AI 中文总结
该研究扩展了Bz4Cast工具的统计验证，测试25次CME事件后发现其技能与SWPC资深值班预报员相当，仅虚警率略高，为空间天气预测提供了经验驱动模型的参考。
AI 中文摘要
来自太阳的日冕物质抛射（CME）会以地磁暴的形式对地球环境产生严重影响，这些地磁暴对全球技术基础设施构成风险，因此预测此类事件至关重要。本文扩展了Bz4Cast工具的统计验证，该工具是首个基于经验驱动的、可在日冕物质抛射到达地球前预测其内部太阳风磁矢量的模型。研究用Bz4Cast模型测试了2012年至2016年间的25次日冕物质抛射事件，并将其技能与美国国家海洋和大气管理局（NOAA）空间天气预测中心（SWPC）用于3天地磁暴预测的启发式方法（G级）进行了比较。在广泛的评分范围内及不确定性范围内，Bz4Cast架构提供的技能与SWPC的资深值班预报员相当。最显著的差异是，Bz4Cast架构的虚警率略高于SWPC的3天预报。
英文摘要
Coronal mass ejections (CMEs) from the Sun can have severe impacts on the Earth environment in the form of geomagnetic storms. These storms pose a risk to the global technological infrastructure, making the prediction of these events imperative. In this paper, we have broadened the statistical verification of the Bz4Cast tool, the first empirically-driven model to forecast solar wind magnetic vectors inside a CME prior to their Earth arrival. Twenty five CME events (between 2012 and 2016) have been tested with the Bz4Cast model, and the skills have been compared to the heuristic approach of NOAA's Space Weather Prediction Center (SWPC) G-scale for 3-day geomagnetic storm forecasts. For a broad range of scores, and within uncertainty, the Bz4Cast architecture provided the same skill as the experienced on-duty forecasters at SWPC. The most prominent difference is that the Bz4Cast architecture provides a slightly higher false alarm ratio than the SWPC 3-day forecast.
Comments5 pages, 6 Figures, 2 Tables, Published in Royal Meteorological Society, Weather
Journal refAustin, H.J. and Savani, N.P. (2018), Skills for forecasting space weather. Weather, 73: 362-366
DOI：10.1002/wea.3076
