Title: Impact of observation thinning and initialization on the LETKF within JMA's global hybrid 4DVar-LETKF data assimilation system

Author: Yoichiro Ota (Japan Meteorological Agency)
Takashi Kadowaki (Japan Meteorological Agency)

The Japan Meteorological Agency (JMA) has been developing a hybrid 4DVar-LETKF (Local Ensemble Transform Kalman Filter) systemas a possible candidate of the next generation operational global data assimilation (DA) system. Firstly, the configuration and the analysis and forecast accuracies of the hybrid DA system compared to the currentoperational DA system will be briefly reviewed. Secondly, we will present some of the recent developments, mainly focusing on the LETKF within the hybrid DA system. To reduce the memory consumption and computational costs of the hybrid DA system, weinvestigatedthe observation data thinning in the LETKF. We will show that the observation data thinning additionally performed in the LETKF to a certain level does not significantly degrade the analysis and forecast accuracies of the hybrid DA system. We will also present the impact of the initialization procedure based on surface pressure tendency analysis(Hamrud et al., 2014) both in the context of the LETKF and the hybrid DA system.Preliminary results show that the application of this initialization reduces the excessive gravity waves in the short-range ensemble forecast initialized from the LETKF analysis ensemble. This leads to the reduction of the innovations of the surface pressure observations and the improvements to the analysis and short-range forecast accuracies.

GMAO Head: Steven Pawson Global Modeling and Assimilation Office NASA Goddard Space Flight Center

Curator: Nikki Privé Last Updated: Feb 9 2015