This study presents a data-driven approach to quantify uncertainties in the quantities of interest (QoIs), i.e., electron density, plasma drifts, and neutral winds, 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 March 16th, 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. The means and variances of the QoIs, as well as the sensitivities of the QoIs with respect to the drivers, are estimated by applying the polynomial chaos expansion (PCE) technique. Our results highlight unique features of the IT system’s uncertainty: 1) the uncertainty of the IT system is larger during nighttime; 2) the spatial distributions of the uncertainty for electron density and zonal drift at fixed local times present 4 peaks in the evening sector which is associated with the low density regions of longitude structure of electron density; 3) 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 is further conducted. Results suggest that the IMF Bz plays a dominant role in the uncertainty of the electron density when IMF Bz is 0 or southward, while the solar wind speed plays a dominant role when IMF Bz is northward.

Low Earth orbit (LEO) radio occultation (RO) constellations can provide global electron density profiles (EDPs) to better specify and forecast the ionosphere-thermosphere (I-T) system. To inform future RO constellation design, this study uses comprehensive Observing System Simulation Experiments (OSSEs) to assess the ionospheric specification impact of assimilating synthetic EDPs into a coupled I-T model. These OSSEs use 10 different sets of RO constellation configurations containing 6 or 12 LEO satellites with base orbit parameter combinations of 520 km or 800 km altitude, and 24 degrees or 72 degrees inclination. The OSSEs are performed using the Ensemble Adjustment Kalman Filter implemented in the Data Assimilation Research Testbed and the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM). A different I-T model is used for the nature run, the Whole Atmosphere Model-Ionosphere Plasmasphere Electrodynamics (WAM-IPE), to simulate the period of interest is the St. Patrick’s Day storm on March 13-18, 2015. Errors from models and EDP retrieval are realistically accounted for in this study through distinct I-T models and by retrieving synthetic EDPs through an extension Abel inversion algorithm. OSSE assessment, using multiple metrics, finds that greater EDP spatial coverage leading to improved specification at altitudes 300 km and above, with the 520 km altitude constellations performing best due to yielding the highest observation counts. A potential performance limit is suggested with two 6-satellite constellations. Lastly, close examination of Abel inversion error impacts highlights major EDP limitations at altitudes below 200 km and dayside equatorial regions with large horizontal gradients and low electron density magnitudes.

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 to 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. On a test set of 2050 ionograms the ResNet50 model provides an accuracy of 89 percent, recall of 87 percent, precision of 95 percent as well as Area Under the Curve (AUC) of 96 percent. In addition to the model, this work also provides a labelled dataset of over 28,000 ionograms, which is extremely useful for the community for future machine learning studies.