Juan Jose Ruiz, University of Buenos Aires, AICS-RIKEN
Ensemble based data assimilation at the mesoscale using large ensembles
CoauthorsGuo-Yuan Lien, Keiichi Kondo, Takemasa Miyoshi
Abstract: Large ensemble data assimilation experiments allows for a more accurate
representation of the structure and temporal evolution of the background
and analysis error covariance matrices. This provides useful information
about the error dynamics in atmospheric systems at different
spatio-temporal scales. Having an accurate representation of the error
covariance matrices also help to improve current localization strategies
in ensemble based data assimilation systems.
In this work, large ensemble data assimilation experiments are performed
using a mesoscale model at 1 km resolution. The SCALE-LETKF mesoscale
data assimilating system is run using 1000 ensemble members and
assimilating radar observations from the Osaka University phased array
radar every 60 seconds. The 13th July 2013 heavy rain case over Kyoto
is used to run the large ensemble experiment.
The background error covariance matrix obtained from the large ensemble
data assimilation experiment is analyzed in order to better understand
its spatial structure and temporal evolution. The dependence of the
error covariance patterns with the state variables is also analyzed.
Results will be presented at the conference.