Preprints
https://doi.org/10.5194/npg-2022-5
https://doi.org/10.5194/npg-2022-5
 
01 Mar 2022
01 Mar 2022
Status: this preprint is currently under review for the journal NPG.

Applying prior correlations for ensemble-based spatial localization

Chu-Chun Chang and Eugenia Kalnay Chu-Chun Chang and Eugenia Kalnay
  • Department of Atmospheric and Oceanic Science, University of Maryland, College Park, United States

Abstract. Localization is an essential technique for ensemble-based data assimilations (DA) to reduce the sampling errors due to limited ensembles. Unlike traditional distance-dependent localization, the correlation cutoff method  (Yoshida and Kalnay, 2018; Yoshida 2019) tends to localize the observation impacts based on their background error correlations. This method was initially proposed as a variable localization strategy for coupled systems, but it also can be extensively utilized as a spatial localization. This study introduced and examined the feasibility of the correlation cutoff method as an alternative spatial localization preliminary on the Lorenz (1996) model. We compared the accuracy of the distance-dependent and Abstract. Localization is an essential technique for ensemble-based data assimilations (DA) to reduce the sampling errors correlation-dependent localizations and extensively explored the potential of integrative localization strategies. Our results suggest that the correlation cutoff method can deliver comparable analysis to the traditional localization more efficiently and with a faster spin-up. These benefits would become even more pronounced under a more complicated model, especially when the ensemble and observation sizes are reduced.

Chu-Chun Chang and Eugenia Kalnay

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2022-5', Zheqi Shen, 26 Mar 2022
  • RC2: 'Comment on npg-2022-5', Anonymous Referee #2, 28 Mar 2022

Chu-Chun Chang and Eugenia Kalnay

Chu-Chun Chang and Eugenia Kalnay

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Short summary
This study introduces a new approach for optimizing the model initialization process, effectively reducing unrealistic error estimations and improving weather prediction. Our method uses the prescribed error correlations to limit the observation usage during the model initialization. The experiment results on the simplified atmosphere model show that our method has a similar performance as the traditional method, while it is better in the early stage and is more computationally efficient.