Modeling subsolar thermospheric waves during a solar flare and penetration electric fields

Thermospheric waves occurring around the time of the 14 July 2000 solar flare were investigated using the Global Ionosphere and Thermosphere Model. The simulation results showed that extensive acoustic and gravity waves were excited by the solar flare in the subsolar region. The subsolar buoyancy period at 400 km altitude was approximately 16 min. Gravity waves with frequencies lower than the buoyancy frequency traveled from the dayside to the nightside and converged in the longitudinal region that was the antisubsolar region when the flare occurred. Acoustic waves with frequencies well above the buoyancy frequency propagated upward from approximately 130 km altitude with increasing amplitudes. The power spectra of the vertical neutral winds in the acoustic branch peaked at a period of approximately 13 min, just below the buoyancy period. The gradient in pressure was the driver of the two waves, while the ion drag caused a phase delay between the variations in the pressure gradient and the vertical velocity in the acoustic waves. An anticorrelation in the high‐frequency component of the vertical neutral wind exists between the subsolar and antisubsolar points at times away from the flare, which was driven by the rapid variations of the ion flows due to the penetration electric field. It is suggested that the penetration of the high‐latitude interplanetary magnetic field electric field to low latitudes can drive neutral waves in the equatorial region through momentum coupling with rapidly changing ion flows.Key PointsThermospheric waves were excited by flare in the subsolar regionGravity waves converged in the antisubsolar region when the flare occurredPenetration E fields drive equatorial neutral wave through neutral‐ion couplingPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110550/1/jgra51512.pd

The effect of background conditions on the ionospheric response to solar flares

The ionospheric response to two X5 solar flares that occurred in different seasons was investigated using the global ionosphere‐thermosphere model. Two questions were investigated: (a) how do different solar flares with similar X‐ray peak intensities disturb the ionosphere during the same background and driving conditions? and (b) how do the geomagnetic field and season affect the ionospheric response to solar flares? These questions were investigated by exchanging the two X5 flares for each other so that there were two pairs of flares with (1) the same background conditions but different irradiances and (2) different background conditions but the same irradiance. The simulations showed that the different solar flares into the same background caused ionospheric disturbances of similar profiles but different magnitudes due to differences in the incident energies, while the same flare spectra caused perturbations of similar magnitudes but different profiles in different backgrounds. On the dayside, the response is primarily controlled by the total integrated energy of the flare, independent of the background. For the northern and southern polar regions, the response is strongly controlled by the solar zenith angle and the incident energy, while the background plays a secondary role. On the nightside, the background conditions, including the magnetic field and season, play a primary role, with the neutral winds and electrodynamics driving the ionospheric response. Key Points Total energy input during a flare determines the magnitude of TEC perturbation TEC perturbation lasted >12 h at the local noon of the time of the flare The TEC perturbation due to a flare is sensitive to the background statePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/107991/1/jgra51102.pd

Comparison of TIDI Line of Sight Winds with ICON-MIGHTI Measurements

The Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics (TIMED) satellite has been making observations of the mesosphere and lower thermosphere (MLT) region for two decades. The TIMED Doppler Interferometer (TIDI) measures the neutral winds using four orthogonal telescopes. In this study, the line of sight (LOS) winds from individual telescopes are compared to the measurements from the Ionospheric Connection Explorer's (ICON's) Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) instrument from 90-100 km altitude during 2020. With the MIGHTI vector winds projected onto the LOS direction of each TIDI telescope, coincidences of the two datasets are found. The four telescopes perform differently and the performance depends on the satellite configuration and local solar zenith angle. Measurements from the coldside telescopes, Telescope 1 (Tel1) and Telescope 2 (Tel2), are better correlated with the MIGHTI winds in general with Tel2 having higher correlation coefficients across all conditions. The performance of Tel1 is comparable to that of Tel2 during backward flight while showing systematic errors larger than the average wind speeds during forward flight. The warmside LOS winds from Telescope 3 (Tel3) and Telescope 4 (Tel4) vary widely in magnitude, especially on the nightside. Compared with MIGHTI winds, the Tel4 measurements have the weakest correlation, while the Tel3 performance is comparable to that of the coldside telescopes during the ascending phase but deteriorates during the descending phase. Based on the TIDI/MIGHTI comparisons, figures of merit are generated to quantify the quality of measurements from individual telescopes in different configurations.Comment: 27 pages, 15 figure

Role of variability in determining the vertical wind speeds and structure

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94947/1/jgra21412.pd

Modeling the ionospheric response to the 28 October 2003 solar flare due to coupling with the thermosphere

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94973/1/rds5664.pd

Understanding the response of the ionosphere‐magnetosphere system to sudden solar wind density increases

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95079/1/jgra20878.pd

Importance of capturing heliospheric variability for studies of thermospheric vertical winds

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95431/1/jgra21925.pd

Thermospheric Weather as Observed by Ground‐Based FPIs and Modeled by GITM

The first long‐term comparison of day‐to‐day variability (i.e., weather) in the thermospheric winds between a first‐principles model and data is presented. The definition of weather adopted here is the difference between daily observations and long‐term averages at the same UT. A year‐long run of the Global Ionosphere Thermosphere Model is evaluated against a nighttime neutral wind data set compiled from six Fabry‐Perot interferometers at middle and low latitudes. First, the temporal persistence of quiet‐time fluctuations above the background climate is evaluated, and the decorrelation time (the time lag at which the autocorrelation function drops to e−1) is found to be in good agreement between the data (1.8 hr) and the model (1.9 hr). Next, comparisons between sites are made to determine the decorrelation distance (the distance at which the cross‐correlation drops to e−1). Larger Fabry‐Perot interferometer networks are needed to conclusively determine the decorrelation distance, but the current data set suggests that it is ∼1,000 km. In the model the decorrelation distance is much larger, indicating that the model results contain too little spatial structure. The measured decorrelation time and distance are useful to tune assimilative models and are notably shorter than the scales expected if tidal forcing were responsible for the variability, suggesting that some other source is dominating the weather. Finally, the model‐data correlation is poor (−0.07 < ρ < 0.36), and the magnitude of the weather is underestimated in the model by 65%.Plain Language SummaryMuch like in the lower atmosphere, weather in the upper atmosphere is harder to predict than climate. Physics‐based models are becoming sophisticated enough that they can in principle predict the weather, and we present the first long‐term evaluation of how well a particular model, Global Ionosphere Thermosphere Model, performs. To evaluate the model, we compare it with a year of data from six ground‐based sites that measure the thermospheric wind. First, we calculate statistics of the weather, such as the decorrelation time, which characterizes how long weather fluctuations persist (1.8 hr in the data and 1.9 hr in the model). We also characterize the spatial decorrelation by comparing weather at different sites. The model predicts that the weather is much more widespread than the data indicates; sites that are 790 km apart have a measured correlation of 0.4, while the modeled correlation is 0.8. In terms of being able to actually predict a weather fluctuation on a particular day, the model performs poorly, with a correlation that is near zero at the low latitude sites, but reaches an average of 0.19 at the midlatitude sites, which are closer to the source that most likely dominates the weather: heating in the auroral zone.Key PointsA long‐term data‐model comparison of day‐to‐day thermospheric variability finds that GITM represents the weather poorly (−0.07 < ρ < 0.36)The average measured decorrelation time of 1.8 hr agrees with the modeled time of 1.9 hrThe weather in GITM contains too little spatial structure, when compared with the measured ∼1,000‐km decorrelation distancePeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148359/1/jgra54757_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148359/2/jgra54757.pd

On Generalized Additive Models for Representation of Solar EUV Irradiance

In the context of space weather forecasting, solar EUV irradiance specification is needed on multiple time scales, with associated uncertainty quantification for determining the accuracy of downstream parameters. Empirical models of irradiance often rely on parametric fits between irradiance in several bands and various solar indices. We build upon these empirical models by using Generalized Additive Models (GAMs) to represent solar irradiance. We apply the GAM approach in two steps: (a) A GAM is fitted between FISM2 irradiance and solar indices F10.7, Revised Sunspot Number, and the Lyman-α solar index. (b) A second GAM is fit to model the residuals of the first GAM with respect to FISM2 irradiance. We evaluate the performance of this approach during Solar Cycle 24 using GAMs driven by known solar indices as well as those forecasted 3 days ahead with an autoregressive modeling approach. We demonstrate negligible dependence of performance on solar cycle and season, and we assess the efficacy of the GAM approach across different wavelengths

Analyzing the hemispheric asymmetry in the thermospheric density response to geomagnetic storms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95274/1/jgra22000.pd