Forecast Global Ionospheric TEC: Apply Modified U‐Net on VISTA TEC Data Set - Zihan Wang; Shasha Zou; Hu Sun; Yang Chen (Space Weather)
Abstract This work developed a modified U‐Net model (a convolutional network architecture) to predict global Total Electron Content (TEC) maps. The input includes the current global TEC map, the current F10.7, the time history of the Interplanetary Magnetic Field Bz and SYM‐H in the previous 4 days, the Hour of Day, and the Day of Year. The output is the global TEC map several hours or several days ahead. The modified U‐Net was trained and validated on a brand new TEC database, the VISTA (Video Imputation with SoftImpute, Temporal smoothing and Auxiliary data) TEC database. The VISTA TEC maps can reveal important large‐scale TEC structures and preserve mesoscale structures simultaneously. Taking advantage of the new neural network and the new database, our model achieves an root of the mean squared error from 1.2 TECU to 2.4 TECU as the prediction horizon increases from 1 hr to 7 days. In addition, the model could reveal multiscale structures in the predicted TEC maps.