Ionospheric sporadic E layers (Es) are intense plasma irregularities between 80 and 130 km in altitude and are generally unpredictable. Reconstructing the morphology of sporadic E layers is not only essential for understanding the nature of ionospheric irregularities and many other atmospheric coupling systems, but is also useful for solving a broad range of demands for reliable radio communication of many sectors reliant on ionosphere-dependent decision-making. Despite the efforts of many empirical and theoretical models, a predictive algorithm with both high accuracy and high efficiency is still lacking. Here we introduce a new approach for Sporadic E Layer Forecast using Artificial Neural Networks (SELF-ANN). The prediction engine is trained by fusing observational data from multiple sources, including a high-resolution ERA5 reanalysis dataset, Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) measurements, and integrated data from OMNIWeb. The results show that the model can effectively reconstruct the morphology of the ionospheric E layer with intraseasonal variability by learning complex patterns. The model obtains good performance and generalization capability by applying multiple evaluation criteria. The random forest algorithm used for preliminary processing shows that local time, altitude, longitude, and latitude are significantly essential for forecasting the E-layer region. Extensive evaluations based on ground-based observations demonstrate the superior utility of the model in dealing with unknown information. The presented framework will help us better understand the nature of the ionospheric irregularities, which is a fundamental challenge in upper-atmospheric and ionospheric physics. Moreover, the proposed SELF-ANN can make a significant contribution to the development of the prediction of ionospheric irregularities in the E layer, particularly when the formation mechanisms and evolution processes of the Es layer are not well understood.

The present study investigates the ionospheric F‐layer scintillation responses in the American sector (90°W–0°, ±50° dip latitude) during the Northern Hemisphere winter sudden stratospheric warming events of 2007–2008, 2008–2009, 2009–2010, and 2012–2013. The scintillation data used in this work are obtained from the radio occultation observations of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission. The important results of this study are described as follows: the occurrence altitude and rate of ionospheric F‐layer scintillation over the American sector decrease by an average of 13.48 km and 17%, respectively, during the sudden stratospheric warming (SSW) periods compared with the pre‐SSW periods. We find that ionospheric F‐layer scintillation over the American sector shows similar inhibitory responses to each SSW event, although these SSW events have diversities in some aspects, such as SSW types and solar and geomagnetic activities. To our knowledge, this study is the first to discover that an SSW event under low solar activity, including a minor warming event, can obviously affect the generation of ionospheric irregularities. Additionally, the types of SSW events may influence the decrease in altitude but not the occurrence rate. The decreased plasma vertical drift during prereversal enhancement, together with the changed meridional wind, are the possible mechanisms for the above results. In addition, we find that the enhanced planetary wave and lunar semidiurnal tide activities may be responsible for the decreases in the occurrence rate of ionospheric F‐layer scintillation before the peak warming of the 2008–2009 and 2009–2010 SSW events.

The ionospheric sporadic E layer, the ionospheric irregularities of enhanced electron density, appears in the Earth's ionosphere at altitudes between 90 and 120 km, which supports the real‐world radio communication needs of many sectors reliant on ionosphere‐dependent decision‐making. The prediction of the occurrence of sporadic E layers has been extremely difficult due to the highly complex behavior. Conventional numerical methods are limited because of the inability to extract high‐level information from data. Deep learning is a powerful tool for mining latent features from data, which can theoretically avoid assumptions constraining physical methods. Inspired by feature extraction, we applied deep learning to explore latent relationships between mapping observable lower atmospheric data and ionospheric data from limited observations. The proposed model was trained with high‐resolution ERA5 data during 1 January 2007–30 August 2018 as input and corresponding ionospheric sporadic E data collected from COSMIC RO measurements as output. The results show that the model can learn complex relevance as bridges connecting the input and the desired output and obtain excellent performance and generalization capability by applying multiple evaluation criteria. Additionally, we established several model architecture training methods to explore the performance of the model with different input data. The statistic results show that model inference performance is proportional to the abundance of input information and is impacted by intraseasonal variability. The inference capability of the model achieves the best performance in the June–August (JJA) and December–February (DJF) seasons, which is the exact period of sporadic E layer significant occurrence, although different models are evaluated.

This study presents the observations of midlatitude plasma irregularities over Eastern Asia during a moderate magnetic storm on 16 July 2003. Multi-instrumental observations, including the ground-based ionosondes, the GNSS networks, and the CHAMP and ROCSAT-1 satellites, were utilized to investigate the occurrence and characteristics of midlatitude plasma irregularities. The midlatitude strong spread F (SSF) mainly occurred in the midnight–morning sector as observed by ionosondes over Japan during this storm. SSF was related to plasma depletions, which is also recorded by GNSS network in the form of the enhancement of the rate of total electron content (TEC) change index (ROTI). The possible mechanism for the generation of SSF is that the enhanced eastward electric fields, associated with the prompt penetration electric fields and disturbance dynamo electric fields, cause the uplift and latitudinal extension of equatorial plasma bubbles (EPBs) to generate the observed midlatitude SSF further. Meanwhile, plasma density increased significantly under the influence of this storm. In addition, other common type of spread F, frequency spread F (FSF), was observed over Japan on the non-storm day and/or at high latitude station WK545, which seems to be closely related to the coupling of medium-scale traveling ionospheric disturbances (MSTIDs) and sporadic E (Es) layer. The above results indicate that various types of midlatitude spread F can be produced by different physical mechanisms. It is found that SSF can significantly affect the performance of radio wave propagation compared with FSF. Our results show that space weather events have a significant influence on the day-to-day variability of the occurrence and characteristics of ionospheric F-region irregularities at midlatitudes.

A quasi-biennial oscillation (QBO) disruption is a very rare phenomenon in which QBO westward wind is temporarily interrupted by the occurrence of a band of westward wind in the tropical stratosphere. This phenomenon is important as it could greatly affect the global atmospheric circulation, especially in the mesosphere. Past observational and modelling studies have shown the QBO varying mesospheric diurnal tide, but the mechanism is still not fully understood. In this study, we report on the strong response of mesospheric diurnal tides to the two QBO disruptions that occurred in 2015–2016 and 2019–2020 and their possible mechanisms. The diurnal tidal winds are observed by a meteor radar chain, consisting of meteor radars located at Kunming (25.6° N, 103.8° E), Wuhan (30.5° N, 114.2° E), Mengcheng (33.4° N, 116.5° E), Beijing (40.3° N, 116.2° E), and Mohe (53.5° N, 122.3° E) in China. These observations provide clear evidence that mesospheric diurnal tides are unusually weakened (by ∼ −6 m s⁻¹) during these QBO disruptions, over Kunming, Wuhan, Mengcheng, and Beijing. Using the Specific Dynamics version of the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (SD-WACCM-X) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset, the analysis indicates that the QBO wind affects mid-latitude mesospheric diurnal tides by modulating both the solar radiative absorption by subtropical stratospheric ozone (∼ 5 to 0.5 hPa) and the tidal–gravity wave interaction in the mesosphere (∼ 60 to 100 km). Thus, these unexpected QBO disruptions provide an opportunity to better understand the coupling between climate change and middle-atmospheric dynamics.

Sudden stratospheric warming (SSW) events in Northern Hemisphere winter atmosphere play a significant effect on large ionospheric disturbances. Nevertheless, the SSW effects on small‐scale structure in ionosphere, that is, the ionospheric F layer irregularities, are generally lack of understanding. This work carefully studied the equatorial F layer scintillation during the SSW event extending from 4 January to 5 February in 2013 by using the data of COSMIC/FORMOSAT‐3 (a Constellation Observing System for Meteorology, Ionosphere, and Climate mission). The occurrence rate of ionospheric F layer scintillation was found to be apparently decreased by about 30% during the SSW period. This phenomenon was obviously seen in American sector (90°W–0°) and African sector (0°–90°E). Another prominent feature was the ~0.5 local time hour delay of maximum scintillation occurrence rate associated with the suppression of equatorial F layer irregularities in the first 2 hr after the sunset during the SSW event in January 2013.