结合光球磁场特征物理量的质子事件短期预报

利用描述太阳活动区光球磁场复杂性和非势性特征的三个物理量(纵向磁场最大水平梯度|▽_hB_z |_m, 强梯度中性线长度L, 孤立奇点数目η)建立了质子事件短期预报模型, 验证了磁场特征物理量对质子事件短期预报的有效性. 目前已建立或使用的太阳质子事件短期预报模型中仍然没有正式将磁场特征物理量作为预报因子. 由于太阳质子事件是小概率事件, 其物理产生机制尚不完全清楚, 这些预报模型往往存在虚报率偏高或报准率偏低的问题. 本文试图将原有质子事件模型所用的传统因子与磁场特征物理量结合起来, 利用神经网络方法建立一个更为有效的质子事件短期预报模型. 利用1997--2001年的训练数据集1871个样本建立了输入层为传统预报因子的模型A以及输入层为传统预报因子和磁场特征物理量的模型B. 通过对2002--2003年973个样本的测试数据集进行模拟预报发现, 模型A与B在具有相同质子事件报准率的情况下, 模型B的虚报率明显降低. 这进一步验证了磁场特征物理量在质子事件短期预报中的作用, 进而可以加强对太阳质子事件的实际预报能力. 

SPE Short-term Forecast With the Photospheric Magnetic Field Properties and Traditional Forecast Factors

In Ref.1] a simple Solar Proton Events (SPE) short-term forecast model is built with three solar photospheric magnetic physical properties (the maximum horizontal gradient of longitudinal magnetic field |▽_hB_z |_m, the length of neutral line with strong gradients L, and the number of singular points η), which suggested magnetic physical properties are effective in forecasting SPE. The traditional SPE forecasting models, which have not used magnetic physical properties as input parameters, often have low Probably of Detections (POD) or high False Alarm Rates (FAR) for SPE. This paper built a more effective SPE short-term forecasting model with the traditional SPE forecasting parameters and magnetic physical properties by BP neural network. Model A uses only the traditional SPE forecasting parameters and Model B uses the traditional SPE forecasting parameters as well as those three magnetic physical parameters. In testing 973 samples during 2002--2003, Model A and B have the same POD while Model B has a lower FAR than Model A. It further testified that magnetic physical properties are effective for forecasting SPE. And the most important thing is that it will largely improve our practical ability in forecasting SPE.