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[1] This paper uses data assimilation to estimate ionospheric state during storm time at subdegree resolution. We use Ionospheric Data Assimilation Four-Dimensional (IDA4D) to resolve the three-dimensional time-varying electron density gradients of the storm-enhanced density poleward plume. By Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE), we infer the three-dimensional plasma velocity from the densities. EMPIRE estimates of ExB drift are made by correcting the Weimer 2000 electric potential model. This is the first time electron densities derived from GPS total electron content (TEC) data are being used to estimate field-aligned and field-perpendicular drifts at such high resolution, without reference to direct drift measurements. The time-varying estimated electron densities are used to construct the ionospheric spatial decorrelation in vertical total electron content (TEC) on horizontal scales of less than 100 km. We compare slant TEC (STEC) estimates to actual STEC GPS observations, including independent unassimilated data. The IDA4D density model of the extreme ionospheric storm on 20 November 2003 shows STEC delays of up to 210 TEC units, comparable to the STEC of the GPS ground stations. Horizontal drifts from EMPIRE are predicted to be northwestward within the storm-enhanced density plume and its boundary, turning northeast at high latitudes. These estimates compare favorably to independent Assimilative Mapping of Ionospheric Electrodynamics-assimilated high-latitude ExB drift estimates. Estimated and measured Defense Meteorological Satellite Program in situ drifts differ by a factor of 2–3 and in some cases have incorrect direction. This indicates that significant density rates of change and more accurate accounting for production and loss may be needed when other processes are not dominant.

[1] The inversion algorithm for Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE) has been created to gain insight into ionospheric dynamics, particularly when direct measurement is unavailable. We extend the capabilities of EMPIRE here, in order to demonstrate its effectiveness on densities obtained from real data. We apply this method to real Ionospheric Data-Assimilation 4-Dimensional (IDA4D) data from storm time measurements, focusing on the midlatitude F2 layer. EMPIRE is used to estimate midlatitude field-aligned and field-perpendicular drifts. The estimated upward drifts from EMPIRE are validated against measurements obtained from the Millstone Hill incoherent scatter radar zenith antenna. The horizontal × drifts are compared to the assimilative mapping of ionospheric electrodynamics (AMIE) model, which estimates drifts from data sources independent of those used in IDA4D. Results show that the direction and magnitude of the × drifts (and therefore the electric fields) may be deduced from imaging based primarily on total electron content data, although altitude variation is not significantly discernible. We also indicate that the initial uplift of the storm-enhanced density may have been more strongly influenced by field-aligned contributions of neutral winds and diffusion than the electric fields.

[1] We develop a new inversion algorithm for Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE). The EMPIRE method uses four-dimensional images of global electron density to estimate the field-aligned neutral wind ionospheric driver when direct measurement is not available. We begin with a model of the electron continuity equation that includes production and loss rate estimates, as well as E x B drift, gravity, and diffusion effects. We use ion, electron, and neutral species temperatures and neutral densities from the Thermosphere Ionosphere Mesosphere Electrodynamics General Circulation Model (TIMEGCM-ASPEN) for estimating the magnitude of these effects. We then model the neutral wind as a power series at a given longitude for a range of latitudes and altitudes. As a test of our algorithm, we have input TIMEGCM electron densities to our algorithm. The model of the neutral wind is computed at hourly intervals and validated by comparing to the "true" TIMEGCM neutral wind fields. We show results for a storm day: 10 November 2004. The agreement between the winds derived from EMPIRE versus the TIMEGCM "true" winds appears to be time-dependent for the day under consideration. This may indicate that the diurnal variation in certain driving processes impacts the accuracy of our neutral wind model. Despite the potential temporal and spatial limits on accuracy, estimating neutral wind speed from measured electron density fields via our algorithm shows great promise as a complement to the more sparse radar and satellite measurements.