Ionospheric data assimilation with thermosphere‐ionosphere‐electrodynamics general circulation model and GPS‐TEC during geomagnetic storm conditions. Issue 6 (17th June 2016) - Chen, C. H.; Lin, C. H.; Matsuo, T.; Chen, W. H.; Lee, I. T.; Liu, J. Y.; Lin, J. T.; Hsu, C. T.
Abstract: The main purpose of this paper is to investigate the effects of rapid assimilation‐forecast cycling on the performance of ionospheric data assimilation during geomagnetic storm conditions. An ensemble Kalman filter software developed by the National Center for Atmospheric Research (NCAR), called Data Assimilation Research Testbed, is applied to assimilate ground‐based GPS total electron content (TEC) observations into a theoretical numerical model of the thermosphere and ionosphere (NCAR thermosphere‐ionosphere‐electrodynamics general circulation model) during the 26 September 2011 geomagnetic storm period. Effects of various assimilation‐forecast cycle lengths: 60, 30, and 10 min on the ionospheric forecast are examined by using the global root‐mean‐squared observation‐minus‐forecast (OmF) TEC residuals. Substantial reduction in the global OmF for the 10 min assimilation‐forecast cycling suggests that a rapid cycling ionospheric data assimilation system can greatly improve the quality of the model forecast during geomagnetic storm conditions. 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. The shorter assimilation‐forecast cycling (10 min in this paper) helps to restrain unrealistic model error growth during the forecast step due to the imbalance among model state variables resulting from an inadequate state update, which in turn leads to a greater forecast accuracy. Key Points: An ionospheric data assimilation system with GPS‐TEC has been developed in this study This data assimilation system is evaluated by disturbed ionosphere during the geomagnetic storm The shorter assimilation time interval can greatly improve the capability of model forecast