… of the model bias, and we consider different ways of modeling (ie parameterizing) the model
bias… We evaluate the effectiveness of the proposed augmented state ensemble Kalman filter …

… to apply the Kalman filter to a model error that is biased, ie … This situation is often the
case in groundwater modeling: … the Kalman filter optimal in the presence of unknown bias, it …

… form of the bias estimate should be used to correct real radiance biases, rather to show
that we can efficiently estimate bias correction parameters with an ensemble Kalman filter. …

… A fundamental assumption in the EnKF assimilation theory is that both observations and
model predictions are free of bias, only in the presence of random and zeromean errors. …

… But the information of a random bias is unknown or incorrect in general. To solve this …
filters, such as an adaptive fading Kalman filter (AFKF) and an adaptive two-stage Kalman filter (…

… The goal of this paper is to introduce the uncompensated bias Kalman filter as presented
by the authors in [6,7]. More recently Hough [8] independently derived a similar algorithm and …

… bias in reviews using Kalman filtering technique that is computationally feasible and can
update the estimation of bias … Our results show a significant presence of sequential bias in the …

… [2001b], for example, found that difficulties occurred because of the presence of model bias,
and many shortcomings in assimilation results have been found due to incorrect knowledge …

… -Kalman filter for mitigating the effects of residual biases on sensor attitude and measurement
generation. The algorithm incorporates the residual bias … by the presence of bias errors. …

… and forecast bias estimation with and without bias correction … Kalman filter (EnKF) allows
for some implicit forecast bias … model and in the presence of bias, only EnBKF_1 is optimal. …