Variable-dependent and selective multivariate localization for ensemble-variational data assimilation in the tropics

Lee, J. C. K., Amezcua, J. and Bannister, R. N. ORCID: https://orcid.org/0000-0002-6846-8297 (2024) Variable-dependent and selective multivariate localization for ensemble-variational data assimilation in the tropics. Monthly Weather Review, 152 (4). pp. 1097-1118. ISSN 1520-0493 doi: 10.1175/MWR-D-23-0201.1

Abstract/Summary

Two aspects of ensemble localization for data assimilation are explored using the simplified non-hydrostatic ABC model in a tropical setting. The first aspect (i) is the ability to prescribe different localization length-scales for different variables (variable-dependent localization). The second aspect (ii) is the ability to control (i.e., to knock-out by localization) multi-variate error covariances (selective multivariate localization). These aspects are explored in order to shed light on the cross-covariances that are important in the tropics and to help determine the most appropriate localization configuration for a tropical ensemble-variational (EnVar) data assimilation system. Two localization schemes are implemented within the EnVar framework to achieve (i) and (ii). One is called the isolated variable-dependent localization scheme (IVDL) and the other is called the symmetric variable-dependent localization (SVDL) scheme. Multi-cycle Observation System Simulation Experiments are conducted using IVDL or SVDL mainly with a 100-member ensemble, although other ensemble sizes are studied (between 10 and 1000 members). The results reveal that selective multivariate localization can reduce the cycle-averaged root-mean-square error (RMSE) in the experiments when cross-covariances associated with hydrostatic balance are retained and when zonal wind/mass error cross-covariances are knocked-out. When variable-dependent horizontal and vertical localization are incrementally introduced, the cycle-averaged RMSE is further reduced. Overall, the best performing experiment using both variable-dependent and selective multivariate localization leads to a 3-4% reduction in cycle-averaged RMSE compared to the traditional EnVar experiment. These results may inform the possible improvements to existing tropical numerical weather prediction systems which use EnVar data assimilation.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/115233
Identification Number/DOI	10.1175/MWR-D-23-0201.1
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Meteorological Society
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