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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 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.