Tsuyuki, T., 2019: Ensemble Kalman filtering based on potential vorticity for atmospheric
multi-scale data assimilation. J. Meteor. Soc. Japan, 97, 1191-1210.
https://doi.org/10.2151/jmsj.2019-067 Graphical Abstract with highlights
JMSJ Editor's Highlight
Plain Language Summary: A multi-scale data assimilation method for the ensemble Kalman filter (EnKF) is proposed and its performance is demonstrated using a shallow water model. The method is based on the conservation and invertibility of potential vorticity. It suppresses spurious error correlations between the balanced and unbalanced parts of dynamical state variables and makes it possible to appropriately address the balance issue of covariance localization.
- The proposed EnKF is superior in the accuracy of the analysis to the conventional EnKF unless the ensemble size is sufficiently large.
- The adjustment of balanced and unbalanced mass variables is necessary for the proposed EnKF to outperform the conventional EnKF.
- The computational cost of the proposed EnKF is about 10% more than that of the conventional EnKF.