10:45 〜 12:15
[PEM12-P10] Study of Model Uncertainty due to Input and Internal Parameters of GAIA
キーワード：電離圏、熱圏、宇宙天気、データ同化、シミュレーション
Prediction of the earth's upper atmosphere is one of the important issues in the space weather research. Variations of ionospheric electron density and thermospheric mass density have significant impacts on the use of GNSS applications, the stable operation of satellites in low earth orbits, and so on. For the purpose of upper atmospheric prediction, we are developing a data assimilative model using a whole atmosphere-ionosphere model called GAIA, with an ensemble Kalman filter method. The assimilation method is to find the most probable solution from observation errors and model uncertainties. Therefore, it is important to reproduce the model uncertainty well in the ensemble in order to improve the performance of the assimilation calculation. It is also useful to understand how much model error exists and what causes it when interpreting model results.
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 extent of spatial distributions and temporal changes in the ionosphere and thermosphere. From this result, we discuss the optimal uncertain parameters to introduce into the data assimilation scheme.
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 extent of spatial distributions and temporal changes in the ionosphere and thermosphere. From this result, we discuss the optimal uncertain parameters to introduce into the data assimilation scheme.