Prediction of Ionograms With/Without Spread-F at Hainan by a Combined Spatio-Temporal Neural Network

Abstract

An intelligent high-definition and short-term prediction of ionograms with/without Spread-F for the observation at Hainan (19.5°N, 109.1°E, magnetic 11°N) is presented in this paper, which comprises a spatio-temporal ConvGRU network and a super-resolution EDSR network. Our prediction is based on spatio-temporal features in the ionogram graph only. There are 469,227 ionograms classified into 5 categories, that is, frequency/range/mix/strong range/no Spread F, over a solar cycle (14 years) labeled manually by the research group, and we process these ionograms into two data sets for training the two networks mentioned above. A series of comprehensive experiments have been designed and conducted to determine the optimal super-parameters. Our method inputs 8 consecutive authentic ionograms (lasting 2 hr) and generates the next 2 figures (next 30 min). Remarkably, all predicted figures achieve a high accuracy rate of over 94% in predicting the occurrence of Spread-F.