Preprints
https://doi.org/10.5194/egusphere-2023-1801
https://doi.org/10.5194/egusphere-2023-1801
15 Aug 2023
 | 15 Aug 2023

Deep Temporal Convolutional Networks for F10.7 Radiation Flux Short-Term Forecasting

Luyao Wang, Hua Zhang, Xiaoxin Zhang, Guangshuai Peng, and Zheng Li

Abstract. F10.7, the solar radiation flux at a wavelength of 10.7 cm (F10.7), is often used as an important parameter input in various space weather models and is also a key parameter for measuring the strength of solar activity levels. Therefore, it is valuable to study and forecast F10.7. In this paper, the temporal convolutional network (TCN) approach in deep learning is used to predict the daily value of F10.7. The F10.7 series from 1957 to 2019 are used, which the datasets from 1957 to 2008 are used for training and the datasets from 2009 to 2019 are used for testing. The results show that the TCN model of prediction F10.7 with a root mean square error (RMSE) from 5.03 to 5.44sfu and correlation coefficients (R) as high as 0.98 during solar cycle 24. The overall accuracy of the TCN forecasts is better than those of the widely used autoregressive (AR) models and the results of the US Space Weather Prediction Center (SWPC) forecasts especially for 2 and 3 days ahead. In addition ,the TCN model is slightly better than other neural network models like backward propagation network (BP) and long short-term memory network (LSTM) in terms of the solar radiation flux F10.7 forecast. The TCN model predicted F10.7 with a lower root mean square error, a higher correlation coefficient and the better overall model prediction.

Luyao Wang, Hua Zhang, Xiaoxin Zhang, Guangshuai Peng, and Zheng Li

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1801', Anonymous Referee #1, 06 Sep 2023
  • RC2: 'Comment on egusphere-2023-1801', Anonymous Referee #2, 13 Sep 2023
Luyao Wang, Hua Zhang, Xiaoxin Zhang, Guangshuai Peng, and Zheng Li
Luyao Wang, Hua Zhang, Xiaoxin Zhang, Guangshuai Peng, and Zheng Li

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Short summary
In this work, we use the solar radiation flux F10.7 data from 1959–2019 and build a deep temporal convolutional network (TCN) prediction model based on deep learning. The results show that the TCN model effectively improves the prediction accuracy of F10.7 radiation flux especially for 2 and 3 days ahead. The TCN model has stronger predictive capability than other classical models, in solar cycle 24.