Two Hours Ahead Prediction of the TEC over China Using a Deep Learning Method

Abstract

In this paper, we forecast the regional total electron content (TEC) over China (0–60° N, 70–140° E) two hours in advance using a deep learning method called pix2pixhd that is based on Generative Adversarial Networks (GAN). We use the International GNSS Service (IGS) TEC maps over China during the 2003–2018 period for training and divide the data into three parts: a training set (2003–2013), a test set (2014–2017), and a validation set (2018). We evaluate the prediction effect of our model using Root Mean Square Error and correlation coefficient and compare our model with IRI-2016. The result demonstrates that our model shows a good performance for TEC prediction in China. Under different geomagnetic and solar activity conditions, the performance of our model is always better than IRI-2016. Analyzing the average difference map between the output of our model and the target IGS TEC map (+2 h), our model behaves well in China including the low-latitude region. In addition, our model behaves better during quiet time and high solar activity years. The successful application of pix2pixhd in forecasting the regional TEC maps over China demonstrates that deep learning methods can solve many geoscience problems, especially for ionospheric parameter forecasting.