We investigate 20 post-coronal mass ejection (CME) blobs formed in the post-CME current sheet (CS) that were observed by K-Cor on 2017 September 10. By visual inspection of the trajectories and projected speed variations of each blob, we nd that all blobs except one show irregular \zigzag" trajectories resembling transverse oscillatory motions along the CS, and have at least one oscillatory pattern in their instantaneous radial speeds. Their oscillation periods are ranging from 30 to 91 s and their speed amplitudes from 128 to 902 kms􀀀1. Among 19 blobs, 10 blobs have experienced at least two cycles of radial speed oscillations with dierent speed amplitudes and periods, while 9 blobs undergo one oscillation cycle. To examine whether or not the apparent speed oscillations can be explained by vortex shedding, we estimate the quantitative parameter of vortex shedding, the Strouhal number, by using the observed lateral widths, linear speeds, and oscillation periods of the blobs. We then compare our estimates with theoretical and experimental results from MHD simulations and uid dynamic experiments. We nd that the observed Strouhal numbers range from 0.2 to 2.1, consistent with those (0.15{3.0) from uid dynamic experiments of blu spheres, while they are higher than those (0.15{0.25) from MHD simulations of cylindrical shapes. We thus nd that blobs formed in a post-CME CS undergo kinematic oscillations caused by uid dynamic vortex shedding. The vortex shedding is driven by the interaction of the outward-moving blob having a blu spherical shape with the background plasma in the post-CME CS.

We develop forecast models of daily probabilities of major flares (M- and X-class) based on empirical relationships between photospheric magnetic parameters and daily flaring rates from May 2010 to April 2018. In this study, we consider ten magnetic parameters characterizing size, distribution, and non-potentiality of vector magnetic fields from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) and Geostationary Operational Environmental Satellites (GOES) X-ray flare data. The magnetic parameters are classified into three types: the total unsigned parameters, the total signed parameters, and the mean parameters. We divide the data into two sets chronologically: 70% for training and 30% for testing. The empirical relationships between the parameters and flaring rates are used to predict flare occurrence probabilities for a given magnetic parameter value. Major results of this study are as follows. First, major flare occurrence rates are well correlated with ten parameters having correlation coefficients above 0.85. Second, logarithmic values of flaring rates are well approximated by linear equations. Third, using total unsigned and signed parameters achieved better performance for predicting flares than the mean parameters in terms of verification measures of probabilistic and converted binary forecasts. We conclude that the total quantity of non-potentiality of magnetic fields is crucial for flare forecasting among the magnetic parameters considered in this study. When this model is applied for operational use, it can be used using the data of 21:00 TAI with a slight underestimation of 2–6.3%.

We have developed an algorithm for tracking coronal mass ejection (CME) propagation that allows us to estimate CME speed and its arrival time at Earth. The algorithm may be used either to forecast the CME’s arrival on the day of the forecast or to update the CME tracking information for the next day’s forecast. In our case study, we successfully tracked CME propagation using the algorithm based on g-values of interplanetary scintillation (IPS) observation provided by the Institute for Space- Earth Environmental Research (ISEE). We were able to forecast the arrival time (Δt = 0.30 h) and speed (Δv = 20 km/s) of a CME event on October 2, 2000. From the CME-interplanetary CME (ICME) pairs provided by Cane & Richardson (2003), we selected 50 events to evaluate the algorithm’s forecast capability. Average errors for arrival time and speed were 11.14 h and 310 km/s, respectively. Results demonstrated that g-values obtained continuously from any single station observation were able to be used as a proxy for CME speed. Therefore, our algorithm may give stable daily forecasts of CME position and speed during propagation in the region of 0.2–1 AU using the IPS g-values, even if IPS velocity observations are insufficient. We expect that this algorithm may be widely accepted for use in space weather forecasting in the near future.