Computer Model Calibration using the Ensemble Kalman Filter

TL;DR

This paper investigates the use of the ensemble Kalman filter (EnKF) for calibrating complex computer models, demonstrating its effectiveness in high-dimensional, computationally demanding scenarios like climate and cosmology.

Contribution

It introduces the application of EnKF to computer model calibration, comparing its performance with traditional methods in high-dimensional, real-world problems.

Findings

EnKF effectively calibrates high-dimensional models.

EnKF outperforms traditional calibration methods in certain scenarios.

Application to climate and cosmology demonstrates versatility.

Abstract

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.