EGU2020-6688
https://doi.org/10.5194/egusphere-egu2020-6688
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Applying LSTM and GAN to build a deep learning model (TGAN-TEC) for global ionospheric TEC

zhou chen1,2,3, Yue deng2, and Jing-Song wang3
zhou chen et al.
  • 1Institute of Space Science and Technology, Nanchang University, Nanchang, China (chenzhou760@foxmail.com)
  • 2Department of Physics, University of Texas at Arlington, Arlington TX 76019, USA (yuedeng@uta.edu)
  • 3Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration, Beijing 100081, China (wangjs@cma.gov.cn)

TEC is very important ionospheric parameter, which is commonly used observation for studying various ionospheric physical mechanism and other technological related to ionosphere (i.e. Global Positioning). However, the variation of global TEC is very dynamic, and its spatiotemporal variation is extremely complicated. So in this paper, we try to build a novel global ionospheric TEC (total electron content) predicting model based on two deep learning algorithms: generative adversarial network (GAN) and long short-term memory (LSTM). Training data is from 10-year IGS TEC data, which provide plenty of data for the GAN and LSTM algorithm to obtain the spatial and temporal variation of TEC respectively. The prediction accuracy of this model have been calculated under different levels of geomagnetic activity. The statistic result suggest that the proposed ionospheric model can be used as an efficient tool for ionospheric TEC short-time prediction.

How to cite: chen, Z., deng, Y., and wang, J.-S.: Applying LSTM and GAN to build a deep learning model (TGAN-TEC) for global ionospheric TEC, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6688, https://doi.org/10.5194/egusphere-egu2020-6688, 2020