Ionospheric response during low and high solar activity

We analyse solar extreme ultraviolet (EUV) irradiance observed by the Solar EUV Experiment (SEE) onboard the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite, and solar proxies (the F10.7 index, and Mg-II index), and compare their variability with the one of the global mean Total Electron Content (GTEC). Cross-wavelet analysis confirms the joint 27 days periodicity in GTEC and solar proxies. We focus on a comparison for solar minimum (2007-2009) and maximum (2013-2015) and find significant differences in the correlation during low and high solar activity years. GTEC is delayed by approximately 1-2 days in comparison to solar proxies during both low and high solar activity at the 27 days solar rotation period. To investigate the dynamics of the delay process, Coupled Thermosphere Ionosphere Plasmasphere electrodynamics model simulations have been performed for low and high solar activity conditions. Preliminary results using cross correlation analysis show an ionospheric delay of 1 day in GTEC with respect to the F10.7 index during low and high solar activity.Wir analysieren vom Solar Extreme Ultraviolet Experiment (SEE) an Bord des Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics (TIMED) Satelliten gemessene solare EUV-Irradianzen, solare Proxies (den F10.7-Index und denMg-II-Index), und vergleichen deren Variabilität mit derjenigen des global gemittelten Gesamtelektronengehalts (GTEC). Kreuzwaveletanalysen bestätigen eine gemeinsame Variabilität im Periodenbereich der solaren Rotation (27 Tage). Wir vergleichen insbesondere den Zusammenhang während des solaren Minimums (2007- 2009) und Maximums (2013-2015), wobei signifikante Unterschiede der Korrelation zwischen solaren und ionosphärischen Parametern auftreten. Es tritt eine Verzögerung der Maxima und Minima von GTEC gegenüber denjenigen der solaren Proxies von einem Tag sowohl im solaren Minimum als auch im solaren Maximum auf

Magnetosphere-Ionosphere Coupling Through E-region Turbulence: Anomalous Conductivities and Frictional Heating

Global magnetospheric MHD codes using ionospheric conductances based on laminar models systematically overestimate the cross-polar cap potential during storm time by up to a factor of two. At these times, strong DC electric fields penetrate to the E region and drive plasma instabilities that create turbulence. This plasma density turbulence induces non-linear currents, while associated electrostatic field fluctuations result in strong anomalous electron heating. These two effects will increase the global ionospheric conductance. Based on the theory of non-linear currents developed in the companion paper, this paper derives the correction factors describing turbulent conductivities and calculates turbulent frictional heating rates. Estimates show that during strong geomagnetic storms the inclusion of anomalous conductivity can double the total Pedersen conductance. This may help explain the overestimation of the cross-polar cap potentials by existing MHD codes. The turbulent conductivities and frictional heating presented in this paper should be included in global magnetospheric codes developed for predictive modeling of space weather.Comment: 13 pages, 5 figures, 2nd of two companion paper

Quantifying the Storm Time Thermospheric Neutral Density Variations Using Model and Observations

Accurate determination of thermospheric neutral density holds crucial importance for satellite drag calculations. The problem is twofold and involves the correct estimation of the quiet time climatology and storm time variations. In this work, neutral density estimations from two empirical and three physicsâ based models of the ionosphereâ thermosphere are compared with the neutral densities along the Challenging Microâ Satellite Payload satellite track for six geomagnetic storms. Storm time variations are extracted from neutral density by (1) subtracting the mean difference between model and observation (bias), (2) setting climatological variations to zero, and (3) multiplying model data with the quiet time ratio between the model and observation. Several metrics are employed to evaluate the model performances. We find that the removal of bias or climatology reveals actual performance of the model in simulating the storm time variations. When bias is removed, depending on event and model, storm time errors in neutral density can decrease by an amount of 113% or can increase by an amount of 12% with respect to error in models with quiet time bias. It is shown that using only average and maximum values of neutral density to determine the model performances can be misleading since a model can estimate the averages fairly well but may not capture the maximum value or vice versa. Since each of the metrics used for determining model performances provides different aspects of the error, among these, we suggest employing mean absolute error, prediction efficiency, and normalized root mean square error together as a standard set of metrics for the neutral density.Plain Language SummaryThermospheric neutral density is the largest source of uncertainty in atmospheric drag calculations. Consequently, mission and maneuver planning, satellite lifetime predictions, collision avoidance, and orbit determination depend on the accurate estimation of the thermospheric neutral density. Thermospheric neutral density varies in different timescales. In short timescales, the largest variations occur due to the geomagnetic storms. Several empirical and physicsâ based models of the ionosphereâ thermosphere system are used for estimating the variations in the neutral density. However, the storm time responses from the models are clouded by the climatology (background variations), upon which the effect of geomagnetic storms is superimposed. In this work, we show that it is critical to use reference levels for the neutral density to extract the true performance of the models for the evaluation of the storm time performances. We demonstrate that mean absolute error, prediction efficiency, and normalized root mean square error should be considered together for the performance evaluations, since each of them provides different aspects of the error.Key PointsUsing the average and maximum values of neutral densities to determine the model performances can be misleadingRemoving the quiet time trend from the neutral density reveals the actual performance of the model in simulating the storm time variationsMean absolute error, prediction efficiency, and normalized root mean square error should be considered together for the evaluationsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148396/1/swe20816_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148396/2/swe20816-sup-0001-2018SW002033-SI.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148396/3/swe20816.pd

First E region observations of mesoscale neutral wind interaction with auroral arcs

We report the first observations of E region neutral wind fields and their interaction with auroral arcs at mesoscale spatial resolution during geomagnetically quiet conditions at Mawson, Antarctica. This was achieved by using a scanning Doppler imager, which can observe thermospheric neutral line-of-sight winds and temperatures simultaneously over a wide field of view. In two cases, the background E region wind field was perpendicular to an auroral arc, which when it appeared caused the wind direction within ∼50 km of the arc to rotate parallel along the arc, reverting to the background flow direction when the arc disappeared. This was observed under both westward and eastward plasma convection. The wind rotations occurred within 7–16 min. In one case, as an auroral arc propagated from the horizon toward the local zenith, the background E region wind field became significantly weaker but remained unaffected where the arc had not passed through. We demonstrate through modeling that these effects cannot be explained by height changes in the emission layer. The most likely explanation seems to be the greatly enhanced ion drag associated with the increased plasma density and localized ionospheric electric field associated with auroral arcs. In all cases, the F region neutral wind appeared less affected by the auroral arc, although its presence is clear in the data

High‐latitude Joule heating response to IMF inputs

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94625/1/jgra17822.pd

CEDAR Electrodynamics Thermosphere Ionosphere (ETI) Challenge for systematic assessment of ionosphere/thermosphere models: Electron density, neutral density, NmF2, and hmF2 using space based observations

In an effort to quantitatively assess the current capabilities of Ionosphere/Thermosphere (IT) models, an IT model validation study using metrics was performed. This study is a main part of the CEDAR Electrodynamics Thermosphere Ionosphere (ETI) Challenge, which was initiated at the CEDAR workshop in 2009 to better comprehend strengths and weaknesses of models in predicting the IT system, and to trace improvements in ionospheric/thermospheric specification and forecast. For the challenge, two strong geomagnetic storms, four moderate storms, and three quiet time intervals were selected. For the selected events, we obtained four scores (i.e., RMS error, prediction efficiency, ratio of the maximum change in amplitudes, and ratio of the maximum amplitudes) to compare the performance of models in reproducing the selected physical parameters such as vertical drifts, electron and neutral densities, NmF2, and hmF2. In this paper, we present the results from comparing modeled values against space-based measurements including NmF2 and hmF2 from the CHAMP and COSMIC satellites, and electron and neutral densities at the CHAMP satellite locations. It is found that the accuracy of models varies with the metrics used, latitude and geomagnetic activity level

CEDARâ GEM Challenge for Systematic Assessment of Ionosphere/Thermosphere Models in Predicting TEC During the 2006 December Storm Event

In order to assess current modeling capability of reproducing storm impacts on total electron content (TEC), we considered quantities such as TEC, TEC changes compared to quiet time values, and the maximum value of the TEC and TEC changes during a storm. We compared the quantities obtained from ionospheric models against groundâ based GPS TEC measurements during the 2006 AGU storm event (14â 15 December 2006) in the selected eight longitude sectors. We used 15 simulations obtained from eight ionospheric models, including empirical, physicsâ based, coupled ionosphereâ thermosphere, and data assimilation models. To quantitatively evaluate performance of the models in TEC prediction during the storm, we calculated skill scores such as RMS error, Normalized RMS error (NRMSE), ratio of the modeled to observed maximum increase (Yield), and the difference between the modeled peak time and observed peak time. Furthermore, to investigate latitudinal dependence of the performance of the models, the skill scores were calculated for five latitude regions. Our study shows that RMSE of TEC and TEC changes of the model simulations range from about 3 TECU (total electron content unit, 1 TECU = 1016 el mâ 2) (in high latitudes) to about 13 TECU (in low latitudes), which is larger than latitudinal average GPS TEC error of about 2 TECU. Most model simulations predict TEC better than TEC changes in terms of NRMSE and the difference in peak time, while the opposite holds true in terms of Yield. Model performance strongly depends on the quantities considered, the type of metrics used, and the latitude considered.Key PointsTEC and TEC changes during a storm predicted by ionosphere models were compared with groundâ based GPS TEC measurementsSkill scores (e.g., RMSE, NRMSE, and Yields) were calculated for five latitude regions in the selected eight longitude sectorsModel performance strongly depends on the quantities considered, the type of metrics used, and the latitude consideredPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139943/1/swe20516.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139943/2/swe20516_am.pd

Validation of Ionospheric Specifications During Geomagnetic Storms: TEC and foF2 During the 2013 March Storm Event

To address challenges of assessing space weather modeling capabilities, the CommunityCoordinated Modeling Center is leading a newly establishedInternational Forum for Space WeatherModeling Capabilities Assessment. This paper presents preliminary results of validation of modeled foF2 (F2 layer critical frequency) and TEC (total electron content) during the first selected 2013 March storm event (17 March 2013). In this study, we used eight ionospheric models ranging from empirical to physics-based, coupled ionosphere-thermosphere and data assimilation models. The quantities we considered are TEC and foF2 changes and percentage changes compared to quiet time background, and the maximum and minimum percentage changes. In addition, we considered normalized percentage changes of TEC. We compared the modeled quantities with ground-based observations of vertical Global Navigation SatelliteSystem TEC (provided by Massachusetts Institute of Technology Haystack Observatory) and foF2 data (provided by Global Ionospheric Radio Observatory) at the 12 locations selected in middle latitudes of the American and European-African longitude sectors. To quantitatively evaluate the models’ performance, we calculated skill scores including correlation coefficient, root-mean square error (RMSE), ratio of the modeled to observed maximum percentage changes (yield), and timing error. Our study indicates that average RMSEs of foF2range from about 1 MHz to 1.5 MHz. The average RMSEs of TEC are between ~5 and ~10 TECU (1 TEC Unit= 1016el/m2). dfoF2[%] RMSEs are between 15% and 25%, which is smaller than RMSE of dTEC[%] ranging from30% to 60%. The performance of the models varies with the location and metrics considered