Ions in the F region ionosphere at 150-400 km altitude consist mainly of molecular NO+ and O2+, and atomic O+. Incoherent scatter (IS) radars are sensitive to the molecular-to-atomic ion density ratio, but its effect to the observed incoherent scatter spectra is almost identical with that of the ion temperature. It is thus very difficult to fit both the ion temperature and the fraction of O+ ions to the observed spectra. In this paper, we introduce a novel combination of Bayesian filtering, smoothness priors, and chemistry modeling to solve for F1 region O+ ion fraction from EISCAT Svalbard IS radar (75.43° corrected geomagnetic latitude) data during the international polar year (IPY) 2007-2008. We find that the fraction of O+ ions in the F1 region ionosphere is controlled by ion temperature and electron production. The median value of the molecular-to-atomic ion transition altitude during IPY varies from 187 km at 16-17 MLT to 208 km at 04-05 MLT. The ion temperature has maxima at 05-06 MLT and 15-16 MLT, but the transition altitude does not follow the ion temperature, because photoionization lowers the transition altitude. A daytime transition altitude maximum is observed in winter, when lack of photoionization leads to very low daytime electron densities. Both ion temperature and the molecular-to-atomic ion transition altitude correlate with the Polar Cap North geomagnetic index. The annual medians of the fitted transition altitudes are 14-32 km lower than those predicted by the International Reference Ionosphere.

We present a new technique for the upcoming tri-static incoherent scatter radar system EISCAT 3D (E3D) to perform a volumetric reconstruction of the 3D ionospheric electric current density vector field, focusing on the feasibility of the E3D system. The input to our volumetric reconstruction technique are estimates of the 3D current density perpendicular to the main magnetic field, $\mathbf{j}_\perp$, and its co-variance, to be obtained from E3D observations based on two main assumptions: 1) Ions fully magnetised above the $E$ region, set to 200 km here. 2) Electrons fully magnetised above the base of our domain, set to 90 km. In this way, $\mathbf{j}_\perp$ estimates are obtained without assumptions about the neutral wind field, allowing it to be subsequently determined. The volumetric reconstruction of the full 3D current density is implemented as vertically coupled horizontal layers represented by Spherical Elementary Current Systems with a built-in current continuity constraint. We demonstrate that our technique is able to retrieve the three dimensional nature of the currents in our idealised setup, taken from a simulation of an active auroral ionosphere using the Geospace Environment Model of Ion-Neutral Interactions (GEMINI). The vertical current is typically less constrained than the horizontal, but we outline strategies for improvement by utilising additional data sources in the inversion. The ability to reconstruct the neutral wind field perpendicular to the magnetic field in the $E$ region is demonstrated to mostly be within $\pm 50$ m/s in a limited region above the radar system in our setup.

During geomagnetically active times, Joule heating in the Lower Thermosphere - Ionosphere is a significant energy source, greatly affecting density, temperature, composition and circulation. At the same time, Joule heating and the associated Pedersen conductivity are amongst the least known parameters in the upper atmosphere in terms of their quantification and spatial distribution, and their parameterization by geomagnetic parameters shows large discrepancies between estimation methodologies, primarily due to a lack of comprehensive measurements in the region where they maximize. In this work we perform a long-term statistical comparison of Joule heating as calculated by the NCAR Thermosphere - Ionosphere - Electrodynamics General Circulation Model (TIE-GCM) and as obtained through radar measurements by the European Incoherent Scatter Scientific Association (EISCAT). Statistical estimates of Joule heating and Pedersen conductivity are obtained from a simulation run over the 11 year period spanning from 2009 until 2019 and from radar measurements over the same period, during times of radar measurements. The results are statistically compared in different Magnetic Local Time sectors and Kp level ranges in terms of median values and percentiles of altitude profiles. It is found that Joule heating and Pedersen conductivity are higher on average in TIE-GCM than in EISCAT for low Kp and are lower than EISCAT for high Kp. It is also found that neutral winds cannot account for the discrepancies between TIE-GCM and EISCAT. Comparisons point towards the need for a Kp-dependent parameterization of Joule heating in TIE-GCM to account for the contribution of small scale effects.

This study presents an improved method to estimate differential energy flux, auroral power and field-aligned current of electron precipitation from incoherent scatter radar data. The method is based on a newly developed data analysis technique that uses Bayesian filtering to fit altitude profiles of electron density, electron temperature, and ion temperature to observed incoherent scatter spectra with high time and range resolutions. The electron energy spectra are inverted from the electron density profiles. Previous high-time resolution fits have relied on the raw electron density, which is calculated from the backscattered power assuming that the ion and electron temperatures are equal. The improved technique is applied to one auroral event measured by the EISCAT UHF radar and it is demonstrated that the effect of electron heating on electron energy spectra, auroral power and upward field-aligned current can be significant at times. Using the fitted electron densities instead of the raw ones may lead to wider electron energy spectra and auroral power up to 75% larger. The largest differences take place for precipitation that produces enhanced electron heating in the upper E region, and in this study correspond to fluxes of electrons with peak energies from 3 to 5 keV. Finally, the auroral power estimates are verified by comparison to the 427.8 nm auroral emission intensity, which show good correlation. The improved method makes it possible to calculate unbiased estimates of electron energy spectra with high time resolution and thereby to study rapidly varying aurora.

Incoherent scatter (IS) radars are invaluable instruments for ionospheric physics, since they observe altitude profiles of electron density (Ne), electron temperature (Te), ion temperature (Ti) and line-of-sight plasma velocity (Vi) from ground. However, the temperatures can be fitted to the observed IS spectra only when the ion composition is known, and resolutions of the fitted plasma parameters are often insufficient for auroral electron precipitation, which requires high resolutions in both range and time. The problem of unknown ion composition has been addressed by means of the full-profile analysis, which assumes that the plasma parameter profiles are smooth in altitude, or follow some predefined shape. In a similar manner, one could assume smooth time variations, but this option has not been used in IS analysis. We propose a plasma parameter fit technique based on Bayesian filtering, which we have implemented as an additional Bayesian Filtering Module (BAFIM) in the GUISDAP analysis package. BAFIM allows us to control gradients in both time and range directions for each plasma parameter separately. With BAFIM we can fit F1 region ion composition together with Ne, Te, Ti, and Vi, and we have reached 4 s/900 m time/range steps in four-parameter fits of Ne, Te, Ti and Vi in E region observations of auroral electron precipitation.

The article proposes a straightforward Kalman filter-based method for computationally efficient ionospheric electron density multi-instrument imaging. The approach uses direct ionospheric measurements, such as ionosondes, and general physical assumptions to estimate the uncertainty associated with the previous reconstructed time step. Therefore the method does not require any electron density model of the ionosphere as a background. The uncertainty is represented by an inverse covariance matrix constructed with Gaussian Markov random fields, allowing the problem to be solved numerically with relatively high resolution. The experiments utilise measurements from dense ground-based GNSS and low Earth orbit beacon satellite receiver networks as well as ionosondes. A synthetic simulation study and real data validation with a specific EISCAT incoherent scatter radar measurement campaign is carried out over Northern European sector. The method can be controlled using parameters with probabilistic and physically realistic interpretations that can be applied to both simulated and real-world data. The results show that the approach is feasible for near real-time regional ionospheric imaging. Especially, the method can be seen as an expansion to local profile measurements field of view, but with sufficient measurement coverage, it also provides information further away from the specific instrument.

We present a new analysis technique for estimating 2D neutral wind pattern using data from a single Scanning Doppler Imager (SDI) or a combination of SDIs, incoherent scatter radars (ISR) and Fabry-Perot interferometers (FPI) within overlapping field-of-views. Neutral wind plays an important role in ionospheric electrodynamics and Ionosphere-Thermosphere coupling, by for example affecting the Joule heating rates and plasma transport. However, reliable and extensive measurements of the neutral wind are rather difficult to obtain. Pointwise measurements can be obtained with ISRs or FPIs, but these measurements can not provide 2D latitude-longitude maps of the neutral wind pattern needed in mesospheric studies. A Scanning Doppler Imager can measure the line-of-sight (LOS) component of the neutral wind in dozens of directions simultaneously. However, further modeling is needed to convert the LOS velocities into 2D velocity maps. Unfortunately these maps are far from unique, as perpendicular velocities (e.g. rotation around the measurement site) are not visible in the LOS data. This can be mitigated by combining data from several nearby SDIs, or a combination of SDIs, FPIs and ISRs. Our analysis technique is based on fitting the LOS data with special vector basis functions called Spherical Elementary Current Systems (SECS). In this approach the wind is naturally divided into curl-free and divergence-free components, and there is no need to provide any explicit boundary conditions on the wind pattern. We present several synthetic test scenarios as well as first results using data from SDIs located in Alaska. Using the synthetic test scenarios we further estimate optimal locations for 2 or 3 SDIs that could be located around the future EISCAT_3D radar system in northern Scandinavia.