Forecasting of Global Ionosphere Maps With Multi-Day Lead Time Using Transformer-Based Neural Networks

Chung Yu Shih, Cissi Ying tsen Lin, Shu Yu Lin, Cheng Hung Yeh, Yu Ming Huang, Feng Nan Hwang, Chia Hui Chang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Ionospheric total electron content (TEC) is a key indicator of the space environment. Geophysical forcing from above and below drives its spatial and temporal variations. A full understanding of physical and chemical principles, available and well-representable driving inputs, and capable computational power are required for physical models to reproduce simulations that agree with observations, which may be challenging at times. Recently, data-driven approaches, such as deep learning, have therefore surged as means for TEC prediction. Owing to the fact that the geophysical world possesses a sequential nature in time and space, Transformer architectures are proposed and evaluated for sequence-to-sequence TEC predictions in this study. We discuss the impacts of time lengths of choice during the training process and analyze what the neural network has learned regarding the data sets. Our results suggest that 12-layer, 128-hidden-unit Transformer architectures sufficiently provide multi-step global TEC predictions for 48 hr with an overall root-mean-square error (RMSE) of ∼1.8 TECU. The hourly variation of RMSE increases from 0.6 TECU to about 2.0 TECU during the prediction time frame.

Original languageEnglish
Article numbere2023SW003579
JournalSpace Weather
Volume22
Issue number2
DOIs
StatePublished - Feb 2024

Keywords

  • TEC prediction
  • Transformer
  • neural network

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