[M-GI24] Data assimilation: A fundamental approach in geosciences

[MGI24-09] Ionospheric data assimilation using a whole atmosphere-ionosphere model GAIA

*Hidekatsu Jin¹, Satoshi Andoh¹, Chihiro Tao¹, Yasunobu Miyoshi², Hiroyuki Shinagawa², Hitoshi Fujiwara³ (1.National Institude of Information and Communications Technology, 2.Kyushu University, 3.Seikei University)

Keywords:Data assimilation, ionosphere, thermosphere, space weather

Prediction of the earth's ionosphere and thermosphere is an important topic of space weather research, since the variations in these regions have impacts on the GNSS applications and communications as well as satellite operations. For the purpose of upper atmospheric prediction, we are developing a data assimilative model using a whole atmosphere-ionosphere model called GAIA and ionospheric observations. We adopt an ensenble Kalman filter approach, in which the most probable solution is obtained in considering observation errors and model uncertainties. It is necessary for the approach being effective that the model uncertainty is well reproduced by the ensemble. For this reason, in this study, we selected several input and internal parameters used in GAIA that could be uncertain, and examined the degree to which these uncertainties contribute to the model output uncertainties. From this result, we discuss the optimal uncertain parameters to introduce into the data assimilation scheme.