Spatially variable model parameters are often highly uncertain and difficult to observe. This has prompted the widespread use of Bayesian characterization methods that can infer parameter values from measurements of related variables, while explicitly accounting for uncertainty. Ensemble versions of Bayesian characterization are particularly convenient when uncertain variables have complex spat...

Using Lorenz96 model with 40 variables, classical methods of advanced data assimilation are explained, implemented and examined. The classical methods include full Kalman filter (KF), extended Kalman filter (EKF), full Kalman smoother (KS), its iterative versions, and sawtooth algorithms (Johnston and Kurishnamurthy 2001). A brief explanation of the theoretical background of ensemble Kalman fil...

We propose a regularization method for ensemble Kalman filtering (EnKF) with elliptic observation operators. Commonly used EnKF methods suppress state correlations at long distances. For observations described by partial differential equations, such as the pressure Poisson equation (PPE) in incompressible fluid flows, distance localization should be cautiously, we cannot disentangle slowly deca...

EnKF-C provides a compact generic framework for off-line data assimilation into large-scale layered geophysical models with the ensemble Kalman filter (EnKF). It is coded in C for GNU/Linux platform and can work either in EnKF or ensemble optimal interpolation (EnOI) mode.

Incorporating temporal (continuous) data into more common discrete data point geospatial models is necessary for dynamic real time model building. The models are otherwise limited in their use for numerical modelling, simulation and the prediction of climatic states over time. By adopting a Bayesian approach it is shown here to be possible to estimate the dynamic behaviour of unobserved climate...

Standard ensemble or particle filtering schemes do not properly represent states of low priori probability when the number of available samples is too small, as is often the case in practical applications. We introduce here a set of parametric resampling methods to solve this problem. Motivated by a general H-theorem for relative entropy, we construct parametric models for the filter distributi...