Model Evaluation Guidelines for Geomagnetic Index Predictions
Model Evaluation Guidelines for Geomagnetic Index Predictions
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DOI：
10.1029/2018sw002067
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发表时间：
2018-12
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影响因子：
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通讯作者：
M. Liemohn;J. McCollough;V. Jordanova;C. Ngwira;Steven K. Morley;C. Cid;W. Tobiska;P. Wintoft
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作者：
M. Liemohn;J. McCollough;V. Jordanova;C. Ngwira;Steven K. Morley;C. Cid;W. Tobiska;P. Wintoft
地磁指数是一个方便的量，它将近地空间某些区域或方面的复杂物理学提取为一个单一的参数。大多数最著名的指数都是从地面磁力计数据集计算出来的，如Dst、SYM-H、Kp、AE、AL和PC。已经创建了许多模型来预测这些指数的值，通常使用地球上游的太阳风测量值作为计算的输入变量。本文件审查了预测地磁指数的模型的现状和用于评估其再现目标指数时间序列的能力的方法。这些现有的方法被合成为基准的新的或更新的地磁指数预测模型的基准的度量的基线集合。这些方法分为两类：（1）拟合性能指标，如均方根误差和平均绝对误差，应用于模型输出和观测值的时间序列比较，以及（2）事件检测性能指标，如Heidke技能评分和检测概率，来自比较模型和观测值超过（或不超过）阈值的列联表。一些例子的代码正在使用这套指标，指标评估的最佳实践，限制和不确定性的其他方面进行了讨论，包括使用地磁指数时要考虑的几个警告。
Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near‐Earth space into a single parameter. Most of the best‐known indices are calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root‐mean‐square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices.