Data Assimilation of Ion Drift Measurements for Estimation of Ionospheric Plasma Drivers

Abstract

During extreme ionospheric storms, plasma behaves very differently than current physics models predict, such as by forming a nighttime ionospheric localized enhancement (NILE). Aiming to decrease the gap between the real measurements and background model estimation, we propose using a data assimilation tool: Estimating Model Parameters Reverse Engineering (EMPIRE) with Kalman filter implementation to ingest ion drift measurements to enable the estimation of electric potential and neutral winds. In this preliminary work, we show results for one storm-time event of EMPIRE estimating global winds and electric potential when augmenting the linear system of observations with measurements of LOS ion drift. We first assimilate Global Navigation Satellite System (GNSS)-based total Electron Content (TEC) measurements with the Sami3 is Another Model of the Ionosphere (SAMI3) first-principles background using the Ionospheric Data Assimilation 4-Dimensional (IDA4D) algorithm. IDA4D yields estimates of plasma density globally at 3-degree resolution and at 5-minute intervals. We then time difference the plasma densities and assimilate those, plus observations of ion drifts speeds from incoherent scatter radar (ISR) located at Massachusetts institute of Technology (MIT) Haystack Observatory. We compare the difference in results between a solution that does not assimilate ion drift (relying on density rates alone versus a solution that does assimilate the ion drift). Three analysis locations are selected: the location of ISR, the location of the NILE, and a site near the magnetic equator along the same longitude line. From the assimilated results, we show that new augmentation of ion velocity measurements impacts the estimated ion motions at all three locations. Further investigation is required to determine the extent to which the ingestion improves the estimation.