Data assimilation in Parker’s dynamo model

Abstract

Application of the extended Kalman filter in the data assimilation problem of the one-dimensional (1D) Parker dynamo model is considered for two extreme cases: (1) when the magnetic field observations are taken uniformly across the entire surface of the liquid core of the Earth and (2) when the observations are taken at one spatial point. The algorithm allows the model solution with arbitrary initial conditions to be modified based on observations. It is shown that the redundancy of the noised data can reduce the efficiency of the recovery of the solution. Considerations on the applicability of data assimilation for the case of a higher dimension and a solution’s convergence dependent on the density of the flow of information assimilated are suggested.