A convective‐scale 1, 000‐member ensemble simulation and potential applications. (28th February 2020)

• Record Type: Journal Article
• Authors: Necker, Tobias; Geiss, Stefan; Weissmann, Martin; Ruiz, Juan; Miyoshi, Takemasa; Lien, Guo‐Yuan
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 146:Number 728(2020)
• Page Start: 1423
• Page End: 1442
• Publication Date: 2020-02-28
• Subjects: convective‐scale -- covariance -- data assimilation -- ensemble sensitivity analysis -- localization -- observing system -- sampling error; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.3744
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract: This study presents the first convective‐scale 1, 000‐member ensemble simulation over central Europe, which provides a unique data set for various applications. A comparison with the operational regional 40‐member ensemble of Deutscher Wetterdienst shows that the 1, 000‐member simulation exhibits realistic spread properties overall. Based on this, we discuss two potential applications. First, we quantify the sampling error of spatial covariances of smaller subsets compared with the 1, 000‐member simulation. Knowledge about sampling errors and their dependence on ensemble size is crucial for ensemble and hybrid data assimilation and for developing better approaches for localization in this context. Secondly, we present an approach for estimating the relative potential impact of different observable quantities using ensemble sensitivity analysis. This will provide the basis for consecutive studies developing future observation and data assimilation strategies. Sensitivity studies on the ensemble size indicate that about 200 ensemble members are required to estimate the potential impact of observable quantities with respect to precipitation forecasts. Abstract : This study presents a unique convective‐scale 1, 000‐member ensemble simulation over central Europe. As a first step, the large ensemble is used to investigate sampling errors and localization in convective‐scale data assimilation. Furthermore, we introduce how the relative potential impact of observable quantities can be estimated using spatiotemporal correlations obtained with ensemble sensitivity analysis.