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

Improving estimates of the ionosphere during geomagnetic storm conditions through assimilation of thermospheric mass density

Dynamical changes in the ionosphere and thermosphere during geomagnetic storm times can have a significant impact on our communication and navigation applications, as well as satellite orbit determination and prediction activities. Because of the complex electrodynamics coupling processes during storms, which cannot be fully described with the sparse set of thermosphere–ionosphere (TI) observations, it is crucial to accurately model the state of the TI system. The approximation closest to the true state can be obtained by assimilating relevant measurements into physics-based models. Thermospheric mass density (TMD) derived from satellite measurements is ideal to improve the thermosphere through data assimilation. Given the coupled nature of the TI system, the changes in the thermosphere will also influence the ionosphere state. This study presents a quantification of the changes and improvement of the model state produced by assimilating TMD not only for the thermosphere density but also for the ionosphere electron density under storm conditions. TMD estimates derived from a single Swarm satellite and the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) physics-based model are used for the data assimilation. The results are presented for a case study during the St. Patricks Day storm 2015. It is shown that the TMD data assimilation generates an improvement of the model’s thermosphere density of up to 40% (measured along the orbit of the non-assimilated Swarm satellites). The model’s electron density during the course of the storm has been improved by approximately 8 and 22% relative to Swarm-A and GRACE, respectively. The comparison of the model’s global electron density against a high-quality 3D electron density model, generated through assimilation of total electron content, shows that TMD assimilation modifies the model’s ionosphere state positively and negatively during storm time. The major improvement areas are the mid-low latitudes during the storm’s recovery phase

Ionospheric response to solar extreme ultraviolet radiation variations: comparison based on CTIPe model simulations and satellite measurements

We investigate the delayed ionospheric response using the observed and CTIPe-model-simulated TEC against the solar EUV flux. The ionospheric delay estimated using model-simulated TEC is in good agreement with the delay estimated for observed TEC. The study confirms the model's capabilities to reproduce the delayed ionospheric response against the solar EUV flux. Results also indicate that the average delay is higher in the Northern Hemisphere as compared to the Southern Hemisphere

Improving the ionospheric state estimate during geomagnetic storm time through assimilation of neutral density data

During geomagnetic storms, communication and navigation instruments can be dramatically affected by the rapid changes that occur in the upper atmosphere. The assimilation of data in physics-based models such as the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) model through and ensemble Kalman filter, can improve the representation of the thermosphere-ionosphere (TI) system. Due to the coupled nature of the TI system, the ionosphere is affected by, among others, changes in the neutral atmosphere. In this study, we investigate the capability of the CTIPe model to provide better estimates of the ionosphere by improving its specification of the thermosphere via data assimilation. Here, we assimilate thermospheric mass density (TMD) observations from the Swarm mission normalized to 400 km altitude during the 2015 St. Patrick’s Day storm. The changes that occur in the ionosphere due to assimilation of TMD data are measured by means of the difference between the model results with and without assimilation. To measure the improvement gained with data assimilation, we compare with independent measurements of electron density along the orbit of GRACE (Gravity Recovery and Climate Experiment) satellite, that shows a reduction in the root mean square error (RMSE) by a 22% with respect to the non-assimilation run. The impact on the global scale is measured by comparing the CTIPe model results with the corresponding output of the 3D B-Spline electron density model. The results illustrate that the electron density equatorial region is the most affected region by assimilation of TMD, with an average RMSE reduction of 25% at the assimilation altitude of 400 km

Possible reasons for underestimating Joule heating in global models: E field variability, spatial resolution, and vertical velocity

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95611/1/jgra18607.pd

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

Effects of Swarm neutral mass density assimilation in the ionospheric state estimate during St. Patrick’s Day storm 2015

During geomagnetic storms, rapid changes in the upper atmosphere can dramatically affect communication and navigation instruments. Therefore, it is necessary to monitor these events through a good representation of the thermosphere-ionosphere (TI) system. This can be achieved by assimilating data into physical models by means of an ensemble Kalman filter. Due to the coupled nature of the TI system, the ionosphere can be affected by, among others, changes in the neutral atmosphere, especially during storm conditions. In this study, we investigate the ability of the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics physics-based model (CTIPe) to improve the representation of the ionosphere by assimilating Swarm neutral mass density during the 2015 St. Patrick's Day storm. To gauge the ionospheric improvement due to data assimilation we compare the results of the assimilation and non-assimilation runs with along the orbit electron density measurements of GRACE satellite. The results show a reduction in the root mean square error (RMSE) by a 22% with respect to the non-assimilation run. The impact on the global scale is evaluated by comparing CTIPe results with the corresponding output of the 3D B-Spline electron density model. The electron density results show that the equatorial region is the most affected area by the assimilation of neutral density

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