10 citations as recorded by crossref.

1. Global ionospheric sporadic E intensity prediction from GNSS RO using a novel stacking machine learning method incorporated with physical observations T. Hu et al. https://doi.org/10.5194/acp-25-11517-2025
2. Editorial: Observations and simulations of layering phenomena in the middle/upper atmosphere and ionosphere B. Yu et al. https://doi.org/10.3389/fspas.2023.1361434
3. Dynamics, chemistry, and modeling studies in the aviation and aerospace transition zone Z. Sheng et al. https://doi.org/10.1016/j.xinn.2025.101012
4. Complexity, Interdisciplinarity, Big Data and AI in Ionosphere Research: Towards a Paradigm Shift S. Radicella https://doi.org/10.3390/atmos17030271
5. Analysis and simulation of the relationship between Es layer and solar activity Y. Zhang et al. https://doi.org/10.1016/j.asr.2025.04.064
6. Analysis of the Relation Between Solar Activity and Parameters of the Sporadic E Layer Y. Zhang et al. https://doi.org/10.3390/atmos16080904
7. Advancing Ionospheric Irregularity Reconstruction With ICON/MIGHTI Wind‐Driven Insights P. Tian et al. https://doi.org/10.1029/2025GL115666
8. Short-term forecast of ionospheric sporadic E intensity fusing physical parameters via deep learning T. Hu et al. https://doi.org/10.1007/s10291-025-01947-0
9. Mechanisms Underlying the Changes in Sporadic E Layers During Sudden Stratospheric Warming H. Zheng et al. https://doi.org/10.3390/atmos15101258
10. The Characteristics and Simulation of Sporadic E Layers in Ascending and Descending Phases of the Solar Cycle at Mid‐Latitude Stations Y. Zhang et al. https://doi.org/10.1029/2024JA033356