Prediction of Ionospheric Activity from the AL Index Time Series Using the Ensemble Transform Kalman Filter

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Tuesday, 6 January 2015: 4:30 PM
227A-C (Phoenix Convention Center - West and North Buildings)
Erin M. Lynch, University of Maryland, College Park, MD; and A. S. Sharma, E. Kalnay, and K. Ide

Ensemble data assimilation techniques, including the Ensemble Transform Kalman Filter (ETKF), have been successfully used to improve predictive skill in cases where a numerical model for forecasting has been developed. It is desirable to extend this utility to systems for which no model exists and observations of the complete state of the system may not be possible. For many natural systems, equations governing the evolution are unknown and only a partial observation of the high dimensional state vector is possible. For dissipative systems in which variables are coupled nonlinearly, the dimensionality of the phase space can be greatly reduced as the dynamics contracts onto a strange attractor. In these cases, it is possible to reconstruct the details of the phase space from a single scalar time series of observations. The magnetosphere is such a system with low dimensional underlying dynamics. The auroral electrojet indices, AL in particular, are constructed from measurements of the horizontal geomagnetic field component in the auroral oval at regular intervals. The values of these indices are linked to activity in the auroral zone and magnetospheric substorm development. From this scalar time series, the details of the phase space of the magnetosphere-solar wind system are reconstructed using Singular Spectrum Analysis. The ETKF is applied to ensemble forecasts made using model data constructed from a very long time series of the index and simulated observations of the index. The prediction skill improves with respect to persistence by incorporating information from observations and the behavior of nearby trajectories.