Eugenia Kalnay

TL;DR: In this paper, the authors introduced a new formulation of the ensemble forecast sensitivity developed by Liu and Kalnay with a small correction from Li et al. The new formulation, like the original one, is tested on the simple Lorenz 40-variable model, and they find that the use of localization in the analysis, necessary in ensemble Kalman filter (EnKF) when the number of ensemble members is much smaller than the model's degrees of freedom, has a negative impact on the accuracy of the sensitivity.

TL;DR: In this paper, a new type of outer loop is proposed for EnKF to improve its ability to handle nonlinear dynamics, especially for long assimilation windows, and a quasi-outer-loop (QOL) algorithm is proposed to improve the ensemble mean so that ensemble perturbations are centered at a more accurate state.

TL;DR: In this article, local ensemble transform Kalman filter (LETKF) and four-dimensional variational data assimilation (4DVAR) schemes are implemented in a quasigeostrophic channel model.

TL;DR: In this paper, the authors used an earth system model of intermediate complexity to investigate how deforestation on various spatial scales affects ground temperature, with an emphasis on the latitudinal temperature response and its underlying mechanisms.

TL;DR: In this paper, the impact of satellite data on both analyses and forecasts has been assessed by diagnosing two sets of analyses and forecast made with and without the use of Satellite data (SAT and NOSAT) within the data assimilation.