  The ensemble Kalman filter (EnKF) (Evensen, 2009) has proven effective in
quantifying uncertainty in a number of challenging dynamic, state estimation,
or data assimilation, problems such as weather forecasting and ocean modeling.
In these problems a high-dimensional state parameter is successively updated
based on recurring physical observations, with the aid of a computationally
demanding forward model that prop- agates the state from one time step to the
next. More recently, the EnKF has proven effective in history matching in the
petroleum engineering community (Evensen, 2009; Oliver and Chen, 2010). Such
applications typically involve estimating large numbers of parameters,
describing an oil reservoir, using data from production history that accumulate
over time. Such history matching problems are especially challenging examples
of computer model calibration since they involve a large number of model
parameters as well as a computationally demanding forward model. More
generally, computer model calibration combines physical observations with a
computational model - a computer model - to estimate unknown parameters in the
computer model. This paper explores how the EnKF can be used in computer model
calibration problems, comparing it to other more common approaches, considering
applications in climate and cosmology.
