A Physics-Based Data Assimilation Model for the High-Latitude Ionosphere: Importance of Data Assimilation Technique in Determining the Model Drivers

摘要

The high-latitude ionosphere is a very dynamic region in the solar-terrestrial system. The constantly existing weather disturbances in the region can adversely affect numerous military and civilian systems and the accurate specification and forecasting of its plasma and electrodynamic structures have fundamental space weather significance. Presently, most of the space-weather models use limited observations and/or indices to define a set of empirical drivers for physical models to move forward in time. Since the empirical drivers have a "climatological" nature and there are significant physical inconsistencies among various empirical drivers due to independent statistical analysis of different observational data or even different inputs to the drivers, the specifications of high-latitude environment from these models can not truthfully reflect the weather features and unrealistic small- and large-scale structures could be produced. UtahStateUniversity has developed a data assimilation model for the high-latitude ionospheric plasma dynamics and electrodynamics to overcome these hurdles. With a set of physical models and an ensemble Kalman filter, the model can determine the drivers that are most truthful to the real space environment by ingesting data from multiple observations, including magnetic perturbation from more than 100 ground-based magnetometers, magnetic measurements of IRIDIUM satellites, SuperDARN line-of-sight velocity, and in-situ drift velocity measured by DMSP satellites. As a result, the model can realistically capture the small- and large-scale plasma structures and sharp electrodynamic boundaries, thus providing a more accurate picture of the high-latitude space weather. In this paper, we describe how the model determines the most truthful drivers and quantitatively demonstrate the differences between the model results of directly using empirical drivers and those of using the drivers that are determined by an ensemble Kalman filter. With these results, we then elucidate the importance of data assimilation for accurate specification and forecasting of space weather.