HR: 11:05h
AN: NG42A-04 INVITED     [Abstracts]
TI: Development of Geomagnetic Data Assimilation Framework: the Challenges and Progress
AU: * Kuang, W
EM: Weijia.Kuang-1@nasa.gov
AF: Space Geodesy Laboratory, NASA GSFC, Greenbelt Road, Greenbelt, MD 20771 United States
AU: Tangborn, A
EM: tangborn@gmao.gsfc.nasa.gov
AF: GMAO, NASA GSFC, Greenbelt Road, Greenbelt, MD 20771 United States
AU: Sun, Z
EM: sunzhib1@math.umbc.edu
AF: Dept. Mathematics and Statistics,UMBC, 1000 Hilltop Circle, Baltimore, MD 21250 United States
AU: Liu, D
EM: ldon@jcet.umbc.edu
AF: JCET, UMBC, 1000 Hilltop Circle, Baltimore, MD 21250 United States
AU: Jiang, W
EM: jiang@bowie.gsfc.nasa.gov
AF: JCET, UMBC, 1000 Hilltop Circle, Baltimore, MD 21250 United States
AU: Sabaka, T
EM: sabaka@geomag.gsfc.nasa.gov
AF: Planetary Geodynamics Laboratory, NASA GSFC, Greenbelt Road, Greenbelt, MD 20771 United States
AU: Bloxham, J
EM: bloxham@geophysics.harvard.edu
AF: Dept. Earth & Planetary Sciences, Harvard University, 20 Oxford Street, Cambridge, MA 02135 United States
AB: The scientific significance of assimilating surface geomagnetic observations into numerical geodynamo models is most obvious for the improvements that can be made to the models: observations can be used to constrain and identify appropriate dynamics for numerical modeling, and to create a dynamically consistent estimate of the state of the Earth's core. This estimate is an essential component needed in order to predict Earth's magnetic environment changes. Supported by NASA and NSF, research groups in NASA GSFC, UMBC and Harvard University are working together to establish a framework for geomagnetic data assimilation. Geomagnetic data assimilation faces new challenges in geomagnetism and geodynamo studies, because the work must necessarily bring together numerical modeling and surface observations, using an assimilation algorithm. These challenges include the differences between the parameter domains used in numerical dynamo modeling and that appropriate for the Earth's core. Also, surface observations can only provide a record on part of the poloidal magnetic field over a fraction of magnetic free decay time of the Earth's core. But foremost, we need an appropriate assimilation algorithm that will allow us to gain insight on model errors and the impact of observations on the physical quantities in the Earth's core. Our current effort involves three main projects: (1) synthetic geomagnetic data assimilation using model generated 'data', aiming at understanding the impact of parameter differences on geomagnetic data assimilation; (2) ensemble error covariance estimation, obtaining insight on how the corrections to the poloidal magnetic field at the core-mantle boundary (CMB) are correlated with other physical quantities; (3) geomagnetic data assimilation tests using real surface geomagnetic records (spectral coefficients from ufm and comprehensive field models) and an optimal interpolation scheme, to examine how numerical dynamo solutions are changed by the surface observations on time scales of several hundred years. These efforts lead to the development of the first geomagnetic data assimilation framework, which includes the three major components: geomagnetic field modeling, geodynamo modeling and data assimilation.
UR: http://mosst.gsfc.nasa.gov
DE: 0520 Data analysis: algorithms and implementation
DE: 1507 Core processes (1213, 8115)
DE: 1510 Dynamo: theories and simulations
DE: 1560 Time variations: secular and longer
DE: 4255 Numerical modeling (0545, 0560)
SC: Nonlinear Geophysics [NG]
MN: Fall Meeting 2005