This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct ob...

State estimation in high dimensional systems remains a challenging part of real time analysis. The ensemble Kalman filter addresses this challenge by using Gaussian approximations constructed from a number of samples. This method has been a large success in many applications. Unfortunately, for some cases, Gaussian approximations are no longer valid and the filter does not work so well. In this...

The literature on ensemble-based data assimilation techniques has been growing rapidly in past decade. These techniques are being explored as possible alternatives to current operational analysis techniques. Ensemble-based assimilation techniques are typically comprised of an ensemble of parallel data assimilation and forecast cycles. The background-error covariances used in the data assimilati...

We present an efficient variation of the Local Ensemble Kalman Filter (Ott et al. 2002, 2004) and the results of perfect model tests with the Lorenz-96 model. This scheme is locally analogous to performing the Ensemble Transform Kalman Filter (Bishop et al. 2001). We also include a four-dimensional extension of the scheme to allow for asynchronous observations.

The ensemble Kalman filter is now an important component of ensemble forecasting. While using the linear relationship between the observation and state variable makes it applicable for large systems, relying on linearity introduces non-negligible bias since the true distribution will never be Gaussian. We review the ensemble Kalman filter from a statistical perspective and analyze the sources o...

Ensemble Kalman filters (EnKF) based on a small ensemble tend to provide collapse of the ensemble over time. It is shown that this collapse is caused by positive coupling of the ensemble members due to use of one common estimate of the Kalman gain for the update of all ensemble members at each time step. This coupling can be avoided by resampling the Kalman gain from its sampling distribution i...