In this study, we generate He i 1083 nm images from Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images using a novel deep learning method (pix2pixHD) based on conditional Generative Adversarial Networks (cGAN). He i 1083 nm images from National Solar Observatory (NSO)/Synoptic Optical Long-term Investigations of the Sun (SOLIS) are used as target data. We make three models: single-input SDO/AIA 19.3 nm image for Model I, single-input 30.4 nm image for Model II, and double-input (19.3 and 30.4 nm) images for Model III. We use data from 2010 October to 2015 July except for June and December for training and the remaining one for test. Major results of our study are as follows. First, the models successfully generate He i 1083 nm images with high correlations. Second, Model III shows better results than those with one input image in terms of metrics such as correlation coefficient (CC) and root mean square error (RMSE). CC and RMSE between real and synthetic ones for model III with 4 by 4 binnings are 0.88 and 9.49, respectively. Third, synthetic images show well observational features such as active regions, filaments, and coronal holes. This work is meaningful in that our model can produce He i 1083 nm images with higher cadence without data gaps, which would be useful for studying the time evolution of the chromosphere and transition region.

In this study, we forecast hourly relativistic (>2 MeV) electron fluxes at geostationary orbit for the next 72 hr using a deep learning model based on multilayer perceptron. The input data of the model are solar wind parameters (temperature, density and speed), interplanetary magnetic field (|B| and Bz), geomagnetic indices (Kp and Dst), and electron fluxes themselves. All input data are hourly averaged ones for the preceding 72 consecutive hours. We use electron flux data from Geostationary Operational Environmental Satellite (GOES)‐15 and ‐16, and perform a mapping for matching these two data. Total period of the data is from 2011 January to 2021 March (GOES‐15 data for 2011–2017 and GOES‐16 data for 2018–2021). We divide the data into training set (January–August), validation set (September), and test set (October–December) to consider the solar cycle effect. Our main results are as follows. First, our model successfully predicts hourly electron fluxes for the next 72 hr. Second, root‐mean‐square error of our model is from 0.18 (for 1 hr prediction) to 0.68 (for 72 hr prediction), and prediction efficiency is from 0.97 to 0.53, which are much better than those of the previous studies. Third, our model well predicts both diurnal variation and sudden increases of electron fluxes associated with fast solar winds and interplanetary magnetic fields. Our study implies that the deep learning model can be applied to forecasting long‐term sequential space weather events.

In this study, we forecast solar wind speed for the next 3 days with a 6 hr cadence using a deep-learning model. For this we use Solar Dynamics Observatory/Atmospheric Imaging Assembly 211 and 193 Å images together with solar wind speeds for the last 5 days as input data. The total period of the data is from 2010 May to 2020 December. We divide them into a training set (January–August), validation set (September), and test set (October–December), to consider the solar cycle effect. The deep-learning model consists of two networks: a convolutional layer–based network for images and a dense layer–based network for solar wind speeds. Our main results are as follows. First, our model successfully predicts the solar wind speed for the next 3 days. The rms error (RMSE) of our model is from 37.4 km s−1 (for the 6 hr prediction) to 68.2 km s−1 (for the 72 hr prediction), and the correlation coefficient is from 0.92 to 0.67. These results are much better than those of previous studies. Second, the model can predict sudden increase of solar wind speeds caused by large equatorial coronal holes. Third, solar wind speeds predicted by our model are more consistent with observations than those by the Wang–Sheely–Arge–ENLIL model, especially in high-speed-stream regions. It is also noted that our model cannot predict solar wind speed enhancement by coronal mass ejections. Our study demonstrates the effectiveness of deep learning for solar wind speed prediction, with potential applications in space weather forecasting.

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