Synergy of Observations and Dynamo Models to Understand and Predict Solar Activity Cycles

[摘要] The long-standing problem of understanding the evolution of the global magnetic fields that drive solar activity through different temporal scales is becoming more tractable because, in addition to 400 years of sunspot records, we now have almost 4 solar cycles of magnetic field observations. These observations allow us to discern physical connections between dynamo model variables and observations using data assimilation analysis. In particular, the Ensemble Kalman Filter approach takes into account uncertainties in both observations and modeling and allows us to make reliable forecasts of solar cycle activity by using a relatively simple non-linear dynamical model of the solar dynamo. To expand this approach for more complex 2D and 3D dynamo modeling, it is necessary to decompose the observed synoptic magnetograms into poloidal and toroidal field components. In this presentation I will present initial results on magnetogram decomposition and assimilation of magnetogram data into dynamo modeling.

[发布日期] 2018-12-10

[学科分类] 天文学(综合)

[关键词] DATA CORRELATION;SOLAR OBSERVATORIES;DYNAMIC MODELS;FORECASTING;SOLAR ACTIVITY;SOLAR CYCLES;SUNSPOTS;MAGNETIC SIGNATURES;SOLAR MAGNETIC FIELD;KALMAN FILTERS;TOROIDAL PLASMAS;POLOIDAL FLUX