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In this study, we make a global total electron content (TEC) forecasting using a novel deep learning method, which is based on conditional generative adversarial networks. For training, we use the International GNSS Service (IGS) TEC maps from 2003 to 2012 with 2‐h time cadence. Our model has two input images (IGS TEC map and 1‐day difference map between the present day and the previous day) and one output image (1‐day future map). The model is tested with two data sets: solar maximum period (2013–2014) and solar minimum period (2017–2018). Then, we compare the results of our model with those of 1‐day Center for Orbit Determination in Europe (CODE) prediction model. Our major results can be summarized as follows. First, we successfully apply our model to the forecast of global TEC maps. Second, our model well predicts daily TEC maps with 1 day in advance using only previous TEC maps. The averaged root mean square error, bias, and standard deviation between AI‐generated and IGS TEC maps are 2.74 TECU, −0.32 TECU, and 2.59 TECU, respectively. Third, our model generates some peak structures around equatorial regions. Fourth, our model shows better performance than 1‐day CODE prediction model during both solar maximum and minimum periods. Fifth, another model with additional input data Kp index gives a slight improvement of the results. Our study shows that our deep learning model based on an image translation method will be effective for forecasting of future images using previous data. Plain Language Summary: The main causes of ionospheric disturbance are solar activity and geomagnetic activity. The total electron content (TEC) is one of the important parameters of ionosphere and it can be used to investigate ionospheric disturbances. We develop a global TEC 1‐day forecasting model using a novel deep learning method. For training, we use the International GNSS Service TEC maps from 2003 to 2012 which include both solar maximum and minimum periods. Our model successfully forecasts the global TEC maps 1 day in advance. Key Points: We make a global total electron content (TEC) 1‐day forecasting using a deep learning model based on conditional generative adversarial networksOur model shows better performance than 1‐day Center for Orbit Determination in Europe prediction model during the solar maximum and solar minimum periodsWe successfully apply our model to the forecast of global TEC maps using only previous TEC data [ABSTRACT FROM AUTHOR]

In this study, we make a model, which is called DeepIRI, to generate improved International Reference Ionosphere (IRI) total electron content (TEC) maps by deep learning based on conditional Generative Adversarial Networks. For this we consider 48,901 pairs of IRI TEC maps and International Global Navigation Satellite Systems (GNSS) Service (IGS) TEC maps from 2001 to 2011 for training the model. We evaluate the model by comparing IGS TEC maps and DeepIRI TEC ones from 2013 to 2017. The DeepIRI TEC maps that our model generated are much more consistent with the corresponding IGS TEC maps than the IRI TEC ones. Especially, ionospheric peak structures are successfully generated in DeepIRI TEC maps while they are not in IRI‐2016 ones. From the average differences between IRI and IGS TEC maps, our model greatly improved the IRI TEC at low‐latitude region around the equatorial anomaly. These results show that our model can improve the global TEC prediction ability of the IRI‐2016. Our study suggests a sufficient possibility to generate DeepIRI global TEC maps in near real time if IRI is generated in time. Our approach can be applied to make improved versions of empirical models if more realistic observations are available with a time delay. Plain Language Summary: The International Reference Ionosphere (IRI) is one of the most widely used standard models of the ionosphere. The IRI total electron content (TEC) is very important to monitor ionospheric disturbances. This paper makes a model to improve IRI TEC maps by deep learning based on conditional Generative Adversarial Networks, which is a general purpose solution to resolve image‐to‐image translation problems. We consider many pairs of IRI TEC maps and IGS TEC maps for training the model. Our model greatly improves the global IRI TEC maps, which are very consistent with the observations. Key Points: We make a model to improve IRI TEC maps by deep learning based on conditional Generative Adversarial Networks (cGANs)The global TEC maps that our model generated are much more consistent with the IGS TEC maps than the IRI TEC mapsOur approach can be applied to make improved versions of empirical models if more realistic observations are available with a time delay [ABSTRACT FROM AUTHOR]

We investigate the statistical characteristics of the ion upflow occurrence in association with ion and electron heatings in the polar ionosphere using the European Incoherent Scatter Svalbard Radar during the period of 2000–2015. The ion upflow events are classified as four types: with ion temperature increase (Type 1), with electron temperature increase (Type 2), with both ion and electron temperature increases (Type 3), and without any temperature increase (Type 4). These four types of upflow events are statistically analyzed with various geophysical conditions. We found that the overall occurrence of ion upflow is highest for Type 3 and then followed by Type 2, Type 1, and Type 4. This result indicates that the ion upflow is highly associated with soft particle precipitation induced electron heating, which becomes more effective with simultaneous friction induced ion heating. The statistical characteristics of ion upflow is summarized as follows: (1) It is most highly distributed in the daytime with a double peak structure but a deep minimum at dusk, (2) the highest occurrence appears at about 350‐ to 450‐km altitude for most of local time but extended to higher altitude near the magnetic local noon, (3) the ions mostly reach only up to about 200 km above their initiated altitudes, (4) it tends to increase with magnetic activity, particularly during the daytime, but (5) decreases and distributed at higher altitude with increasing solar activity, and (6) finally, the maximum occurrence appears in December solstice but the minimum in June solstice for most of local times. Key Points: Ambipolar electric field with electron heating may be more effective to produce ion upflow when the ions are also simultaneously heatedDiurnal variation of ion upflow occurrence shows a double peak structure during the day and a deep minimum at duskMaximum occurrence appears in December solstice but minimum in June solstice [ABSTRACT FROM AUTHOR]