講演情報
[PEM12-P23]GAIA simulations of the ionospheric response to successive X-class solar flares on September 6, 2017
*松村 充1、塩川 和夫1、大塚 雄一1、品川 裕之2、陣 英克2、三好 勉信3、藤原 均4、垰 千尋2、津川 卓也2、新堀 淳樹1、渡邉 恭子5、西本 将平5、今田 晋亮1、川手 朋子6、李 京宣7 (1.名古屋大学宇宙地球環境研究所、2.情報通信研究機構、3.九州大学、4.成蹊大学、5.防衛大学、6.宇宙科学研究所、7.国立天文台)
キーワード：
電離圏、全電子数、太陽フレア
Solar flares enhance EUV and X-ray radiation to promote the ionization of the Earth's atmosphere, which increases the plasma density on the dayside ionosphere. Higher plasma density in the ionospheric F region degrades the Global Navigation Satellite System (GNSS), and in the E and D region can cause HF radio communication blackouts. The plasma density variation depends on the wavelength spectrum and temporal variation of the flare irradiance. They vary from flare to flare, and it is important to understand the various types of the ionospheric flare response. In this paper we focus on the two successive X-class flares that occurred on September 6, 2017: X2.2 peaking at 9:10 UT and X9.3 at 12:02 UT.
To understand how the ionosphere responded to the flares, we carried out numerical simulations using the Ground-to-topside model of Atmosphere and Ionosphere for Aeronomy (GAIA) [Jin et al., 2011]. We used the Flare Irradiance Spectral Model (FISM) [Chamberlin et al., 2007, 2008] to drive the GAIA. Model simulations showed that on the sunlit side, molecular ion density, which dominates the ionospheric E region, increased in several minutes and decayed in several hours in the same way as the two flare irradiances. The simulations also showed that atomic oxygen ion density, which dominates the ionospheric F region, increased with the first flare and sustained the enhancement until the second flare. Consequently, the density further increased with the second flare and the decay time was longer than that for single flare. When the molecular ion density was higher in the model, HF radio signals of ionosonde observations were under blackout. To further validate the model simulation, we will compare the simulated ion density with Total Electron Content (TEC) measured by ground GNSS receivers. We will also compare the simulated magnetic field variations with ones measured by ground magnetometers.
In addition, we will report another GAIA simulation with a physics-based flare irradiance model [Imada et al., 2011] instead of the FISM, an empirical model. We tuned the parameters of the physics-based irradiance model with statistical data of satellites so that the model can predict the irradiance spectra without measurement. We will compare preliminary results using the model with ones using the FISM.
To understand how the ionosphere responded to the flares, we carried out numerical simulations using the Ground-to-topside model of Atmosphere and Ionosphere for Aeronomy (GAIA) [Jin et al., 2011]. We used the Flare Irradiance Spectral Model (FISM) [Chamberlin et al., 2007, 2008] to drive the GAIA. Model simulations showed that on the sunlit side, molecular ion density, which dominates the ionospheric E region, increased in several minutes and decayed in several hours in the same way as the two flare irradiances. The simulations also showed that atomic oxygen ion density, which dominates the ionospheric F region, increased with the first flare and sustained the enhancement until the second flare. Consequently, the density further increased with the second flare and the decay time was longer than that for single flare. When the molecular ion density was higher in the model, HF radio signals of ionosonde observations were under blackout. To further validate the model simulation, we will compare the simulated ion density with Total Electron Content (TEC) measured by ground GNSS receivers. We will also compare the simulated magnetic field variations with ones measured by ground magnetometers.
In addition, we will report another GAIA simulation with a physics-based flare irradiance model [Imada et al., 2011] instead of the FISM, an empirical model. We tuned the parameters of the physics-based irradiance model with statistical data of satellites so that the model can predict the irradiance spectra without measurement. We will compare preliminary results using the model with ones using the FISM.