Decision theory and the analysis of rare event space weather forecasts
Decision theory and the analysis of rare event space weather forecasts
复制标题
决策理论与罕见事件空间天气预报分析
DOI：
--
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
Daniel N. Baker
中科院分区：
文献类型：
--
作者：
R. Weigel;T. Detman;E. Rigler;Daniel N. Baker
从决策理论的几个基本结果，适用于罕见事件的预测进行审查，并提出了一种替代方法比较罕见事件的预测。一个基本的结果是，对于一个大类的用户只感兴趣的经济效用，相关的性能量是正确的和错误的报警预测的数量。这与大多数预测模型被优化以具有高数据-模型相关性的现实形成对比，这并不总是对应于最大的经济效用。Wilks（2001）开发的价值评分（VS）部分解决了建模者和用户相关指标之间的脱节。虽然价值分数更接近于用户最可能感兴趣的内容，但是对于成本和收益取决于预测事件的幅度的现实情况，最大VS不一定对应于最大效用。提出了一种替代的比较和呈现方法，可以解决这个问题。对于考虑的用户类，模型性能的完整规范需要计算在几个振幅水平和警告时间跨度下的正确、错误警报和错过预测的概率。为建模者和用户所涉及的计算的例子给出了由Dst指数参数化的大振幅高能电子注量和磁暴的预测。
Several basic results from decision theory as applied to rare event forecasts are reviewed, and an alternative method for comparing rare event forecasts is presented. A fundamental result is that for a large class of users only interested in economic utility, the relevant performance quantity is the number of correct and false alarm forecasts. This is contrasted with the reality that most forecast models are optimized to have a high data‐model correlation, which does not always correspond to maximum economic utility. The value score (VS) developed by Wilks (2001) partially resolves this disconnect between modeler‐ and user‐relevant metrics. Although the value score is closer to what is most likely of interest to a user, maximal VS does not necessarily correspond to maximal utility for the realistic case where the cost and benefit are dependent on the amplitude of the forecasted event. An alternative comparison and presentation method is proposed which may resolve this problem. For the class of users considered, full specification of model performance requires computation of the probability of correct, false alarm, and missed forecasts at several amplitude levels and warning time spans. Examples of the computations involved for the modeler and user are given for predictions of large‐amplitude energetic electron fluence and geomagnetic storms parameterized by the Dst index.