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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38 citations as recorded by crossref.
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- Modeling ionospheric foF2 response during geomagnetic storms using neural network and linear regression techniques M. Tshisaphungo et al. https://doi.org/10.1016/j.asr.2018.03.025
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- Modeling of local ionospheric time varying characteristics based on singular value decomposition over low-latitude GPS stations R. Neeli et al. https://doi.org/10.1007/s10509-018-3403-1
- Modelling total electron content during geomagnetic storm conditions using empirical orthogonal functions and neural networks J. Uwamahoro & J. Habarulema https://doi.org/10.1002/2015JA021961
- Variation of hmF2 and NmF2 deduced from DPS-4 over Multan (Pakistan) and their comparisons with IRI-2012 & IRI-2016 during the deep solar minimum between cycles 23 & 24 M. Ameen et al. https://doi.org/10.1016/j.asr.2018.01.043
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- Assessment of IRI2020 model accuracy in predicting ionospheric parameters: Insights from multiple ionosonde stations E. Seba & S. Poedts https://doi.org/10.1016/j.asr.2024.09.026
- Modelling ionospheric vertical drifts over Africa low latitudes using Empirical Orthogonal functions and comparison with climatological model M. Dubazane et al. https://doi.org/10.1016/j.asr.2017.10.024
- Global total electron content prediction performance assessment of the IRI-2016 model based on empirical orthogonal function decomposition S. Li et al. https://doi.org/10.5194/angeo-38-331-2020
- Modeling and analysis of ionosphere TEC over China and adjacent areas based on EOF method S. Li et al. https://doi.org/10.1016/j.asr.2019.04.018
- Constructing a Regional Ionospheric TEC Model in China with Empirical Orthogonal Function and Dense GNSS Observation B. Xiong et al. https://doi.org/10.3390/rs15215207
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- An Explainable Dynamic Prediction Method for Ionospheric foF2 Based on Machine Learning J. Wang et al. https://doi.org/10.3390/rs15051256
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