New encoder–decoder convolutional LSTM neural network architectures for next-day global ionosphere maps forecast

Global navigation satellite system (GNSS) signals are significantly affected by the ionosphere. An efficient way to assess the ionospheric effects on GNSS signals is by retrieving the vertical total electron content (VTEC). Here, we propose convolutional recurrent neural network architectures to forecast VTEC based on global ionosphere maps (GIMs) of the days before the prediction period. We proposed modifications to the encoder–decoder convolutional long short-term memory (ED-ConvLSTM) architecture by innovatively using GIMs from several days and exploring the previous day’s GIM. Three new architectures were tested: The first one uses a residual connection to force the network to learn the difference between the previous and the next-day GIM; the second one uses the memory from the encoder to improve the transformation from the previous to the next-day GIM; and a third one uses 3 × 3 kernels on the encoder and 1 × 1 kernels on the decoder. Two experiments were performed using the international GNSS service (IGS) final GIM product from the years 2014–2015 (high solar activity) and 2019–2020 (low solar activity). The proposed architectures obtained better results than the original version of ED-ConvLSTM and other baseline models. We evaluated the influence of the number of GIMs (from one to four days) in the next-day GIM predictions. The results suggest that providing more than one day of GIM to the proposed networks can lead to better prediction metrics.

CODE GIMs are available at https://cddis.nasa.gov/archive/gnss/products/ionex/, and we provided a script on GitHub to download and organize the files.

BiConvLSTM:

Bidirectional ConvLSTM

ConvLSTM:

Convolutional LSTM

ED-ConvLSTM:

Encoder–decoder ConvLSTM

ED-ConvLSTM-ND:

Encoder–decoder ConvLSTM with next-day transformation

ED-ConvLSTM-Res:

Encoder–decoder ConvLSTM with residual connection

GIM:

Global ionosphere map

GNSS:

Global navigation satellite systems

IGS:

International GNSS Service

LSTM:

Long short-term memory

MAE:

Mean absolute error

RNN:

Recurrent neural network

RMSE:

Root mean squared error

ST-ConvLSTM:

Spatiotemporal ConvLSTM

VTEC:

Vertical total electron content

The authors thank IGS, NASA, and CODE for the IONEX files made publicly available. The authors would like to thank the Defense Engineering graduate program of the Brazilian Military Institute of Engineering (IME). M.C.M de Paulo is a doctorate candidate funded by IME. M.P. Ferreira (Grant 306345/2020-0) and H.A. Marques (Grant 431559/2018-0) are funded by the Brazilian National Council for Scientific and Technological Development (CNPq). R.Q. Feitosa thank Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ grant 260631) and CPNq (Grant 317106/2021-0).

This work was funded by the grants acknowledged.

Authors and Affiliations

1. Military Institute of Engineering (IME), Praça Gen. Tibúrcio, 80, Rio de Janeiro, Brazil

   M. C. M. de Paulo, H. A. Marques & M. P. Ferreira

2. Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rua Marquês de São Vicente, 225, Rio de Janeiro, Brazil

   R. Q. Feitosa

Contributions

All authors contributed to the conceptualization and methodology. MCMDP implemented the models and prepared the experiments. All authors wrote and reviewed the manuscript.

Corresponding author

Correspondence to M. C. M. de Paulo.

Conflict of interest

The authors declare that they have no known conflicting financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

Not applicable.

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