摘要
Constraining numerical geodynamo models with surface geomagnetic observationsis very important in many respects:it directly helps to improve numericalgeodynamo models,and expands their geophysical applications beyond geomagnetism.A successful approach to integrate observations with numerical models isdata assimilation,in which Bayesian algorithms are used to combine observationaldata with model outputs,so that the modified solutions can then be used as initialconditions for forecasts of future physical states.In this paper,we present the firstgeomagnetic data assimilation framework,which comprises the MoSST core dynamicsmodel,a newly developed data assimilation component(based on ensemble covarianceestimation and optimal interpolation),and geomagnetic field models basedon paleo,archeo,historical and modern geomagnetic data.The overall architecture,mathematical formulation,numerical algorithms and computational techniques of theframework are discussed.Initial results with 100-year geomagnetic data assimilationand with synthetic data assimilation are presented to demonstrate the operation of thesystem.
基金
This research is supported by NSF Mathematical Geosciences program under the grants EAR-0327875 and EAR-0327843,NASA Solid Earth and Natural Hazard Program,NASA Mars Fundamental Research Program.We also thank GSFC NPPCS and NASA NAS for computation support.
