Global Navigation Satellite System radio occultation signals often show extremely strong levels of scintillation when passing through the ionospheric E region. This is related to sporadic E ( E s )—dense layers of metallic ions that can form in the E region, influencing terrestrial and satellite radio propagation. In our report on the 2007–2014 variation of E region S 4 amplitude fluctuation indices measured by the FORMOSAT‐3/Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) satellite constellation, we find that the spatial and temporal variation of the maximum S 4 index in the E region is proportionate to the occurrence rate of extreme scintillation and by extension, sporadic E . We also find that the monthly median extreme S 4 amplitude fluctuation index in the E region midlatitudes shows a dependence on variation of the El Niño–Southern Oscillation (ENSO) in the troposphere that has not been previously reported. The ENSO‐related variation of the E region median extreme S 4 indices varies closely with the tropopause height, with both parameters lagging the Oceanic Niño Index by roughly 1 to 2 months, while also displaying a similar spectrum of periodicities. This similarity is especially strong in the southern midlatitudes. These results indicate that ENSO signatures can be transmitted to E s formation mechanisms, potentially through modulation of vertically propagating atmospheric tides that alter lower thermospheric wind shears. The end result is the modulation of the interannual variation of extreme E s values by ENSO.

We present a machine-learning (ML) approach for classifying kinematic profiles of elliptical galaxies in the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. Previous studies employing ML to classify spectral data of galaxies have provided valuable insights into morphological galaxy classification. This study aims to enhance the understanding of galaxy kinematics by leveraging ML. The kinematics of 2624 MaNGA elliptical galaxies are investigated using integral field spectroscopy by classifying their one-dimensional velocity dispersion (VD) profiles. We utilized a total of 1266 MaNGA VD profiles and employed a combination of unsupervised and supervised learning techniques. The unsupervised K-means algorithm classifies VD profiles into four categories: flat, decline, ascend, and irregular. A bagged decision trees classifier (TreeBagger)-supervised ensemble is trained using visual tags, achieving 100 ${{\ \rm per\ cent}}$ accuracy on the training set and 88 ${{\ \rm per\ cent}}$ accuracy on the test set. Our analysis identifies the majority (68 ${{\ \rm per\ cent}}$) of MaNGA elliptical galaxies presenting flat VD profiles, which requires further investigation into the implications of the dark matter problem.

The conversion of airglow intensity to volume emission rate (VER) is a common method for studying the ionosphere, but the contribution of the intensity conversion process to the uncertainty in estimated electron or ion density is significant. The Abel inversion is a commonly used method for retrieving VERs from vertical profiles of airglow intensities accumulated along the rays horizontally at the tangent point, but it requires that the intensities converge to zero at their uppermost height, which is often not the case due to observational limitations. In this study, we present a method for optimizing the retrieval of VER from satellite-measured airglow intensities using the techniques of deep learning and Abel inversion. This method can be applied to fill in unobserved or discontinuous observations in airglow intensity profiles with the Chapman function, allowing them to be used with the Abel inversion to determine VERs. We validate the method using limb 135.6 nm airglow emission intensity data from the NASA Global-scale Observations of the Limb and Disk (GOLD) mission. Our training process involves using three hidden layers with varying numbers of neurons, and we compare the performance of the best-performing deep learning models to Abel-transformed results from real-time observations. The combination of Abel inversion and deep learning has the potential to optimize the process of converting intensity to VER and improve the capacity for analyzing ionospheric observations.