No abstract

No abstract

[1] We detected clear concentric waves and short-period oscillations in the ionosphere after an Enhanced Fujita scale (EF)5 tornado hit Moore, Oklahoma, U.S., on 20 May 2013 using dense wide-coverage ionospheric total electron content (TEC) observations in North America. These concentric waves were nondispersive, with a horizontal wavelength of~120 km and a period of~13 min. They were observed for more than 7 h throughout North America. TEC oscillations with a period of 4 min were also observed to the south of Moore for more than 8 h. A comparison between the TEC observations and infrared cloud image from the GOES satellite indicates that the concentric waves and short-period oscillations are caused by supercell-induced atmospheric gravity waves and acoustic resonances, respectively. This observational result provides the first clear evidence of a severe meteorological event causing atmospheric waves propagating upward in the upper atmosphere and reaching the ionosphere. Citation: Nishioka, M., T. Tsugawa, M. Kubota, and M. Ishii (2013), Concentric waves and short-period oscillations observed in the ionosphere after the 2013 Moore EF5 tornado, Geophys. Res. Lett., 40,[5581][5582][5583][5584][5585][5586]

high-latitudes to low-latitudes. The significant coincidence between the structures of 630 nm band airglow and total electron content indicates that the perturbations take place in the bottomside of the ionospheric F region. Coherent echoes from the field-aligned irregularities were observed by the MU radar in the nights when the TID activity was high.

We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010-2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite. We detected active regions (ARs) from the full-disk magnetogram, from which ∼60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.