We commonly refer to state estimation theory in geosciences as data assimilation (DA). This
term encompasses the entire sequence of operations that, starting from the observations of a …

Climate change, air quality, and environmental degradation are important societal
challenges for the Twenty-first Century. These challenges require an intelligent response …

This open-access textbook's significant contribution is the unified derivation of data-
assimilation techniques from a common fundamental and optimal starting point, namely …

In this book the authors describe the principles and methods behind probabilistic forecasting
and Bayesian data assimilation. Instead of focusing on particular application areas, the …

The use of the ensemble smoother (ES) instead of the ensemble Kalman filter increases the
nonlinearity of the update step during data assimilation and the need for iterative …

This paper examines the properties of the Iterated Ensemble Smoother (IES) and the
Multiple Data Assimilation Ensemble Smoother (ES–MDA) for solving the history matching …

Abstract The ensemble Kalman filter (EnKF) is a sequential data assimilation method that
has been demonstrated to be effective for history matching reservoir production data and …

This paper provides a unifying mean field based framework for the derivation and analysis of
ensemble Kalman methods. Both state estimation and parameter estimation problems are …

The iterative ensemble Kalman filter (IEnKF) was recently proposed in order to improve the
performance of ensemble Kalman filtering with strongly nonlinear geophysical models. The …

Parameters in climate models are usually calibrated manually, exploiting only small subsets
of the available data. This precludes both optimal calibration and quantification of …