We present a comprehensive solar flare forecast model with a probability and a statistically significant range of daily peak X-ray flux. For this, we consider μ-corrected total unsigned radial magnetic fluxes from the SOHO/MDI and SDO/HMI, and flare lists from GOES from 1996 to 2021. Our model predicts two types of forecast results when a magnetic flux of an active region (AR) is given. First, using a relationship between magnetic fluxes and flaring rates, a probability of C1.0 or greater flares and a probability of M1.0 or greater flares within a day are predicted respectively. Second, a mean (μ) and standard deviation (σ) of daily peak X-ray fluxes are given from a historical distribution between magnetic fluxes and daily peak X-ray fluxes. Using the mean and standard deviation, we provide the statistical range of possible flare sizes. We verify two forecast results by using various performance metrics and investigate the performance depending on the climatology event rate. Based on the metric values, our model can give a better performance than the climatology forecast. Solar flares are considered to be caused by specific triggers and physical mechanisms that have not yet been precisely identified. In addition, there is another perspective that the size of the flare that will occur due to a trigger is close to random because the flaring loop is in a self-organized critical state. Our model can give the simplest forecasting results considering these two perspectives.

In this study, we examine the relationships between the National Oceanic and Atmospheric Administration (NOAA) space weather scale frequencies and the maximum monthly sunspot number in each solar cycle: 1975 to 2020 for radio blackouts (R scales) and solar radiation storms (S scales), 1932 to 2020 for geomagnetic storms (G scales). Our main results are as follows. First, we find that NOAA space weather scale frequencies have strong solar cycle dependencies. Second, we propose new linear relationships between the frequency of certain scales (R1 to R4, and G1 to G4) and the maximum monthly sunspot number. T-test results show that R1 to R3 and G1 to G4 relationships are statistically meaningful, but marginal for R4. Third, our results significantly reduce the root-mean-square error (RMSE) between observed and suggested frequencies compared to the NOAA’s current frequencies. For example, in the case of solar cycle 24, our new prediction (74) for R3 scale is much more consistent with the observational frequency (74) than the NOAA prediction (175), and our prediction (85) for G3 scale is much closer to the observation (40) than the NOAA prediction (200). Our work may provide a useful guideline for advancing the space weather scales.

The Sun-Earth Lagrange point L4, which is called a parking space of space, is considered one of the unique places where solar activity and the heliospheric environment can be observed continuously and comprehensively. The L4 mission affords a clear and wide-angle view of the Sun-Earth line for the study of Sun-Earth connections from remote-sensing observations. The L4 mission will significantly contribute to advancing heliophysics science, improving space weather forecasting capability, extending space weather studies far beyond near-Earth space, and reducing risk from solar radiation hazards on human missions to the Moon and Mars. Our paper outlines the importance of L4 observations by using remote-sensing instruments and advocates comprehensive and coordinated observations of the heliosphere at multi-points including other planned L1 and L5 missions. We mainly discuss scientific perspectives on three topics in view of remote sensing observations: (1) solar magnetic field structure and evolution, (2) source regions of geoeffective solar energetic particles (SEPs), and (3) stereoscopic views of solar corona and coronal mass ejections (CMEs).

In this study, we perform a statistical investigation of the kinematic classification of 4,264 coronal mass ejections (CMEs) from 1996 to 2015 observed by SOHO/LASCO C3. Using the constant acceleration model, we classify these CMEs into three groups: deceleration, constant velocity, and acceleration motion. For this, we devise three different classification methods using fractional speed variation, height contribution, and visual inspection. The main results of this study can be summarized as follows. First, the fractions of three groups depend on the method used. Second, about half of the events belong to the groups of acceleration and deceleration. Third, the fractions of three motion groups as a function of CME speed are consistent with one another. Fourth, the fraction of acceleration motion decreases as CME speed increases, while the fractions of other motions increase with speed. In addition, the acceleration motions are dominant in low speed CMEs whereas the constant velocity motions are dominant in high speed CMEs.