The minimum spanning tree histogram as a verification tool for multidimensional ensemble forecasts
The minimum spanning tree histogram as a verification tool for multidimensional ensemble forecasts
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最小生成树直方图作为多维集合预测的验证工具
DOI：
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发表时间：
2004
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通讯作者：
D. Wilks
中科院分区：
文献类型：
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作者：
D. Wilks
最小生成树（MST）直方图是传统标量秩直方图背后思想的多元扩展。它列出的频率，在n个预测场合，MST长度的排名为每个合奏，在这样的长度，这是通过取代观察其合奏成员依次获得的组内。在原始形式下，它无法区分集合偏差和集合欠分散，也无法辨别方差较小的预报变量的贡献。使用缩放和去偏MST直方图诊断集合预报属性的说明，无论是合成高斯集合和实际集合预报的小样本。考虑到预测中的序列相关性，还列出了MST直方图和标量秩直方图对x 2临界值的调整，以评估秩均匀性。
The minimum spanning tree (MST) histogram is a multivariate extension of the ideas behind the conventional scalar rank histogram. It tabulates the frequencies, over n forecast occasions, of the rank of the MST length for each ensemble, within the group of such lengths that is obtained by substituting an observation for each of its ensemble members in turn. In raw form it is unable to distinguish ensemble bias from ensemble underdispersion, or to discern the contributions of forecast variables with small variance. The use of scaled and debiased MST histograms to diagnose attributes of ensemble forecasts is illustrated, both for synthetic Gaussian ensembles and for a small sample of actual ensemble forecasts. Also presented are adjustments to x 2 critical values for evaluating rank uniformity, for both MST histograms and scalar rank histograms, given serial correlation in the forecasts.