Ensemble forecasting of major solar flares: methods for combining models
Ensemble forecasting of major solar flares: methods for combining models
复制标题
主要太阳耀斑的集合预报：组合模型的方法
DOI：
10.1051/swsc/2020042
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发表时间：
2020
影响因子：
3.3
通讯作者：
P. Gallagher
中科院分区：
文献类型：
--
作者：
J. A. Guerra;S. Murray;S. Murray;D. S. Bloomfield;P. Gallagher;P. Gallagher
实用空间天气预报的重要组成部分之一是太阳耀斑的预测。现在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。空间天气研究人员越来越多地寻求陆地天气界使用的方法来改进当前的预报技术。集合预报多年来一直用于数值天气预报，作为一种组合不同预测以获得更准确结果的方法。在这里，我们通过线性组合一组业务预测方法（ASAP、ASSA、MAG4、MOSWOC、NOAA 和 MCSTAT）的全盘概率预测来构建主要太阳耀斑的集合预测。每种方法的预测均按一个因素进行加权，该因素考虑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）。研究发现，大多数合奏团比任何成员单独取得了更好的技能指标（5% 到 15% 之间）。此外，超过 90% 的集成（通过预测属性衡量）比简单的等权重平均值表现更好。最后，集成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，它们估计小于 20%。这种简单的多模型、线性集合技术可以为运营空间气象中心提供构建多功能集合预报系统的基础——这是一个改进的预报起点，可以根据不同的最终用户需求进行定制。
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.
自动太阳活动预测：使用机器学习和太阳成像自动预测太阳耀斑的混合计算机平台
影响因子：
3.7
作者：
Colak T
通讯作者：
Colak T