2008/07/31 by Irina Kitiashvili, I. N. Kitiashvili, A. G. Kosovichev +1 · 1 citation
Computer Science · Earth and Planetary Sciences · Mathematics · Physics and Astronomy · #Climatology #Data assimilation #Dynamo #Dynamo theory #Ensemble Kalman filter #Environmental science #Extended Kalman filter #Geology #Geophysics and Gravity Measurements #Kalman filter #Magnetic field #Magnetic helicity #Mathematics #Meteorology #Physics #Solar Radiation and Photovoltaics #Solar and Space Plasma Dynamics #Solar cycle #Solar dynamo #Solar wind #Statistical physics #Statistics #Sunspot #astro-ph
paper · pdf · doi:10.1086/594999
published as Published in Astrophysical Journal Letters. Vol. 688, p. L49 - L52, 2008 · 10 pages, 3 figures
openalex publication_date 2008/10/29 · arxiv created 2009/02/11 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Despite the known general properties of the solar cycles, a reliable forecast of the 11 yr sunspot number variations is still a problem. The difficulties are caused by the apparent chaotic behavior of the sunspot numbers from cycle to cycle and by the influence of various turbulent dynamo processes, which we are far from understanding. For predicting the solar cycle properties we make an initial attempt to use the Ensemble Kalman Filter (EnKF), a data assimilation method, which takes into account uncertainties of a dynamo model and measurements, and allows us to estimate future observational data. We present the results of forecasting of the solar cycles obtained by the EnKF method in application to a low-mode nonlinear dynamical system modeling the solar α Ω -dynamo process with variable magnetic helicity. Calculations of the predictions for the previous sunspot cycles show a reasonable agreement with the actual data. This forecast model predicts that the next sunspot cycle will be significantly weaker (by ~30%) than the previous cycle, continuing the trend of low solar activity.