Prediction of solar activity cycles by the EnKF data assimilation method
Irina N. Kitiashvili, Stanford University, Stanford, CA; and A. G. Kosovichev
Solar activity is a primary factor determining the space weather and climate. However, because of the incomplete knowledge of dynamo processes inside Sun it is very difficult to make an accurate forecast of the activity level on the scale of sunspot cycles. We make an initial attempt to apply the Ensemble Kalman Filter (EnKF) method to assimilate the available sunspot data into a non-linear dynamo model, and predict solar cycles. The model is based on the classical Parker's formulation and includes variations of large-scale and turbulent magnetic helicities. The assimilation method takes into account uncertainties of the dynamo model and the observed sunspot number series. The method has been tested by calculating predictions of the past cycles and showed a reasonable agreement between the predicted and the actual data. After this, we have calculated a prediction for the upcoming solar cycle 23, and found that it will be approximately 30% weaker than the cycle 22, confirming some previous expectations. Our experience demonstrates the power of the data assimilation approach for predicting the solar variability, and suggests further development of this method for space weather forecasts.
Poster Session , Space Weather Posters
Monday, 18 January 2010, 2:30 PM-4:00 PM
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