Abstract
Ensemble data assimilation estimates the initial conditions and the flow-dependent background error covariance using observations and ensemble forecasts. The ensemble background error covariance represents the model uncertainty, but it is usually underestimated due to insufficient ensemble size and model errors. Consequently, analysis overtrusts the model forecasts and ignores observations. To solve this problem, we implemented the stochastically perturbed hybrid tendencies scheme to the local ensemble transform Kalman filter in a global numerical weather prediction model—the Korean Integrated Model. It describes the model uncertainties from the computational representations of underlying partial differential equations and the imperfect physical parameterizations, simultaneously. As a result, the new stochastic perturbation scheme leads to an increase in ensemble spread and a decrease in the ensemble mean error, especially in the troposphere.
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Acknowledgements
This work is supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1A6A1A08025520) and by the NRF grant funded by the Korean government (MSIT) (NRF-2021R1A2C1095535).
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Lim, S., Park, S.K. (2023). Model Error Representations Using the Covariance Inflation Methods in Ensemble Data Assimilation System. In: Park, S.K. (eds) Numerical Weather Prediction: East Asian Perspectives. Springer Atmospheric Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-40567-9_12
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