JpGU-AGU Joint Meeting 2017

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[EJ] Oral

M (Multidisciplinary and Interdisciplinary) » M-GI General Geosciences, Information Geosciences & Simulations

[M-GI29] [EJ] Data-driven analysis, modeling and prediction in geosciences

Sat. May 20, 2017 3:30 PM - 5:00 PM 102 (International Conference Hall 1F)

convener:Tatsu Kuwatani(Japan Agency for Marine-Earth Science and Technology), Dmitri Kondrashov(University of California, Los Angeles), Hiromichi Nagao(Earthquake Research Institute, The University of Tokyo), Sergey Kravtsov(University of Wisconsin Milwaukee), Chairperson:Kenta Yoshida(Japan Agency for Marine-earth Science and Technology), Chairperson:Dmitri Kondrashov(University of California, Los Angeles,)

4:45 PM - 5:00 PM

[MGI29-18] Solar terrestrial modelling: Application of systems methodologies

*Simon N Walker1, Michael Balikhin1, Richard Boynton1 (1.ACSE, University of Sheffield, Sheffield, UK)

Keywords:systems modelling, magnetospheric processes, solar wind response

The response of the magnetosphere to changes in the solar wind is the result of the a complex series of processes, each acting over disparate scales in both space and time. The basic premise of physics based modelling is to understand each of these processes separately before coupling them into a single model. This diversity in process mechanisms and their temporal/spatial scales is one of the main reasons that such models have not been developed. Systems science provides a complementary route for modeling. This data driven approach involves the study of the evolution of a system as a whole based on a set of driving parameters. In this presentation we show how the application of systems modelling can be used to investigate such complex problems in space physics as magnetospheric response to the solar wind to the evolution of turbulence. In contrast to other data driven methodologies, systems techniques can also advance understanding of the micro-processes within the system. In addition, use of the systems approach, and especially frequency domain analysis, may be employed to validate analytical and numerical models.