte Carlo
solutions with the correct asymptotic behavior such as particle filters exist, but they are …

This study evaluated three algorithms of the iterative ensemble Kalman filter (EnKF). They
are Confirming EnKF, Restart EnKF, and modified Restart EnKF developed to resolve the …

Accurate numerical weather forecasting is of great importance. Due to inadequate
observations, our limited understanding of the physical processes of the atmosphere, and …

This paper investigates the role of the linear analysis step of the ensemble Kalman filters
(EnKF) in disrupting the balanced dynamics in a simple atmospheric model and compares it …

The article deals with the predictions of time and space evolution of pollution dispersion
during the early phase of a hypotetical radiation accident. The goal is to design a proper fast …

The current trend for history matching is to find multiple calibrated models instead of a single
set of model parameters that match the historical data. The advantage of several current …

Estimation methods such as the Kalman filter identify best state estimates based on certain
optimality criteria using a model of the system and the observations. A common assumption …

Two classes of state estimation schemes, variational (4DVar) and ensemble Kalman (EnKF),
have been developed and used extensively by the weather forecasting community as …

Particle filter and ensemble Kalman filter are two Bayesian filtering algorithms adapted to
nonlinear state–space models. The problem of nonlinear Bayesian filtering is challenging …

Les modèles mathématiques sont importants pour la compréhension de la dynamique de
l'atmosphère et de l'océan. Mais si ils étaient notre seule source d'information aucune …