HR: 1340h
AN: NG43B-0585    [Abstracts]
TI: Studies of synthetic observation data assimilation into geodynamo solutions: understanding the effects of Rayleigh number and changes in solution error due to data assimilation
AU: * liu, d
EM: Ldon@jcet.umbc.edu
AF: JCET/UMBC, 1000 hilltop circle, baltimore, md 21250 United States
AU: kuang, w
EM: kuang@bowie.gsfc.nasa.gov
AF: Goddard Space Flight Center, GSFC/NASA, greenbelt, md 20771 United States
AU: tangborn, a
EM: tangborn@gmao.gsfc.nasa.gov
AF: Goddard Space Flight Center, GSFC/NASA, greenbelt, md 20771 United States
AU: sun, z
EM: sunzhib1@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: Bloxham, j
EM: jeremy_bloxham@harvard.edu
AF: Depart of Earth and Planetary Sciences, harvard university, cambridge, ma 02138 United States
AB: One key problem in geomagnetic data assimilation is the uncertainty of the Rayleigh number R in the Earth's outer core that measures the driving force for convection. This uncertainty directly affects the leading order magnetohydrodynamic balance, and therefore the typical magnetic field strength in the core. To understand the effects of Rayleigh number and the geomagnetic data assimilation, we examine the dynamo model solutions for two different Rayleigh numbers R1 and R2 (R2 > R1), while the latter is used as our reference or `true' solution and therefore synthetic observation data were created based on this solution. The synthetic observation data is to be assimilated into the solution at Rayleigh numbers R1. The forecast solutions after synthetic observation data assimilation are then used to examine how numerical solutions are affected throughout the outer core by the assimilation near the core-mantle boundary (CMB). The objective here is to study how to better use data assimilation to bring the two sets of solutions as close as possible. By varying the assimilation time frequency and the assimilation depth (in radius), a set of parametric studies are conducted to better understand the inherent correlations among all field (state) variables, and solution error covariance. We carry out an analysis of the responses from the assimilated solution relative to the reference solution, particularly focusing on how the unobserved state variables (e.g. velocity and temperature) change due to the geomagnetic observation. The synthetic study provides some valuable information for and also paves the road to future geomagnetic data assimilation algorithms.
DE: 0520 Data analysis: algorithms and implementation
DE: 1510 Dynamo: theories and simulations
DE: 1517 Magnetic anomalies: modeling and interpretation
DE: 3315 Data assimilation
DE: 5440 Magnetic fields and magnetism
SC: Nonlinear Geophysics [NG]
MN: Fall Meeting 2005