Space weather forecasts are generally made for events with an arbitrary size threshold imposed on an event statistical size distribution which is likely described by a power law. This is the case for solar energetic (E > 10 MeV) particle (SEP) events, which have a differential power law exponent of γ = 1.2. Event forecasts are usually evaluated by skill scores using a contingency table that matches the forecasted events against observed events independently of the event sizes. Each observed event is either a forecasted hit or a miss, and each forecasted event is either an observed hit or a false alarm. However, for SEP events and most other space weather parameters the event size is a critical factor for the user. It is more important that large events be well forecasted than threshold events. In addition, false alarms may be useful when they match observed events just below the forecast threshold. We explore a forecast evaluation scheme to incorporate the event size within the usual format of a binary contingency table to evaluate model performance. The scheme is applied to three different input options of a recently published evaluation of the Proton Prediction System (PPS) for SEP events to show differences between numbers-based and intensity-based skill scores of the PPS. We demonstrate how identical skill scores can result from models with extremely different performances of event intensity forecasts. The scheme requires model validation and would benefit from testing with other space weather applications.

中文翻译：

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根据事件大小探索应急技能得分

通常对具有任意大小阈值的事件进行空间天气预报，该大小阈值强加于事件统计大小分布，这很可能由幂定律描述。太阳高能（E  > 10 MeV）粒子（SEP）事件就是这种情况，其微分幂律指数为γ = 1.2。事件预测通常是使用权能表通过技能评分评估的，该表将预测的事件与观察到的事件相匹配，而与事件的大小无关。每个观察到的事件都是预测的命中或未命中，并且每个预测的事件都是观察到的命中或错误警报。但是，对于SEP事件和大多数其他空间天气参数，事件大小是用户的关键因素。与阈值事件相比，对大型事件进行更好的预测更为重要。此外，当虚假警报与恰好低于预测阈值的观察到的事件相匹配时，虚假警报可能会很有用。我们探索一种预测评估方案，以将事件大小合并到二进制列联表的常规格式中，以评估模型的性能。该方案应用于最近发布的SEP事件质子预测系统（PPS）评估的三个不同输入选项，以显示PPS的基于数字和基于强度的技能得分之间的差异。我们演示了事件强度预测的性能截然不同的模型如何产生相同的技能分数。该方案需要模型验证，并且将从其他太空天气应用的测试中受益。