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Abstract This work investigates mesoscale structures in the northern high‐latitude thermosphere using an ascending‐descending accelerometry (ADA) technique to determine whether observed in‐track acceleration perturbations are influenced by in‐track winds. The ADA technique is applied to accelerometer measurements from the Challenging Minisatellite Payload mission between 2003 and 2006 during quiet geomagnetic activity, revealing a climatological view of regularly occurring acceleration perturbation structures. The ADA technique reveals a structured acceleration enhancement on the dayside with a strong signature of density dominance confined to a spatial envelope ranging from 8:00 to 17:00 magnetic local time (MLT) and between 72° and 82° magnetic latitude, aligning with past observations of the cusp density enhancement. Additionally, this sector displays a wind perturbation structure with a reversal in direction that coincides with the center of the enhancement. The premidnight quadrant shows strong evidence of wind influence in the acceleration perturbations from 18:00 to 24:00 MLT between 70° and 90° magnetic latitude associated with southward wind perturbations. This suggests that past analyses of this region could have misidentified this structure as a density enhancement by neglecting in‐track wind influences in accelerometry‐derived mass density data sets. The early morning quadrant consists of negative acceleration perturbations attributed to density depletions, with signatures of southward wind perturbations. These mass density perturbations, in conjunction with in‐track wind perturbations, suggest that the coupled ionosphere‐thermosphere mechanisms responsible for the high‐latitude density structure also influence the wind structure. This work is supplemented with TIEGCM simulations to verify the accuracy of ADA and highlight discrepancies between the simulations and observations. 

Abstract The Iterative Driver Estimation and Assimilation (IDEA) data assimilation technique was used with the Whole Atmosphere Model (WAM) to improve neutral density specification in the upper thermosphere. Two different neutral density data sources were examined to enhance the capability of simulating the global thermospheric state. The first were accelerometer estimates of neutral density from the Challenging Mini‐Satellite Payload (CHAMP) satellite. The second were neutral density estimates from the Global Ultraviolet Imager (GUVI) limb‐scan airglow observations aboard the Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite. Due to the intensity of the November 2003 storm, two changes were necessary in WAM. The first was allowing the Kp geomagnetic index to exceed 9 and the second was changing the relationship between Kp and the solar wind parameters used to drive the model. With these changes, results show that IDEA effectively captures the thermospheric neutral density at the CHAMP satellite altitude and follows the time‐dependence through the November 2003 storm period. Furthermore, a cross‐comparison was conducted with the GUVI dayside limb scan measurements. GUVI neutral densities within 270–320 km show the closest agreement with WAM when CHAMP data was assimilated by IDEA. We speculate on the potential for observations from GUVI at 300 km to be used as a data source in the IDEA‐WAM simulations. These simulations demonstrate the utility of the IDEA data assimilation technique with physical models and that using either accelerometer observations or ultraviolet airglow limb measurement during extreme storm periods could be used. 

Abstract The electron/ion density/temperature and ion velocities observed by the ROCSAT-1 and DEMETER satellites are used to examine the daytime wavenumber-4 (WN4) feature in the equatorial/low latitude ionosphere during various months and solar activity levels of 1999–2010. A moving median process has been employed to isolate WN4 features and calculate their amplitudes, while the upward ion drift is used to estimate electric fields. The ROCSAT-1 and DEMETER ion density, ion temperature, and ion velocity generally yield prominent WN4 features over the center of Pacific Ocean, the west side of South America, the center of the Atlantic Ocean, and Southern India. The correlation coefficient between the deviation of ion density and upward ion drift is significant during high solar activity of 1999–2004, while it approaches to zero during low solar activity of 2004–2010. This confirms that the longitudinal variation of the upward ion drift is essential during high solar activity, and the associated amplitude of dynamo eastward electric field is in the range of 0.10–0.14 mV/m, which is 15–19% of daily dynamo electric field. By contrast, the deviation of the ion density and the northward field-aligned ion flow show a clear anti-correlation which yields a maximum coefficient in August during low solar activity but no correlation during high solar activity. These indicate that the longitudinal variation of the meridional field-aligned ion flow could play an important role during low solar activity, and its amplitude is in the range of 10.44–13.91 m/s, which is 10–13% of the ambient ion flows. 

Abstract The numerical forecast methods used to predict ionospheric convective plasma instabilities associated with Equatorial Spread‐F(ESF) have limited accuracy and are often computationally expensive. We test whether it is possible to bypass first‐principle numeric simulations and forecast irregularities using machine learning models. The data are obtained from the incoherent scatter radar at the Jicamarca Radio Observatory located in Lima, Peru. Our models map vertical plasma drifts, time, and solar activity to the occurrence and location of clusters of echoes telltale of ionospheric irregularities. Our results show that these models are capable of identifying the predictive power of the tested inputs, obtaining accuracies around 75%. 

Abstract This study presents a data‐driven approach to quantify uncertainties in the ionosphere‐thermosphere (IT) system due to varying solar wind parameters (drivers) during quiet conditions (Kp < 4) and fixed solar radiation and lower atmospheric conditions representative of 16 March 2013. Ensemble simulations of the coupled Whole Atmosphere Model with Ionosphere Plasmasphere Electrodynamics (WAM‐IPE) driven by synthetic solar wind drivers generated through a multi‐channel variational autoencoder (MCVAE) model are obtained. Applying the polynomial chaos expansion (PCE) technique, it is possible to estimate the means and variances of the QoIs as well as the sensitivities of the QoIs with regard to the drivers. Our results highlight unique features of the IT system's uncertainty: (a) the uncertainty of the IT system is larger during nighttime; (b) the spatial distributions of the uncertainty for electron density and zonal drift at fixed local times present 4 peaks in the evening sector, which are associated with the low‐density regions of longitude structure of electron density; (c) the uncertainty of the equatorial electron density is highly correlated with the uncertainty of the zonal drift, especially in the evening sector, while it is weakly correlated with the vertical drift. A variance‐based global sensitivity analysis suggests that the IMF Bz plays a dominant role in the uncertainty of electron density. A further discussion shows that the uncertainty of the IT system is determined by the magnitudes and universal time variations of solar wind drivers. Its temporal and spatial distribution can be modulated by the average state of the IT system. 

Abstract This paper uses a regional simulation of plasma convective instability in the postsunset equatorial ionosphere together with a global atmosphere/ionosphere/plasmasphere GCM (WAM‐IPE) to forecast irregularities associated with equatorial spreadF(ESF) for 1–2 hr after sunset. First, the regional simulation is initialized and forced using ionosphere state parameters derived from campaign data from the Jicamarca Radio Observatory and from empirical models. The irregularities produced by these simulations are found to be quantitatively similar to those observed. Next, the aforementioned state parameters are replaced with parameters from WAM‐IPE, and the resulting departures between the simulated and observed irregularities are noted. In one of five cases, the forecast failed to accurately predict ESF irregularities due to the late reversal of the zonal thermospheric winds. In four of five cases, significant differences between the observed and predicted prereversal enhancement (PRE) of the background vertical drifts resulted in degraded forecast accuracy. This highlights the need for improved PRE forecasting in the global‐scale model. 

Abstract Spread‐F (SF) is a feature that can be visually observed on ionograms when the ionosonde signals are significantly impacted by plasma irregularities in the ionosphere. Depending on the scale of the plasma irregularities, radio waves of different frequencies are impacted differently when the signals pass through the ionosphere. An automated method for detecting SF in ionograms is presented in this study. Through detecting the existence of SF in ionograms, we can help identify instances of plasma irregularities that are potentially affecting the high‐frequency radio‐wave systems. The ionogram images from Jicamarca observatory in Peru, during the years 2008–2019, are used in this study. Three machine learning approaches have been carried out: supervised learning using Support Vector Machines, and two neural network‐based learning methods: autoencoder and transfer learning. Of these three methods, the transfer learning approach, which uses convolutional neural network architectures, demonstrates the best performance. The best existing architecture that is suitable for this problem appears to be the ResNet50. With respect to the training epoch number, the ResNet50 showed the greatest change in the metric values for the key metrics that we were tracking. Furthermore, on a test set of 2050 ionograms, the model based on the ResNet50 architecture provides an accuracy of 89%, recall of 87%, precision of 95%, as well as Area Under the Curve of 96%. The work also provides a labeled data set of around 28,000 ionograms, which is extremely useful for the community for future machine learning studies. 

Observations of solar terminator waves (STWs) in thermospheric mass density (TMD) measurements above 500 km reveal STWs as dominant features of the upper thermosphere. While previous investigations have shown that STWs in the middle‐lower thermosphere have amplitudes on the order of 6%–8% of the background TMD in that region, this study shows that STWs exhibit a striking amplification with altitude, producing density perturbations of up to a factor of two near 500 km. The study analyzes STWs in TMD data across altitude, solar cycle, and both solstices, leveraging a direct comparative methodology with Challenging Minisatellite Payload, Gravity Recovery and Climate Experiment Follow‐On, and High‐Accuracy Satellite Drag Model. The first detection of a dawn STW with a magnitude comparable to dusk is made near 500 km, contrasting the asymmetry seen at lower altitudes. The newfound prominence of STWs highlights the need for further research into their generation mechanisms, role in geophysical variability, and broader implications for thermospheric modeling and spacecraft operations. 

In this paper, we investigated the seasonal and geomagnetic dependence of the auroral ‐region neutral winds and the tidal components between 90 and 125 km using nearly continuously sampled measurements from the Poker Flat Incoherent Scatter Radar (PFISR) from 2010 to 2019. The average winds show consistent semidiurnal oscillations between 100 and 115 km and diurnal oscillations above 115 km in all seasons with some seasonal and geomagnetic activity dependencies. In general, the semidiurnal oscillation in zonal and meridional directions is strongest in summer and weakest in winter. The diurnal oscillation is strongest in winter and weakest in spring. More details on the seasonal and geomagnetic activity dependencies are revealed in the tidal decomposition results. Tidal decomposition results show eastward mean wind below 115 km in summer, fall, and winter and westward mean wind above 115 km in all seasons. The meridional mean is northward below 115 km and southward above in all seasons. The diurnal amplitudes are small below 110 km and increase with altitude above 110 km in all seasons with larger enhancements in the meridional direction. The semidiurnal amplitudes increase with altitude below 110 km and reach a maximum at around 110 km, then decrease or keep stable (depending on the geomagnetic activity) above 110 km in both directions and all seasons. The diurnal phases shift to earlier times with the increase of geomagnetic activity but show different variations with altitudes in zonal and meridional directions. The semidiurnal phases show a downward progressing trend in both directions and in all seasons. 

This paper presents a study of the global medium‐scale (scales620 km) gravity wave (GW) activity (in terms of zonal wind variance) and its seasonal, local time, and longitudinal variations by employing the enhanced‐resolution (50 km) whole atmosphere model (WAMT254) and space‐based observations for geomagnetically quiet conditions. It is found that the GW hotspots produced by WAMT254 in the troposphere and stratosphere agree well with previously well‐studied orographic and nonorographic sources. In the ionosphere‐thermosphere (IT) region, GWs spread out forming latitudinal band‐like hotspots. During solstices, a primary maximum in GW activity is observed in WAMT254 and GOCE over winter mid‐high latitudes, likely associated with higher‐order waves with primary sources in polar night jet, fronts, and polar vortex. During all the seasons, the enhancement of GWs around the geomagnetic poles as observed by GOCE (at 250 km) is well captured by simulations. WAMT254 GWs in the IT region also show dependence on local time due to their interaction with migrating tides leading to diurnal and semidiurnal variations. The GWs are more likely to propagate up from the MLT region during westward/weakly eastward phase of thermospheric tides, signifying the dominance of eastward GW momentum flux in the MLT. Additionally, as a novel finding, a wavenumber‐4 signature in GW activity is predicted by WAMT254 between 6 and 12 local times in the tropics at 250 km, which propagates eastward with local time. This behavior is likely associated with the modulation of GWs by wave‐4 signal of nonmigrating tides in the lower thermospheric zonal winds.