To measure the magnetic field strength in the solar corona, we examined 10 fast (≥ 1000 km s −1 ) limb CMEs which show clear shock structures in SOHO/LASCO images. By applying piston-shock relationship to the observed CME's standoff distance and electron density compression ratio, we estimated the Mach number, Alfven speed, and magnetic field strength in the height range 3 to 15 solar radii (R s ). Main results from this study are: (1) the standoff distance observed in solar corona is consistent with those from a magnetohydrodynamic (MHD) model and near-Earth observations; (2) the Mach number as a shock strength is in the range 1.49 to 3.43 from the standoff distance ratio, but when we use the density compression ratio, the Mach number is in the range 1.47 to 1.90, implying that the measured density compression ratio is likely to be underestimated due to observational limits; (3) the Alfven speed ranges from 259 to 982 km s −1 and the magnetic field strength is in the range 6 to 105mG when the standoff distance is used; (4) if we multiply the density compression ratio by a factor of 2, the Alfven speeds and the magnetic field strengths are consistent in both methods; (5) the magnetic field strengths derived from the shock parameters are similar to those of empirical models and previous estimates.

We analyze the high-energy particle emission from the Sun in two extreme solar particle eventsin which protons are accelerated to relativistic energies and can cause a significant signal even in the ground-based particle detectors. Analysis of a relativistic proton event is based on modeling of the particle transport and interaction, from a nearSun source through the solar wind and the Earth's magnetosphere and atmosphere to a detector on the ground. This allows us to deduce the time profile of the proton source at the Sun and compare it with observed electromagnetic emissions. The 1998 May 2 event is associated with aflare and a coronal mass ejection (CME), which were well observed by the Nançay Radioheliograph, thusthe images of theradio sources are available. For the 2003 November 2 event, the low corona images of the CME liftoff obtained at the Mauna Loa Solar Observatoryare available. Those complementary data sets are analyzed jointly with the broadband dynamic radio spectra, EUV images, and other data available for both events. We find a common scenario for both eruptions, including the flare's dual impulsive phase, the CME-launch-associated decimetric-continuum burst, and the late, low-frequency type III radio bursts at the time of the relativistic proton injection into the interplanetary medium. The analysis supports the idea that the two considered events start with emission of relativistic protons previously accelerated during the flare and CME launch, then trapped in large-scale magnetic loops and later released by the expanding CME.

[1] In this study we have made a forecast evaluation of geoeffective coronal mass ejections (CMEs) by using frontside halo CMEs and the magnetospheric ring current index, Dst. This is the first time, to our knowledge, that an attempt has been made to construct contingency tables depending on the geoeffectiveness criteria as well as to estimate the probability of CME geoeffectiveness depending on CME location and/or speed. For this, we consider 7742 CMEs observed by SOHO/LASCO and select 305 frontside halo CMEs with their locational information from 1997 to 2003 using SOHO/ EIT images and GOES data. To select CME-geomagnetic storm (Dst < À50 nT) pairs, we adopt a CME propagation model for estimating the arrival time of each CME at the Earth and then choose the nearest Dst minimum value within the window of ±24 hours. For forecast evaluation, we present contingency tables to estimate statistical parameters such as probability of detection yes (PODy) and false alarm ratio (FAR). We examine the probabilities of CME geoeffectiveness according to their locations, speeds, and their combination. From these studies, we find that (1) the total probability of geoeffectiveness for frontside halo CMEs is 40% (121/305); (2) PODys for the location (L < j50°j) and the speed (>400 km s À1 ) are estimated to be larger than 80% but their FARs are about 60%; (3) the most probable areas (or coverage combinations) whose geoeffectiveness fraction is larger than the mean probability ($40%), are 0°< L < +30°for slower speed CMEs ( 800 km s À1 ), and À30°< L < +60°for faster CMEs (>800 km s À1 ); (4) when the most probable area is adopted as the new criteria, the PODy becomes slightly lower, but all other statistical parameters such as FAR and bias are significantly improved. Our results can give us some criteria to select geoeffective CMEs with the probability of geoeffectiveness depending on the location, speed, and their combination.

In this Letter, we apply deep-learning methods to the image-to-image translation from solar magnetograms to solar ultraviolet (UV) and extreme UV (EUV) images. For this, We consider two convolutional neural network models with different loss functions, one (Model A) is with L1 loss (L 1), and the other (Model B) is with L 1 and cGAN loss (L cGAN). We train the models using pairs of Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) nine-passband (94, 131, 171, 193, 211, 304, 335, 1600, and 1700 Å) UV/EUV images and their corresponding SDO/Helioseismic and Magnetic Imager (HMI) line-of-sight (LOS) magnetograms from 2011 to 2016. We evaluate the models by comparing pairs of SDO/AIA images and the corresponding ones generated in 2017. Our main results from this study are as follows. First, the models successfully generate SDO/AIA-like solar UV and EUV images from SDO/HMI LOS magnetograms. Second, in view of three metrics (pixel-to-pixel correlation coefficient, relative error, and the percentage of pixels having errors less than 10%), the results from Model A are mostly comparable or slightly better than those from Model B. Third, in view of the rms contrast measure, the generated images by Model A are much more blurred than those by Model B because of L cGAN specialized for generating realistic images.