Model Error Representation Using the Stochastically Perturbed Hybrid Physical–Dynamical Tendencies in Ensemble Data Assimilation System

• Published in: Applied sciences Vol. 10; no. 24; p. 9010
• Main Authors: Lim, Sujeong, Koo, Myung-Seo, Kwon, In-Hyuk, Park, Seon Ki
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2020
• Subjects: Data assimilation; dynamical tendency; ensemble data assimilation; Humidity; Methods; model error; Partial differential equations; physical tendency; Physics; Standard deviation; stochastic perturbation; stochastic perturbed hybrid tendencies scheme; Weather forecasting
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10249010

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.