Assimilation of thermospheric measurements for ionosphere‐thermosphere state estimation

We develop a method that uses data assimilation to estimate ionospheric-thermospheric (IT) states during mid-latitude nighttime storm conditions. The algorithm Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE) uses time-varying electron densities in the F-region, derived primarily from total electron content data, to estimate two drivers of the IT: neutral winds and electric potential. A Kalman filter is used to update background models based on ingested plasma densities and neutral wind measurements. This is the first time a Kalman filtering technique is used with the EMPIRE algorithm and the first time neutral wind measurements from 630.0-nm Fabry-Perot interferometers (FPI) are ingested to improve estimates of storm time ion drifts and neutral winds. The effects of assimilating remotely sensed neutral winds from FPI observations are studied by comparing results of ingesting: electron densities (N) only, N plus half the measurements from a single FPI, and then N plus all of the FPI data. While estimates of ion drifts and neutral winds based on N give estimates similar to the background models, this study's results show that ingestion of the FPI data can significantly change neutral wind and ion drift estimation away from background models. In particular, once neutral winds are ingested, estimated neutral winds agree more with validation wind data and estimated ion drifts in the magnetic field parallel direction are most sensitive as compared to field-perpendicular-zonal and -meridional directions. Also, data assimilation with FPI measurements helps provide insight into the effects of contamination to 630.0-nm emissions experienced during geomagnetic storms.