We present a novel sampling-based method for estimating probabilities of rare or failure events. Our approach is founded on the ensemble Kalman filter (EnKF) inverse problems. Therefore, we reformulate event problem as an and apply EnKF to generate samples. To estimate probability failure, use final samples fit distribution model importance sampling with respect fitted distribution. This leads ...

Complex systems are often described with competing models. Such divergence of interpretation on the system may stem from model fidelity, mathematical simplicity, and more generally, our limited knowledge of the underlying processes. Meanwhile, available but limited observations of system state could further complicates one’s prediction choices. Over the years, data assimilation techniques, such...

We demonstrate the usefulness of a digital Gaussian filter to provide a distance-dependent reduction of background error covariance estimates generated from an ensemble of forecasts. These improved background error covariance estimates are used in a hybrid ensemble Kalman filter data assimilation scheme to generate a reduced-error ensemble of model initial conditions. The benefits of using the ...

Lagrangian measurements from passive ocean instruments provide a useful source of data for estimating and forecasting the ocean’s state (velocity field, salinity field, etc). However, trajectories from these instruments are often highly nonlinear, leading to difficulties with widely-used data assimilation algorithms such as the ensemble Kalman filter (EnKF). Additionally, the velocity field is ...

We consider the relative advantages of two advanced data assimilation systems, 4-D-Var and ensemble Kalman filter (EnKF), currently in use or under consideration for operational implementation. With the Lorenz model, we explore the impact of tuning assimilation parameters such as the assimilation window length and background error covariance in 4-D-Var, variance inflation in EnKF, and the effec...

Hybrid data assimilation methods combine elements of ensemble Kalman filters (EnKF) and variational methods. While most approaches have focused on augmenting an operational variational system with dynamic error covariance information from an EnKF [1][2][4][5][8], we take the opposite perspective of augmenting an operational EnKF with information from a simple 3D-Variational (3D-Var) method [7]....

Data assimilation in meteorology seeks to provide a current analysis of the state of the atmosphere to use as initial conditions in a weather forecast. This is achieved by using an estimate of a previous state of the system and merging that with observations of the true state of the system. Ensemble Kalman filtering is one method of data assimilation. Ensemble Kalman filters operate by using an...

We present several methods for the efficient implementation of the Ensemble Kalman Filter (EnKF) of Evensen. It is shown that the EnKF can be implemented without access to the observation matrix, and only an observation function is needed; this greatly simplifies software design. New implementations of the EnKF formulas are proposed, with linear computational complexity in the number of data po...

A number of algorithms to solve large-scale Kalman filtering problems have been introduced recently. The ensemble Kalman filter represents the probability density of the state estimate by a finite number of randomly generated system states. Another algorithm uses a singular value decomposition to select the leading eigenvectors of the covariance matrix of the state estimate and to approximate t...