Importance of data assimilation technique in defining the model drivers for the space weather specification of the high-latitude ionosphere



[1] The high-latitude ionosphere is a dynamic region in the solar-terrestrial system. The disturbances in this region can adversely affect numerous military and civilian systems, and the accurate specification and forecast of its plasma and electrodynamic structures are important for space weather research. Presently, most of the space weather models use limited observations and/or indices to define a set of empirical drivers for physical models forward in time. The empirical drivers have a “climatological” nature, and there are significant physical inconsistencies among various empirical drivers. Therefore, the specifications of high-latitude environment from these space weather models cannot truthfully reflect the weather features. Utah State University (USU) has developed a data assimilation model for the high-latitude ionosphere plasma dynamics and electrodynamics to overcome these hurdles. With a set of physical models and an ensemble Kalman filter, the model can define the drivers that are most truthful to the real space environment by ingesting data from multiple observations. In this paper, we will provide the details on how the model drivers truthful to real space weather are defined in the developed USU data assimilation model and show the space weather variability of the model outputs driven by these model drivers for various seasonal and geomagnetic conditions. Also, we will present preliminary results of validation and comparison studies to demonstrate that the model results with the optimal magnetospheric drivers determined by data assimilation are the better representations of real space environment.