HR: 1330h
AN: GP13B-03    [Abstracts]
TI: Ensemble Calculation of Error Covariances in the MoSST Core Dynamics Model
AU: * Zhibin, S
EM: sunzhib1@umbc.edu
AF: Department of Mathematics and Statistics, University of Maryland- Baltimore County, Baltimore, MD 21250 United States
AU: Liu, D
EM: dliu@bowie.gsfc.nasa.gov
AF: JCET, University of Maryland-Baltimore County, Baltimore, MD 21250 United States
AU: Tangborn, A
EM: tangborn@gmao.gsfc.nasa.gov
AF: JCET, University of Maryland-Baltimore County, Baltimore, MD 21250 United States
AU: Jiang, W
EM: jiang@bowie.gsfc.nasa.gov
AF: JCET, University of Maryland-Baltimore County, Baltimore, MD 21250 United States
AU: Kuang, W
EM: kuang@bowie.gsfc.nasa.gov
AF: Space Geodesy Branch, Goddard Space Flight Center, Greenbelt, MD 20771 United States
AB: Data assimilation is the methodology by which observations are combined with a model output to get an improved estimate of the state of a system. An optimal estimate can potentially be obtained using Bayesian techniques, provided that good estimates of observation and model error statistics are available. Geomagnetic observations, through the geomagnetic field models, have well understood error characteristics. Geodynamo models, on the other hand, have not yet begun to develop the means to estimate error statistics. One approach to obtain these estimates, currently used in ocean and atmospheric data assimilation, is to carry out an ensemble of model runs that have initial conditions consisting of a converged geodynamo solution plus a random perturbation. The final state of these ensemble runs can then be used to estimate variance, spatial correlation and cross correlations between the different state variables (velocity, magnetic field and temperature). The correlations are especially important because magnetic field observations are made only at the Earth's surface and the depth to which observations need to correct the magnetic field needs to be well defined. Also, the cross correlations can be used to determine how much the observations should correct the velocity and temperature fields. We present the results of an ensemble calculation of background error covariances in a numerical geodynamo model. The covariances are presented in terms of spherical harmonic coefficients, as a function of radial position. Cross correlations are presented between magnetic, velocity and temperature fields. We discuss the implications of these correlations for carrying out geomagnetic data assimilation.
DE: 1507 Core processes (8115)
SC: Geomagnetism and Paleomagnetism [GP]
MN: 2005 Joint Assembly