Improving solar wind persistence forecasts: Removing transient space weather events, and using observations away from the Sun‐Earth line
Improving solar wind persistence forecasts: Removing transient space weather events, and using observations away from the Sun‐Earth line
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改进太阳风持续性预报：消除瞬态空间天气事件，并利用远离日地线的观测
DOI：
10.1002/2016sw001447
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
M. Owens
中科院分区：
文献类型：
--
作者：
P. Kohutova;Francois;E. Henley;M. Owens
这项研究展示了两个重要的方法，提高太阳风的持久性预测，利用环境太阳风的相对不变的性质，提供27天的预测，当使用拉格朗日L1点的数据。这种预测是有用的环境风的预测工具，并为基准的太阳风模型。我们表明，太阳风的持久性预测，可以提高通过删除瞬态太阳风的功能，如日冕物质抛射（CME）。使用CME指标自动识别1998年至2011年ACE数据中的CME污染期，并将其替换为来自先前会合旋转的太阳风，持久性预报得到改善（相对于基线）：Bz的技能得分，确定太阳风地球有效性的关键参数，当使用具有良好业务潜力的质子温度指标时，提高了7.7个百分点。我们还表明，持久性预报可以通过使用远离L1的测量来改善，以减少前置时间的成本，降低对整个会合期日冕稳定性的要求。使用STEREO B数据从2007年到2013年创建这样一个减少提前期的持久性预测，我们表明，Bz技能得分提高了17.1个百分点，相对于ACE。最后，我们报告的影响持久性预测从任何未来的任务，以L5拉格朗日点和成功的业务实施（2015年春季）的正常（ACE为基础的）和减少前置时间（STEREO为基础的）持久性预测在气象局的空间天气业务中心，以及未来的改进计划。
This study demonstrates two significant ways of improving persistence forecasts of the solar wind, which exploit the relatively unchanging nature of the ambient solar wind to provide 27 day forecasts, when using data from the Lagrangian L1 point. Such forecasts are useful as a prediction tool for the ambient wind, and for benchmarking of solar wind models. We show that solar wind persistence forecasts can be improved by removing transient solar wind features such as coronal mass ejections (CMEs). Using CME indicators to automatically identify CME‐contaminated periods in ACE data from 1998 to 2011, and replacing these with solar wind from a previous synodic rotation, persistence forecasts improve (relative to a baseline): skill scores for Bz, a crucial parameter for determining solar wind geoeffectiveness, improve by 7.7 percentage points when using a proton temperature‐based indicator with good operational potential. We also show that persistence forecasts can be improved by using measurements away from L1, to reduce the requirement on coronal stability for an entire synodic period, at the cost of reduced lead time. Using STEREO‐B data from 2007 to 2013 to create such a reduced lead time persistence forecast, we show that Bz skill scores improve by 17.1 percentage points relative to ACE. Finally, we report on implications for persistence forecasts from any future missions to the L5 Lagrangian point and on the successful operational implementation (in spring 2015) of the normal (ACE‐based) and reduced lead time (STEREO‐based) persistence forecasts in the Met Office's Space Weather Operations Centre, as well as plans for future improvements.