[M-GI28] [EE] Data assimilation: A fundamental approach in geosciences

[MGI28-08] The development of data assimilation in the ionospheric space weather

★Invited papers

*Chia-Hung Chen¹, Charles Lin¹, Tomoko Matsuo^2,3, Jann-Yenq Liu^4,5 (1.Department of Earth Sciences, National Cheng Kung University, Taiwan, 2.Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, USA, 3.Space Weather Prediction Center, National Oceanic and Atmospheric Administration, Boulder, Colorado, USA, 4.Institute of Space Science, National Central University, Chung-Li, Taiwan, 5.Center for Space and Remote Sensing Research, National Central University, Chung-Li, Taiwan)

Keywords:data assimilation, ionospheric forecast model, geomagnetic storm

An ionospheric data assimilation forecast model has been developed by ensemble Kalman filter (EnKF) to adjust ionospheric observations into a thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM). Using this assimilation model, the performances of ionospheric forecast during the geomagnetic storm conditions are further evaluated in this study. Results suggest a rapid assimilation-forecast cycling (10-min in this study) can greatly improve the quality of the model forecast. Furthermore, updating the thermospheric state variables in the coupled thermosphere-ionosphere forecast model in the assimilation step is an important factor in improving the trajectory of model forecasting. Different high-latitude ionospheric convection models, Heelis and Weimer, are further evaluated in different latitude regions. Results show the better forecast in the electron density at the low-latitude region during the storm main phase and the recovery phase. The well reproduced eastward electric field at the low-latitude region by the assimilation model reveals that the electric fields may be an important factor to have the contributions on the accuracy of ionospheric forecast.