Yuki Kubo

Bio: Yuki Kubo is an academic researcher from National Institute of Information and Communications Technology. The author has contributed to research in topics: Solar flare & Space weather. The author has an hindex of 14, co-authored 52 publications receiving 773 citations. Previous affiliations of Yuki Kubo include University of Electro-Communications.

Solar Flare Prediction Model with Three Machine-Learning Algorithms Using Ultraviolet Brightening and Vector Magnetogram

Abstract: We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 h. 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 magnetogram, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite. We detected active regions 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 (SVM), k-nearest neighbors (k-NN), and extremely randomized trees (ERT). The prediction score, the true skill statistic (TSS), was higher than 0.9 with a fully shuffled dataset, 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 the 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 h, all of which are strongly correlated with the flux emergence dynamics in an active region.

Development of the 3‐D MHD model of the solar corona‐solar wind combining system

Abstract: [1] In the framework of integrated numerical space weather prediction, we have developed a 3-D MHD simulation model of the solar surface-solar wind system. We report the construction method of the model and its first results. By implementing a grid system with angularly unstructured and increasing radial spacing, we realized a spherical grid that has no pole singularity and realized a fine grid size around the inner boundary and a wide-range grid up to a size of 1 AU simultaneously. The magnetic field at the inner boundary is specified by the observational data. In order to obtain the supersonic solar wind speed, parameterized source functions are introduced into the momentum and energy equations. These source functions decay exponentially in altitude as widely used in previous studies. The absolute values of the source functions are controlled so as to reflect the topology of the coronal magnetic field. They are increased inside the magnetic flux tube with subradial expansion and reduced inside the magnetic flux tube with overradial expansion. This adjustment aims to reproduce the variation of the solar wind speed according to the coronal magnetic structure. The simulation simultaneously reproduces the plasma-exit structure, the high- and low-temperature regions, the open and closed magnetic field regions in the corona, the fast and slow solar wind, and the sector structure in interplanetary space. It is confirmed from the comparison with observations that the MHD model successfully reproduces many features of both the fine solar coronal structure and the global solar wind structure.

Deep Flare Net (DeFN) Model for Solar Flare Prediction

Abstract: We developed a solar flare prediction model using a deep neural network (DNN), named Deep Flare Net (DeFN). The model can calculate the probability of flares occurring in the following 24 h in each active region, which is used to determine the most likely maximum classes of flares via a binary classification (e.g., >=M class versus =C class versus =10^7 K) and the X-ray and 131 A intensity data 1 and 2 h before an image. For operational evaluation, we divided the database into two for training and testing: the dataset in 2010-2014 for training and the one in 2015 for testing. The DeFN model consists of deep multilayer neural networks, formed by adapting skip connections and batch normalizations. To statistically predict flares, the DeFN model was trained to optimize the skill score, i.e., the true skill statistic (TSS). As a result, we succeeded in predicting flares with TSS=0.80 for >=M-class flares and TSS=0.63 for >=C-class flares. Note that in usual DNN models, the prediction process is a black box. However, in the DeFN model, the features are manually selected, and it is possible to analyze which features are effective for prediction after evaluation.

A Comparison of Flare Forecasting Methods. II. Benchmarks, Metrics and Performance Results for Operational Solar Flare Forecasting Systems.

Abstract: Solar flares are extremely energetic phenomena in our Solar System. Their impulsive, often drastic radiative increases, in particular at short wavelengths, bring immediate impacts that motivate solar physics and space weather research to understand solar flares to the point of being able to forecast them. As data and algorithms improve dramatically, questions must be asked concerning how well the forecasting performs; crucially, we must ask how to rigorously measure performance in order to critically gauge any improvements. Building upon earlier-developed methodology (Barnes et al, 2016, Paper I), international representatives of regional warning centers and research facilities assembled in 2017 at the Institute for Space-Earth Environmental Research, Nagoya University, Japan to - for the first time - directly compare the performance of operational solar flare forecasting methods. Multiple quantitative evaluation metrics are employed, with focus and discussion on evaluation methodologies given the restrictions of operational forecasting. Numerous methods performed consistently above the "no skill" level, although which method scored top marks is decisively a function of flare event definition and the metric used; there was no single winner. Following in this paper series we ask why the performances differ by examining implementation details (Leka et al. 2019, Paper III), and then we present a novel analysis method to evaluate temporal patterns of forecasting errors in (Park et al. 2019, Paper IV). With these works, this team presents a well-defined and robust methodology for evaluating solar flare forecasting methods in both research and operational frameworks, and today's performance benchmarks against which improvements and new methods may be compared.

Features of Particle and Heavy Ion Transport code System (PHITS) version 3.02

Abstract: We have upgraded many features of the Particle and Heavy Ion Transport code System (PHITS) and released the new version as PHITS3.02. The accuracy and the applicable energy ranges of the code were ...

「Earth,Planets and Space」のオープンアクセス出版

Abstract: ▶ Gathers original articles on topics in earth and planetary sciences ▶ Coverage includes geomagnetism, aeronomy, space science, seismology, volcanology, geodesy and planetary science ▶ Official journal of the Society of Geomagnetism and Earth, Planetary and Space Sciences, The Seismological Society of Japan, The Volcanological Society of Japan, The Geodetic Society of Japan, and The Japanese Society for Planetary Sciences

In Situ Observations of Interstellar Plasma with Voyager 1

Abstract: Launched over 35 years ago, Voyagers 1 and 2 are on an epic journey outward from the Sun to reach the boundary between the solar plasma and the much cooler interstellar medium. The boundary, called the heliopause, is expected to be marked by a large increase in plasma density, from about 0.002 per cubic centimeter (cm−3) in the outer heliosphere, to about 0.1 cm−3 in the interstellar medium. On 9 April 2013, the Voyager 1 plasma wave instrument began detecting locally generated electron plasma oscillations at a frequency of about 2.6 kilohertz. This oscillation frequency corresponds to an electron density of about 0.08 cm−3, very close to the value expected in the interstellar medium. These and other observations provide strong evidence that Voyager 1 has crossed the heliopause into the nearby interstellar plasma.

In Situ Observations of Interstellar Plasma With Voyager 1 (Invited)

Abstract: Finally Out Last summer, it was not clear if the Voyager 1 spacecraft had finally crossed the heliopause—the boundary between the heliosphere and interstellar space. Gurnett et al. (p. 1489, published online 12 September) present results from the Plasma Wave instrument on Voyager 1 that provide evidence that the spacecraft was in the interstellar plasma during two periods, October to November 2012 and April to May 2013, and very likely in the interstellar plasma continuously since the series of boundary crossings that occurred in July to August 2012. Electron densities detected by Voyager 1 show that the spacecraft is in the interstellar plasma. Launched over 35 years ago, Voyagers 1 and 2 are on an epic journey outward from the Sun to reach the boundary between the solar plasma and the much cooler interstellar medium. The boundary, called the heliopause, is expected to be marked by a large increase in plasma density, from about 0.002 per cubic centimeter (cm−3) in the outer heliosphere, to about 0.1 cm−3 in the interstellar medium. On 9 April 2013, the Voyager 1 plasma wave instrument began detecting locally generated electron plasma oscillations at a frequency of about 2.6 kilohertz. This oscillation frequency corresponds to an electron density of about 0.08 cm−3, very close to the value expected in the interstellar medium. These and other observations provide strong evidence that Voyager 1 has crossed the heliopause into the nearby interstellar plasma.

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

Abstract: The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.