Articles | Volume 29, issue 8
https://doi.org/10.5194/angeo-29-1501-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/angeo-29-1501-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Regular paper
| 
31 Aug 2011
Regular paper |
| 31 Aug 2011
Modeling ionospheric foF2 by using empirical orthogonal function analysis
E. A
Institute of Space Physics and Applied Technology, School of Earth and Space Sciences, Peking University, Beijing, China
Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI, USA
D.-H. Zhang
Institute of Space Physics and Applied Technology, School of Earth and Space Sciences, Peking University, Beijing, China
State Key Laboratory of Space Weather, Chinese Academy of Sciences, Beijing, China
Z. Xiao
Institute of Space Physics and Applied Technology, School of Earth and Space Sciences, Peking University, Beijing, China
State Key Laboratory of Space Weather, Chinese Academy of Sciences, Beijing, China
Y.-Q. Hao
Institute of Space Physics and Applied Technology, School of Earth and Space Sciences, Peking University, Beijing, China
A. J. Ridley
Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI, USA
M. Moldwin
Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI, USA
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- Global total electron content prediction performance assessment of the IRI-2016 model based on empirical orthogonal function decomposition S. Li et al. 10.5194/angeo-38-331-2020
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- Estimation of Ionospheric Critical Plasma Frequencies From GNSS‐TEC Measurements Using Artificial Neural Networks V. Otugo et al. 10.1029/2019SW002257
- A new regional total electron content empirical model in northeast China J. Feng et al. 10.1016/j.asr.2016.06.001
- A global empirical orthogonal function model of plasmaspheric electron content P. Chen et al. 10.1016/j.asr.2019.09.039
- An Explainable Dynamic Prediction Method for Ionospheric foF2 Based on Machine Learning J. Wang et al. 10.3390/rs15051256
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