HR: 1340h
AN: SA13A-1071    [Abstracts]
TI: Numerical Simulation for Generalized Aurora Computed Tomography
AU: * Tanaka, Y
EM: ytanaka@nipr.ac.jp
AF: Research Organization of Information and Systems, 1-9-10 Kaga Itabashi-ku, Tokyo, 173- 8515, Japan
AU: Aso, T
EM: t-aso@nipr.ac.jp
AF: National Institute of Polar Research, 1-9-10 Kaga Itabashi-ku, Tokyo, 173-8515, Japan
AU: Gustavsson, B
EM: Bjorn.Gustavsson@phys.uit.no
AF: Department of Physics, University of Tromso, N9037, Tromso, N9037, Norway
AU: Tanabe, K
EM: tanabe.kunio@waseda.jp
AF: Faculty of Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo, 169-8555, Japan
AU: Kadokura, A
EM: kadokura@nipr.ac.jp
AF: National Institute of Polar Research, 1-9-10 Kaga Itabashi-ku, Tokyo, 173-8515, Japan
AU: Ogawa, Y
EM: yogawa@nipr.ac.jp
AF: National Institute of Polar Research, 1-9-10 Kaga Itabashi-ku, Tokyo, 173-8515, Japan
AB: The conventional method of aurora tomographic inversion is extended to a more generalized aurora computed tomography (CT). The generalized aurora CT is the method to reconstruct energy distribution of auroral precipitating electrons from multimodal data, such as electron density enhancement from the EISCAT radar and cosmic noise absorption (CNA) from imaging riometer, as well as auroral images. In this study, we evaluate the feasibility of the generalized aurora CT by numerical simulation. The forward problem is based on model calculation of auroral emission and electron density enhancement for incident electrons and the mapping of the results to the instruments. Assuming the energy and spatial distributions of the incident electrons, the three-dimensional (3D) distributions of volume emission rate and electron density are calculated. The data observed with the ALIS (Auroral Large Imaging System) cameras, the EISCAT radar, and the imaging riometer are obtained by mapping the volume emission rate and electron density to each instrument. We attempt to retrieve the initial distribution of precipitating electrons from the simulated observational data. The inversion analysis is based on the Bayesian inference, in which the problem is formulated as the maximization problem of posterior probability. The results are compared between the reconstruction from only auroral images and that from multimodal data.
DE: 2407 Auroral ionosphere (2704)
DE: 2431 Ionosphere/magnetosphere interactions (2736)
DE: 2455 Particle precipitation
DE: 2494 Instruments and techniques
DE: 2736 Magnetosphere/ionosphere interactions (2431)
SC: SPA-Aeronomy [SA]
MN: 2007 Fall Meeting