Model error representation using the stochastically perturbed hybrid physical–dynamical tendencies in ensemble data assimilation system

Sujeong Lim, Myung Seo Koo, In Hyuk Kwon, Seon Ki Park

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Ensemble data assimilation systems generally suffer from underestimated background error covariance that leads to a filter divergence problem—the analysis diverges from the natural state by ignoring the observation influence due to the diminished estimation of model uncertainty. To alleviate this problem, we have developed and implemented the stochastically perturbed hybrid physical–dynamical tendencies to the local ensemble transform Kalman filter in a global numerical weather prediction model—the Korean Integrated Model (KIM). This approach accounts for the model errors associated with computational representations of underlying partial differential equations and the imperfect physical parameterizations. The new stochastic perturbation hybrid tendencies scheme generally improved the background error covariances in regions where the ensemble spread was not sufficiently expressed by the control experiment that used an additive inflation and the relaxation to prior spread method.

Original languageEnglish
Article number9010
Pages (from-to)1-12
Number of pages12
JournalApplied Sciences (Switzerland)
Volume10
Issue number24
DOIs
StatePublished - 2 Dec 2020

Bibliographical note

Funding Information:
Funding: This research was funded by the Basic Science Research Program through the National Research Foundation of Korea (NRF) grant number 2018R1A6A1A08025520, funded by the Ministry of Education and granted to S. K. Park. It was also partly supported by the 2018 Scholarship of the Ewha Womans University granted to S. Lim. This work has been carried out as part of the R&D project on the development of global numerical weather prediction systems of the Korea Institute of Atmospheric Prediction Systems funded by the Korea Meteorological Administration.

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Dynamical tendency
  • Ensemble data assimilation
  • Model error
  • Physical tendency
  • Stochastic perturbation
  • Stochastic perturbed hybrid tendencies scheme

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