Representation of Model Error in Convective‐Scale Data Assimilation: Additive Noise Based on Model Truncation Error

Abstract

To account for model error on multiple scales in convective‐scale data assimilation, we incorporate the small‐scale additive noise based on random samples of model truncation error and combine it with the large‐scale additive noise based on random samples from global climatological atmospheric background error covariance. A series of experiments have been executed in the framework of the operational Kilometre‐scale ENsemble Data Assimilation system of the Deutscher Wetterdienst for a 2‐week period with different types of synoptic forcing of convection (i.e., strong or weak forcing). It is shown that the combination of large‐ and small‐scale additive noise is better than the application of large‐scale noise only. The specific increase in the background ensemble spread during data assimilation enhances the quality of short‐term 6‐hr precipitation forecasts. The improvement is especially significant during the weak forcing period, since the small‐scale additive noise increases the small‐scale variability which may favor occurrence of convection. It is also shown that additional perturbation of vertical velocity can further advance the performance of combination.

Assimilating Visible and Infrared Radiances in Idealized Simulations of Deep Convection

“…2) KENDA DATA ASSIMILATION CONFIGURATION The KENDA assimilation system (Schraff et al 2016) is operational at Deutscher Wetterdienst and has been used for a number of assimilation studies (Schomburg et al 2015;Necker et al 2018;Weissmann 2014, 2016;Zeng et al 2019). It is based on a local ensemble transform Kalman filter (LETKF; Hunt et al 2007).…”

“…In contrast to the assimilation experiments by Scheck et al (2020) and Hutt et al (2020), no multiplicative or additive inflation (Zeng et al 2019) of the error covariance matrix is used. To conserve positivity of relative humidity, we employ saturation adjustment in the LETKF (Schraff et al 2016).…”

Assimilation of SEVIRI Water Vapor Channels With an Ensemble Kalman Filter on the Convective Scale

“…The ensemble Kalman filter underestimates the forecast error covariance matrix due to limited ensemble size or model errors (Anderson and Anderson, 1999). This problem is often addressed by covariance inflation (Hamill and Whitaker, 2011;Luo and Hoteit, 2013;Houtekamer and Zhang, 2016;Zeng et al, 2019). In addition to the covariance inflation described in Schraff et al (2016), i.e., multiplicative covariance inflation and relaxation to prior perturbations, the present work implements additive covariance inflation (Mitchell and Houtekamer, 2000;Zeng et al, 2019).…”

Assimilation of radar reflectivity volumes in a pre‐operational framework

“…This different behaviour is likely to be associated with the different weather regimes observed in each period and, in turn, to the presence ( Nov2018 ) or absence ( Sept2018 and Oct2018 ) of convective equilibrium. In fact, as observed in previous studies (Craig et al ., 2012; Davolio et al ., 2017; Zeng et al ., 2019), in non‐equilibrium conditions the impact of assimilation lasts longer than under equilibrium conditions.…”