Guidance on how to improve vertical covariance localization based on a 1000-member ensemble

Author(s)
Tobias Necker, David Hinger, Philipp Griewank, Takemasa Miyoshi, Martin Weissmann
Abstract

The success of ensemble data assimilation systems substantially depends on localization, which is required to mitigate sampling errors caused by modeling background error covariances with undersized ensembles. However, finding an optimal localization is highly challenging as covariances, sampling errors, and appropriate localization depend on various factors. Our study investigates vertical localization based on a unique convection-permitting 1000-member ensemble simulation. 1000-member ensemble correlations serve as truth for examining vertical correlations and their sampling error. We discuss requirements for vertical localization by deriving an empirical optimal localization (EOL) that minimizes the sampling error in 40-member sub-sample correlations with respect to the 1000-member reference. Our analysis covers temperature, specific humidity, and wind correlations on various pressure levels. Results suggest that vertical localization should depend on several aspects, such as the respective variable, vertical level, or correlation type (self- or cross-correlations). Comparing the empirical optimal localization with common distance-dependent localization approaches highlights that finding suitable localization functions bears substantial room for improvement. Furthermore, we discuss the gain of combining different localization approaches with an adaptive statistical sampling error correction.

Organisation(s)
Department of Meteorology and Geophysics
External organisation(s)
Ulmer Fundamental Symmetries Laboratory
Journal
Nonlinear Processes in Geophysics
Volume
30
Pages
13-29
No. of pages
17
ISSN
1023-5809
DOI
https://doi.org/10.5194/npg-30-13-2023
Publication date
01-2023
Peer reviewed
Yes
Austrian Fields of Science 2012
105206 Meteorology
ASJC Scopus subject areas
Geochemistry and Petrology, Geophysics, Statistical and Nonlinear Physics
Portal url
https://ucrisportal.univie.ac.at/en/publications/guidance-on-how-to-improve-vertical-covariance-localization-based-on-a-1000member-ensemble(f87845e2-4154-48a3-b547-3085c0e843b1).html