Forecasting Geomagnetic Activity Using Kalman Filters

Details Forecasting Geomagnetic Activity Using Kalman Filters Forecasting Geomagnetic Activity Using Kalman Filters

May 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006agusmsm41d..07v&link_type=abstract

American Geophysical Union, Fall Meeting 2007, abstract #SM41D-07

Physics

7839 Nonlinear Phenomena (4400, 6944), 2722 Forecasting (7924, 7964), 2784 Solar Wind/Magnetosphere Interactions, 2788 Magnetic Storms And Substorms (7954), 3238 Prediction (3245, 4263)

Scientific paper

The coupling of energy from the solar wind to the magnetosphere leads to the geomagnetic activity in the form of storms and substorms and are characterized by indices such as AL, Dst and Kp. The geomagnetic activity has been predicted near-real time using local linear filter models of the system dynamics wherein the time series of the input solar wind and the output magnetospheric response were used to reconstruct the phase space of the system by a time-delay embedding technique. Recently, the radiation belt dynamics have been studied using a adaptive linear state space model [Rigler et al. 2004]. This was achieved by assuming a linear autoregressive equation for the underlying process and an adaptive identification of the model parameters using a Kalman filter approach. We use such a model for predicting the geomagnetic activity. In the case of substorms, the Bargatze et al [1985] data set yields persistence like behaviour when a time resolution of 2.5 minutes was used to test the model for the prediction of the AL index. Unlike the local linear filters, which are driven by the solar wind input without feedback from the observations, the Kalman filter makes use of the observations as and when available to optimally update the model parameters. The update procedure requires the prediction intervals to be long enough so that the forecasts can be used in practice. The time resolution of the data suitable for such forecasting is studied by taking averages over different durations.

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