大太阳耀斑的包围预报：模型组合方法
Ensemble Forecasting of Major Solar Flares: Methods for Combining Models
摘要：
实用空间天气预报的一个基本组成部分是预测太阳耀斑。目前网上有多种耀斑预报方法，但仍不清楚哪种方法效果最好，而且没有一种方法比气候预报好得多。空间天气研究人员日益关注地面天气界使用的方法，以改进目前的预报技术。数值天气预报多年来一直使用包围预报，将不同的预报组合起来，以获得更准确的结果。在这里，我们构建集合预报的主要太阳耀斑的线性组合的全磁盘概率预报从一组业务预报方法（ASAP，ASSA，MAG 4，MOSWOC，NOAA，和MCSTAT）。每种方法的预测都由一个因子加权，该因子说明了该方法预测先前事件的能力，并考虑了几个性能指标（概率和分类）。据发现，大多数合奏实现了更好的技能指标（5 - 15%之间）比任何单独的成员。此外，超过90%的集合比简单的等权重平均值表现更好（通过预测属性测量）。最后，集合不确定性高度依赖于正在优化的内部度量，并且对于大于0.2的概率，估计它们小于20%。这种简单的多模型线性集合技术可为运行中的空间天气中心提供构建多功能集合预报系统的基础-这是其预报的一个改进的起点，可根据不同的最终用户需要进行调整。
英文摘要：
One essential component of operational space weather forecasting is the prediction of solar flares. With a multitude of flare forecasting methods now available online it is still unclear which of these methods performs best, and none are substantially better than climatological forecasts. Space weather researchers are increasingly looking towards methods used by the terrestrial weather community to improve current forecasting techniques. Ensemble forecasting has been used in numerical weather prediction for many years as a way to combine different predictions in order to obtain a more accurate result. Here we construct ensemble forecasts for major solar flares by linearly combining the full-disk probabilistic forecasts from a group of operational forecasting methods (ASAP, ASSA, MAG4, MOSWOC, NOAA, and MCSTAT). Forecasts from each method are weighted by a factor that accounts for the method's ability to predict previous events, and several performance metrics (both probabilistic and categorical) are considered. It is found that most ensembles achieve a better skill metric (between 5\% and 15\%) than any of the members alone. Moreover, over 90\% of ensembles perform better (as measured by forecast attributes) than a simple equal-weights average. Finally, ensemble uncertainties are highly dependent on the internal metric being optimized and they are estimated to be less than 20\% for probabilities greater than 0.2. This simple multi-model, linear ensemble technique can provide operational space weather centres with the basis for constructing a versatile ensemble forecasting system -- an improved starting point to their forecasts that can be tailored to different end-user needs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
