Differential evolution algorithm-based multiple-factor optimization methods for data assimilation

TL;DR: In this article, the authors proposed an ensemble Kalman filter for data assimilation using the flow-dependent statistics calculated from an ensemble of short-range forecasts (a technique referred to as Ensemble Kalman filtering) in an idealized environment.

TL;DR: A practical method for data assimilation in large, spatiotemporally chaotic systems, a type of “ensemble Kalman filter”, in which the state estimate and its approximate uncertainty are represented at any given time by an ensemble of system states.

TL;DR: In this paper, a nonlinear filtering theory is applied to unify the data assimilation and ensemble generation problem and to produce superior estimates of the probability distribution of the initial state of the atmosphere (or ocean) on regional or global scales.

TL;DR: A new, local formulation of the ensemble Kalman filter approach for atmospheric data assimilation based on the hypothesis that, when the Earth’s surface is divided up into local regions of moderate size, vectors of the forecast uncertainties in such regions tend to lie in a subspace of much lower dimension than that of the full atmospheric state vector of such a region.