Preprints
https://doi.org/10.5194/egusphere-2022-434
https://doi.org/10.5194/egusphere-2022-434
 
09 Jun 2022
09 Jun 2022
Status: this preprint is open for discussion.

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

Tobias Necker1, David Hinger1, Philipp Johannes Griewank1, Takemasa Miyoshi2, and Martin Weissmann1 Tobias Necker et al.
  • 1Institut für Meteorologie und Geophysik, Universität Wien, Vienna, Austria
  • 2RIKEN Center for Computational Science, Kobe, Japan

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.

Tobias Necker et al.

Status: open (until 04 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Tobias Necker et al.

Tobias Necker et al.

Viewed

Total article views: 121 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
94 22 5 121 2 3
  • HTML: 94
  • PDF: 22
  • XML: 5
  • Total: 121
  • BibTeX: 2
  • EndNote: 3
Views and downloads (calculated since 09 Jun 2022)
Cumulative views and downloads (calculated since 09 Jun 2022)

Viewed (geographical distribution)

Total article views: 108 (including HTML, PDF, and XML) Thereof 108 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 02 Jul 2022
Download
Short summary
This study investigates vertical localization based on a unique convection-permitting 1000-member ensemble simulation. We discuss requirements for vertical localization by deriving an empirical optimal localization (EOL) that minimizes the sampling error in 40-member sub-sample correlations assuming 1000-member correlations as truth. Results suggest that vertical localization should depend on different aspects that can help to improve vertical localization in future data assimilation systems.