By analyzing the observation data from Dongchuan Debris Flow Observation and Research Station and historical data from year 1965 to 1990 gotten from National Astronomical Observatories/ Yunnan Observatory, the respond...By analyzing the observation data from Dongchuan Debris Flow Observation and Research Station and historical data from year 1965 to 1990 gotten from National Astronomical Observatories/ Yunnan Observatory, the responding of debris flow in Jiangjia Ravine to Solar Proton Flare is studied. The following conclusion can be drawn. Solar Proton Flare, as one of most important astronomical factors, affects the activity of debris flow in Yunnan. Generally, from 1965 to 1990, the more active Solar Proton Flare is, the greater the probability of high frequency and large runoff of debris flow is. On the contrary, the less active Solar Proton Flare is, the greater the probability of low frequency, small runoff, and low sediment transport of debris flow is.展开更多
An ensemble prediction model of solar proton events （SPEs）, combining the information of solar flares and coronal mass ejections （CMEs）, is built. In this model, solar flares are parameterized by the peak flux, th...An ensemble prediction model of solar proton events （SPEs）, combining the information of solar flares and coronal mass ejections （CMEs）, is built. In this model, solar flares are parameterized by the peak flux, the duration and the longitude. In addition, CMEs are parameterized by the width, the speed and the measurement position angle. The importance of each parameter for the occurrence of SPEs is estimated by the information gain ratio. We find that the CME width and speed are more informative than the flare’s peak flux and duration. As the physical mechanism of SPEs is not very clear, a hidden naive Bayes approach, which is a probability-based calculation method from the field of machine learning, is used to build the prediction model from the observational data. As is known, SPEs originate from solar flares and/or shock waves associated with CMEs. Hence, we first build two base prediction models using the properties of solar flares and CMEs, respectively. Then the outputs of these models are combined to generate the ensemble prediction model of SPEs. The ensemble prediction model incorporating the complementary information of solar flares and CMEs achieves better performance than each base prediction model taken separately.展开更多
We analyze the line data from solar flares to present evidence for the emission spectrum of the recently discussed electron-proton pairs at high temperatures. We also point out that since the pairing phenomenon provid...We analyze the line data from solar flares to present evidence for the emission spectrum of the recently discussed electron-proton pairs at high temperatures. We also point out that since the pairing phenomenon provides an additional source for these lines—the conventional source being the highly ionized high-Z atoms already existing in the solar atmosphere, we have a plausible explanation of the FIP effect.展开更多
基金the Knowledge Innovation Program of Chinese Academy Sciences (KZX3-SW-352)Frontier Program of Institute of Mountain Hazards and Environment, CAS (C3200307)
文摘By analyzing the observation data from Dongchuan Debris Flow Observation and Research Station and historical data from year 1965 to 1990 gotten from National Astronomical Observatories/ Yunnan Observatory, the responding of debris flow in Jiangjia Ravine to Solar Proton Flare is studied. The following conclusion can be drawn. Solar Proton Flare, as one of most important astronomical factors, affects the activity of debris flow in Yunnan. Generally, from 1965 to 1990, the more active Solar Proton Flare is, the greater the probability of high frequency and large runoff of debris flow is. On the contrary, the less active Solar Proton Flare is, the greater the probability of low frequency, small runoff, and low sediment transport of debris flow is.
基金supported by the Young Researcher Grant of National Astronomical Observatories, Chinese Academy of Sciences, the National Basic Research Program of China (973 Program, Grant No. 2011CB811406)the National Natural Science Foundation of China (Grant Nos. 10733020, 10921303, 11003026 and 11078010)
文摘An ensemble prediction model of solar proton events （SPEs）, combining the information of solar flares and coronal mass ejections （CMEs）, is built. In this model, solar flares are parameterized by the peak flux, the duration and the longitude. In addition, CMEs are parameterized by the width, the speed and the measurement position angle. The importance of each parameter for the occurrence of SPEs is estimated by the information gain ratio. We find that the CME width and speed are more informative than the flare’s peak flux and duration. As the physical mechanism of SPEs is not very clear, a hidden naive Bayes approach, which is a probability-based calculation method from the field of machine learning, is used to build the prediction model from the observational data. As is known, SPEs originate from solar flares and/or shock waves associated with CMEs. Hence, we first build two base prediction models using the properties of solar flares and CMEs, respectively. Then the outputs of these models are combined to generate the ensemble prediction model of SPEs. The ensemble prediction model incorporating the complementary information of solar flares and CMEs achieves better performance than each base prediction model taken separately.
文摘We analyze the line data from solar flares to present evidence for the emission spectrum of the recently discussed electron-proton pairs at high temperatures. We also point out that since the pairing phenomenon provides an additional source for these lines—the conventional source being the highly ionized high-Z atoms already existing in the solar atmosphere, we have a plausible explanation of the FIP effect.
