Impact of Thermospheric Wind Data Assimilation on Ionospheric Electrodynamics Using a Coupled Whole Atmosphere Data Assimilation System

Abstract

AbstractThe upward plasma drift and equatorial ionization anomaly (EIA) in the Earth's ionosphere are strongly influenced by the zonal electric field, which is generated by the wind dynamo. Specification and forecasting of thermospheric winds thus plays an important role in ionospheric weather prediction. In this study, we assess the impact of assimilating thermospheric wind observations from the Michelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) instrument on NASA's Ionospheric CONnection (ICON) explorer satellite on the ionospheric electrodynamics. Empirical localization functions (ELFs) of ICON/MIGHTI zonal and meridional winds are also computed and applied to our data assimilation experiments, enabling improved assimilation of the ICON/MIGHTI wind observations. A set of observing system simulation experiments (OSSEs) are performed using the National Center for Atmospheric Research (NCAR) Whole Atmosphere Community Climate Model with thermosphere‐ionosphere eXtension (WACCMX) with data assimilation provided by the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter. The results show that assimilating the ICON/MIGHTI wind observations with the ELF improves the zonal and meridional wind root mean square error (RMSE) by 16%–18% and 7%–10%, respectively. The improved wind specification further improves the low‐latitude E × B vertical drift RMSE by about 18%. The response of electron densities is slower, and the overall impact is smaller. The improvement of the ionosphere F‐region maximum electron density (NmF2) between ±45° is about 8% after 18 days of data assimilation.