Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review

TL;DR: In this article, the authors review the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data-assimilation in Coupled Chemistry Meteorology Models (CCMM).

TL;DR: In this paper, a hybrid approach based on the combination of data assimilation and machine learning is proposed for a twofold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting their future states.

TL;DR: In this paper, the authors proposed a framework to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both, through matching low-order statistics between ESMs, observations, and high resolution simulations.

TL;DR: It is argued that in order to understand the dynamics of either system, Earth System Models must be coupled with Human System Models through bidirectional couplings representing the positive, negative, and delayed feedbacks that exist in the real systems.

TL;DR: In this paper, the clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the?stat-tran-sition? method of analysis of dynamic systems.

TL;DR: In this article, a new sequential data assimilation method is proposed based on Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter.

TL;DR: An overview of methods for sequential simulation from posterior distributions for discrete time dynamic models that are typically nonlinear and non-Gaussian, and how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature are shown.

TL;DR: A comprehensive text and reference work on numerical weather prediction, first published in 2002, covers not only methods for numerical modeling, but also the important related areas of data assimilation and predictability.