Multivariable IntegratedEvaluation of Model Performance with the Vector Field Evaluation Diagram
Multivariable IntegratedEvaluation of Model Performance with the Vector Field Evaluation Diagram
复制标题
用向量场评价图对模型性能进行多变量综合评价
DOI：
10.5194/gmd-10-3805-2017
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
符淙斌
中科院分区：
文献类型：
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作者：
徐忠峰;韩瑛;符淙斌
抽象的。本文提出了一种多变量综合评价（MVIE）方法来衡量气候模式在模拟多场时的总体性能。MVIE的一般思想是将各种标量场分组为向量场，并使用向量场评估（VFE）图将构造的向量场与观察到的向量场进行比较。根据向量场均方根长度（RMSL）、向量场相似系数和向量均方根偏差（RMSVD）三个统计量之间的余弦关系设计了VFE图。这三个统计量可以合理地表示两个多维向量场之间的对应统计量。因此，人们可以使用VFE图来总结多个标量场的三个统计量，并便于模型性能的相互比较。VFE图可以说明各个字段的总均方根偏差有多少是由于均方根值的差异造成的，有多少是由于模式相似性差造成的。MVIE方法可以根据应用灵活地应用于全场（包括均值和异常）或异常场。我们还提出了一个多变量综合评价指数（MIEI），它考虑了多个标量场的幅度和模式相似性。MIEI有望在模拟多个领域时提供更准确的模型性能评估。MIEI、VFE图和常用的单个变量的统计度量构成了一个层次化的评估方法，可以对模型性能进行更全面的评估。
Abstract. This paper develops a multivariable integrated evaluation (MVIE) method to measure the overall performance of climate model in simulating multiple fields. The general idea of MVIE is to group various scalar fields into a vector field and compare the constructed vector field against the observed one using the vector field evaluation (VFE) diagram. The VFE diagram was devised based on the cosine relationship between three statistical quantities: root mean square length (RMSL) of a vector field, vector field similarity coefficient, and root mean square vector deviation (RMSVD). The three statistical quantities can reasonably represent the corresponding statistics between two multidimensional vector fields. Therefore, one can summarize the three statistics of multiple scalar fields using the VFE diagram and facilitate the intercomparison of model performance. The VFE diagram can illustrate how much the overall root mean square deviation of various fields is attributable to the differences in the root mean square value and how much is due to the poor pattern similarity. The MVIE method can be flexibly applied to full fields (including both the mean and anomaly) or anomaly fields depending on the application. We also propose a multivariable integrated evaluation index (MIEI) which takes the amplitude and pattern similarity of multiple scalar fields into account. The MIEI is expected to provide a more accurate evaluation of model performance in simulating multiple fields. The MIEI, VFE diagram, and commonly used statistical metrics for individual variables constitute a hierarchical evaluation methodology, which can provide a more comprehensive evaluation of model performance.