First storm‐time plasma velocity estimates from high‐resolution ionospheric data assimilation

Abstract

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.

Ionospheric data assimilation with thermosphere‐ionosphere‐electrodynamics general circulation model and GPS‐TEC during geomagnetic storm conditions

“…Another assimilation model developed by Datta‐Barua et al . [] was used to estimate the storm time E × B drift, one of the important forcings from the electron density observations. Chartier et al .…”

Assimilation of thermospheric measurements for ionosphere‐thermosphere state estimation

“…The EMPIRE algorithm as previously formulated without Kalman filtering was detailed in Appendix of Datta‐Barua et al []. This study builds on the previous work by reconfiguring EMPIRE to correct for horizontal neutral winds (in contrast to the magnetic field‐aligned wind component only), ingest neutral wind data from an FPI, and implement a Kalman filtering scheme.…”

“…Details on how these are used for this study can be found below and in section 3.1. Equations are identical to previous versions of EMPIRE [ Datta‐Barua et al , , , ].…”

“…Two years later, Datta‐Barua et al [] extended EMPIRE's capabilities to make corrections to electric potential and expand estimates to multiple longitudes (3° resolution magnetic latitude and longitude, 20 km altitude resolution). More recently, the EMPIRE technique has been developed to use an existing plasma density and a specification to estimate neutral winds and electric fields at 1° and even 0.5° resolution for longitude and latitude and 50 km resolution for altitude [ Datta‐Barua et al , ]. To achieve such high resolution, they used the Weimer 2000 polar electric potential model as a background field and made corrections to this potential based on plasma density rates.…”

“…Section 2 defines the state and observations in the default case without FPI data ingestion and then with it. Section 3 describes the new adaptations to EMPIRE since Datta‐Barua et al []. It contains the definition of the augmented mapping matrix for FPI ingestion and the Kalman filter used to optimally estimate the basis function coefficients.…”