Coronal holes are solar regions with low soft X-ray or low extreme ultraviolet intensities. The magnetic fields from coronal holes extend far away from the Sun, and thus they are identified as regions with open magnetic field lines. Coronal holes are concentrated in the polar regions during the sunspot minimum phase, and spread to lower latitude during the rising phase of solar activity. In this work, we identify coronal holes with outward and inward open magnetic fluxes being in the opposite poles during solar quiet period. We find that during the sunspot rising phase, the outward and inward open fluxes perform pole-to-pole trans-equatorial migrations in opposite directions. The migration of the open fluxes consists of three parts: open flux areas migrating across the equator, new open flux areas generated in the low latitude and migrating poleward, and new open flux areas locally generated in the polar region. All three components contribute to the reversal of magnetic polarity. The percentage of contribution from each component is different for different solar cycle. Our results also show that the sunspot number is positively correlated with the lower-latitude open magnetic flux area, but negatively correlated with the total open flux area.

Modern space missions provide a great number of height profiles of ionospheric electron density, measured by the remote sensing technique of radio occultation (RO). The deducing of the profiles from the RO measurements suffers from bias, resulting in negative values of the electron density. We developed a machine learning technique that allows automatic identification of ionospheric layers and avoids the bias problem. An algorithm of convolutional neural networks was applied for the classification of the height profiles. Six classes of the profiles were distinguished on the base of prominent ionospheric layers F2, Es, E, F1 and F3, as well as distorted profiles (Sc). For the models, we selected the ground truth of more than 712 height profiles measured by the COSMIC/Formosat-3 mission above Taiwan from 2011 to 2013. Two different models, a 1D convolutional neural network (CNN) and fully convolutional network (FCN), were applied for classification. It was found that both models demonstrate the best classification performance, with the average accuracy around 0.8 for prediction of the F2 layer-related class and the E layer-related class. The F1 layer is classified by the models with good performance (>0.7). The CNN model can effectively classify the Es layer with an accuracy of 0.75. The FCN model has good classification performance (0.72) for the Sc-related profiles. The lowest performance (<0.4) was found for the F3 layer-related class. It was shown that the more complex FCN model has better classification performance for both large-scale and small-scale variations in the height profiles of the ionospheric electron density.

Recovering and distinguishing different ionospheric layers and signals usually requires slow and complicated procedures. In this work, we construct and train five convolutional neural network (CNN) models: DeepLab, fully convolutional DenseNet24 (FC-DenseNet24), deep watershed transform (DWT), Mask R-CNN, and spatial attention-UNet (SA-UNet) for the recovery of ionograms. The performance of the models is evaluated by intersection over union (IoU). We collect and manually label 6131 ionograms, which are acquired from a low-latitude ionosonde in Taiwan. These ionograms are contaminated by strong quasi-static noise, with an average signal-to-noise ratio (SNR) equal to 1.4. Applying the five models to these noisy ionograms, we show that the models can recover useful signals with IoU > 0.6. The highest accuracy is achieved by SA-UNet. For signals with less than 15% of samples in the data set, they can be recovered by Mask R-CNN to some degree (IoU > 0.2). In addition to the number of samples, we identify and examine the effects of three factors: (1) SNR, (2) shape of signal, (3) overlapping of signals on the recovery accuracy of different models. Our results indicate that FC-DenseNet24, DWT, Mask R-CNN and SA-UNet are capable of identifying signals from very noisy ionograms (SNR < 1.4), overlapping signals can be well identified by DWT, Mask R-CNN and SA-UNet, and that more elongated signals are better identified by all models.

Coronal holes (CHs) are regions with unbalanced magnetic flux and have been associated with open magnetic field (OMF) structures. However, it has been reported that some CHs do not intersect with OMF regions. To investigate the inconsistency, we apply a potential-field (PF) model to construct the magnetic fields of the CHs. As a comparison, we also use a thermodynamic magnetohydrodynamic (MHD) model to synthesize coronal images and identify CHs from the synthetic images. The results from both the potential-field CHs and synthetic MHD CHs reveal that there is a significant percentage of closed field lines extending beyond the CH boundaries and more than 50% (17%) of PF (MHD) CHs do not contain OMF lines. The boundary-crossing field lines are more likely to be found in the lower latitudes during active times. While they tend to be located slightly closer than the non-boundary-crossing ones to the CH boundaries, nearly 40% (20%) of them in PF (MHD) CHs are not located in the boundary regions. The CHs without open field lines are often smaller and less unipolar than those with open field lines. The MHD model indicates higher temperature variations along the boundary-crossing field lines than the non-boundary-crossing ones. The main difference between the results of the two models is that the dominant field lines in the PF and MHD CHs are closed and open field lines, respectively.

The ionosphere is a region of ionized gases, plasmas, populating the upper atmosphere and thermosphere (Kelley, 1989). The ionosphere consists of layers concentrated at specific heights. Radio waves propagate through the ionospheric layers at different group velocities and hence, split into different wave modes according to the electron density, the magnetic field, etc. An experimental ground-based technique of ionosondes has been used for a long time to investigate the vertical profile of the ionospheric ionization represented by the density of free electrons, so-called electron content (EC).The data products of ionosonde measurements are ionograms, which exhibit signals deflected by the ionosphere at various virtual heights as a function of the sounding frequency. The virtual height of the deflection is obtained by assuming that the wave beams are propagating at the speed of light. The sounding frequency at which the virtual height rapidly increases is called the critical frequency, which also corresponds to the local maximum of the EC. In addition, the splitting in the sounding frequency between the signals of different wave modes is related to the local magnetic field. These ionogram parameters can be used for the true height analysis (Titheridge, 1988;Tsai et al., 1995), estimating the magnetic field strength (Piggott & Rawer, 1978) and modelling the electron density higher than the deflection height by the Chapman function (Huang & Reinisch, 2001). Furthermore, the stability of some ionogram interpretation algorithms (Hui et al., 2018;Pulinets, 1995) rely on the intersection point of the ordinary mode and the extraordinary mode signals.