Title:Use and impact of conventional and satelliteobservations in a three-dimensional variationaldata assimilation system on a cubed-sphere grid

Author: Ji-Hyun Ha (Korea Institute of Atmospheric Prediction Systems)
In-Hyuk Kwon (Korea Institute of Atmospheric Prediction Systems)
Sangil Kimand JiHyeKwun (Korea Institute of Atmospheric Prediction Systems)

Korea Institute of Atmospheric Prediction Systems (KIAPS) has been developing a three-dimensional variational data assimilation (3DVAR) system and successfully implemented to the KIAPS Integrated Modeling (KIM) system that has fully unstructured quadrilateral meshes based on the cubed-sphere grid. Until now, we have assimilatedthe conventional data (radiosonde, surface and aircraft data)using the bi-linear and log-pressure interpolation in order to map the model data to the location of observations. When compared with the experiment without the data assimilation, the analysis using the 3DVAR system shows good results in the northern hemisphere, where the conventional observation networks are dense, in the southern hemisphere, however, the conventional data assimilation has little or no impact as we expected. The previous studies noted that the use of satellite data is the most effective way to reduce forecast error. Therefore, in recent, we aredeveloping the assimilation of the satellite radiance data, in which the observation operator is constructed from the Radiative Transfer for TOVS (RTTOV) model. In the workshop, we will present our framework of the conventional and satellite data assimilation system and discuss its performance.

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

Curator: Nikki Privé Last Updated: Feb 9 2015