日本地球惑星科学連合2015年大会

講演情報

インターナショナルセッション(口頭発表)

セッション記号 P (宇宙惑星科学) » P-EM 太陽地球系科学・宇宙電磁気学・宇宙環境

[P-EM07] Space Weather, Space Climate, and VarSITI

2015年5月26日(火) 11:00 〜 12:45 302 (3F)

コンビーナ:*片岡 龍峰(国立極地研究所)、海老原 祐輔(京都大学生存圏研究所)、三好 由純(名古屋大学太陽地球環境研究所)、清水 敏文(宇宙航空研究開発機構宇宙科学研究所)、浅井 歩(京都大学宇宙総合学研究ユニット)、陣 英克(情報通信研究機構)、佐藤 達彦(日本原子力研究開発機構)、草野 完也(名古屋大学太陽地球環境研究所)、宮原 ひろ子(武蔵野美術大学造形学部)、中村 卓司(国立極地研究所)、塩川 和夫(名古屋大学太陽地球環境研究所)、伊藤 公紀(横浜国立大学大学院工学研究院)、座長:清水 敏文(宇宙航空研究開発機構宇宙科学研究所)

11:30 〜 11:45

[PEM07-41] Prediction of MeV electron flux throughout the outer radiation belt by multivariate autoregressive model

*坂口 歌織1長妻 努1Harlan Spence2Geoffrey Reeves3 (1.情報通信研究機構、2.University of New Hampshire、3.Los Alamos National Laboratory)

キーワード:radiation belt, prediction, Van Alen Probes

The radiation belts are consisted of relativistic energy electrons in MeV range. The electron flux in the outer belt is highly variable depending on both solar wind and magnetospheric conditions. Enhanced fluxes sometimes cause deep dielectric charging on spacecraft and therefore satellite anomaly happens after the discharge. Prediction of such MeV electron variations is needed for safety operation of the satellite in the near Earth's orbit, but the physical processes of acceleration, loss, and transport of relativistic electrons are not fully understood so far. Japanese space weather information center at NICT has developed a multivariate autoregressive (AR) model for the prediction of electron flux at geostationary orbit (GEO). The model can estimates future flux variations by a few days lagging response of solar wind parameter changes [Sakaguchi et al., 2013]. Now, we have developed new models to predict electron flux variation throughout the outer radiation belt at L=3?6. Observation data of 2.3 MeV electrons in 2012-2014 by Van Allen Probes are used as predictor time series variate. The appropriate combinations of explanation variate are examined and selected respectively for each of L value (ΔL=0.2) model among geomagnetic indices (AE, Kp, Dst) as well as solar wind parameters (speed, BZ, BS, Pdyn). The combinations of these variates systematically change according to L-value shift. In the presentation, we show the estimation method of multivariate AR coefficient matrixes and discuss about estimated combinations of explanation variate. Also we show past prediction results that were validated by observation data based on two skill scores of prediction efficiency and persistence.