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Abstract At altitudes below about 600 km, satellite drag is one of the most important and variable forces acting on a satellite. Neutral mass density predictions in the upper atmosphere are therefore critical for (a) designing satellites; (b) performing adjustments to stay in an intended orbit; and (c) collision avoidance maneuver planning. Density predictions have a great deal of uncertainty, including model biases and model misrepresentation of the atmospheric response to energy input. These may stem from inaccurate approximations of terms in the Navier‐Stokes equations, unmodeled physics, incorrect boundary conditions, or incorrect parameterizations. Two commonly parameterized source terms are the thermal conduction and eddy diffusion. Both are critical components in the transfer of the heat in the thermosphere. Determining how well the major constituents (N2, O2, and O) are as heat conductors will have effects on the temperature and mass density changes from a heat source. This work shows the effectiveness of using the retrospective cost model refinement (RCMR) technique at removing model bias caused by different sources within the Global Ionosphere Thermosphere Model. Numerical experiments, Challenging Minisatellite Payload and Gravity Recovery and Climate Experiment data during real events are used to show that RCMR can compensate for model bias caused by both inaccurate parameterizations and drivers. RCMR is used to show that eliminating model bias before a storm allows for more accurate predictions throughout the storm.
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Abstract We present simulation results of the vertical structure of Large Scale Traveling Ionospheric Disturbances (LSTIDs) during synthetic geomagnetic storms. These data are produced using a one‐way coupled SAMI3/Global Ionosphere Thermosphere Model (GITM) model, where GITM provides thermospheric information to SAMI3 (SAMI3 is Another Model of the Ionosphere), producing LSTIDs. We show simulation results which demonstrate that the traveling atmospheric disturbances (TADs) generated in GITM extend to the topside ionosphere in SAMI3 as LSTIDs. The speed and wavelength (600–700 m/s and 10º–20° latitude) are consistent with LSTID observations in storms of similar magnitudes. We demonstrate the LSTIDs reach altitudes beyond the topside ionosphere with amplitudes of <5% over background which will facilitate the use of plasma measurements from the topside ionosphere to supplement measurements from Global Navigation Satellite System in the study of Traveling Ionospheric Disturbances (TIDs). Additionally, we demonstrate the dependence of the characteristics of these TADs and TIDs on longitude.
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Abstract 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 toe −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%. -
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Abstract In this study, field‐aligned currents (FACs) and ionospheric electric fields on different spatial scales are investigated through the analysis of FAC data from the Swarm satellites and electric field data from the Dynamic Explorer 2, respectively, from all seasons and under all solar wind conditions and varying levels of solar activity. Distributions of the average and variable components of FAC and electric field are the main focuses of this study, where the FAC variability is represented by the standard deviation of FAC in each magnetic latitude/magnetic local time bin and electric field variability is represented by the square root of the sum of squares of standard deviations of magnetic eastward and equatorward components of the electric field. We found that the mean patterns of the FAC and electric field are mainly contributed by the large‐scale (wavelength: ⩾500 km) FAC and electric field. Unlike the average, in addition to the large scale, variabilities of FAC and electric field are not negligible on mesoscale (wavelength: 100–500 km) and small scale (wavelength: 8–100 km), while the FAC variability shows a different scale dependence from the electric field variability. Specifically, for decreasing scale sizes, the FAC variability increases while the electric field variability decreases, suggesting that the strong FACs on small scale and mesoscale do not necessarily correspond to strong ionospheric electric fields on those scales. Further, FAC variabilities on large scale and mesoscale are included into the Global Ionosphere Thermosphere Model (GITM) and the corresponding impacts on Joule heating have been assessed. It was found that, for the conditions studied here, the large‐scale FAC variability may significantly increase the Joule heating (~160% globally) and that the enhancement due to the mesoscale FAC variability is not negligible (~36% globally).
