Solar Activity Level Estimation and Solar Activity Prediction Xin Huang Huaning Wang Liyun Zhang

Main Contents Solar Active Longitude Prediction  Where solar activity often occurs Solar Activity Level Estimation  How to estimate activity level of active regions Solar Flare Prediction  Whether solar flares will happen

Solar active longitude prediction 1: phenomenon The differential rotation law: Ω is the angular velocity at a given latitude ϕ Ω 0 is the equatorial angular velocity B describes the differential rotation rate. Two persistent active longitudes of X-ray flares separated by about 180 ◦ existed on the sun by considering the differential rotation law

Solar active longitude prediction 2: method The latitude in the (i+1)th rotation is approximately the mean latitude of solar activity in the ith rotation (φ i ). Then, the predicted angular velocity of active longitudes in the (i+1)th rotation (Ω p (i+1)) can be given by The central position of active longitude in the kth day in the (i+1)th rotation (Λ pk (i+1) ) can be predicted as: & A simulated prediction of the central active longitudes in 2005 in the northern hemisphere.  Λ i1 is the central position of active longitude at the end of the ith rotation Predicted migration of active longitude in the kth day in the (i+1)th Carrington rotation

Solar active longitude prediction 3: performance N f is the number of flares occurred in the two longitude bands N t is the total number of all flares 23rd solar cycle

Solar activity level estimation 1: phenomenon Within a certain interval, more than one flare may occur. In order to consider the influence of all the flares, the total importance of these flares is : The sensitivity of parameters is measured by the flare productivity of parameters P(X): Active regions are characterized by maximum horizontal gradient of longitudinal magnetic field, the length of neutral line and the number of singular points. From 1996 to 2007, there are magnetograms containing 1055 active regions

Solar activity level estimation 2: method A1A1 A2A2 X0X0 W ∣▽ h B z ∣ m L η A 1 and A 2 are two asymptotic values W is approximate width between these two asymptotic values X 0 is the center of W Dimensionless parameter

Solar activity level estimation 3: example ×10 -6 W/m 2

Solar flare prediction 1: method 1-Nearest Neighbor

Solar flare prediction 2: performance 10-fold cross validation 

Summary (Zhang, et al 2007, )Active longitude can be predicted by considering the solar surface differential rotation (Zhang, et al 2007, 2011) Activity level of active regions can be estimated by the dimensionless method (Wang, et al 2009) Performance of solar flare prediction is improved by adding the information of active longitude

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