Accounting for Flow-dependence in the Background Error Variance within the JMA Global Four-dimensional Variational Data Assimilation System

Abstract

The background error variance is modified within the Japan Meteorological Agency (JMA) global fourdimensional variational (4D-Var) data assimilation system; the impact is investigated. In the operational 4D-Var the background error variance is assumed to be constant globally at each vertical level for each variable. This study performs additional data assimilation experiments by allowing horizontal and temporal inhomogeneities in the background error variance. A new method with a similar idea to the one for the dynamic quality control is proposed in this study to account for temporal variations, i.e., flow-dependence. It turns out that the impact by the horizontal and temporal inhomogeneities is small and that including the horizontal variations does not necessarily improve the forecast. However, considering flow-dependence indicates generally positive impact.

An extended specification of flow‐dependent background error variances in the Météo‐France global 4D‐Var system

“…Studies have been conducted on hybrid schemes (Hamill and Snyder, 2000;Lorenc, 2003;Wang et al, 2008) which combine the static 3D-Var B matrix with the flowdependent EnKF B matrix. Other ideas are also described in works by Purser et al (2003), Lindskog et al (2006) and Miyoshi and Kadowaki (2008), for instance. More recently, Buehner et al (2010aBuehner et al ( , 2010b presented promising results from using in a near-operational variational system the background-error covariances that are estimated from the ensemble of background states produced by an EnKF.…”

Spatially extended estimates of analysis and short-range forecast error variances

“…true analysis error variance) or forecast states (true forecast error variance) is therefore critical for assessing the performance of analysis and forecast systems. In addition, the estimates of AFEV provide references for tuning initial ensemble perturbations Kalnay, 1993, 1997;Molteni et al, 1996;Wei et al, 2008;Feng et al, 2014) and background error variances in data assimilation (DA) schemes (Fisher, 1996;Miyoshi and Kadowaki, 2008), respectively.…”