Verification of cloud-fraction forecasts
Verification of cloud-fraction forecasts
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DOI：
10.1002/qj.481
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发表时间：
2009-07-01
影响因子：
8.9
通讯作者：
Illingworth, Anthony J.
中科院分区：
文献类型：
--
作者：
Hogan, Robin J.;O'Connor, Ewan J.;Illingworth, Anthony J.
云雷达和激光雷达可用于评估数值天气预报模式在预测云的时间和位置方面的技能，但由于云分数分布的非高斯性质，必须谨慎选择适当的技能度量。我们比较了许多不同的验证措施的性质，并得出结论，在现有的措施中，优势比的对数是最适合的云分数。我们还提出了一种新的度量，对称极端依赖分数，它具有非常吸引人的特性，是公平的（对于大样本），难以对冲和独立于被验证数量的发生频率。然后，我们使用来自五个欧洲地面站点和七个预测模型的数据，使用“Cloudnet”分析系统进行处理，以调查预测技能对云分数阈值（二元技能分数），高度，水平规模和（对于英国气象局和德国气象局模型）预测提前时间的依赖。研究发现，这些模式在预测边界层云的时间和位置方面最不熟练，而在预测中层云方面最熟练，尽管在后一种情况下，它们往往低估了有云时的平均云分。研究发现，技能随预测提前期呈近似反指数递减，从而可以估计预测的“半衰期”。当考虑瞬时模型快照的技能时，我们发现典型的值在2.5到4.5天之间。版权所有(C) 2009皇家气象学会
Cloud radar and lidar can be used to evaluate the skill of numerical weather prediction models in forecasting the timing and placement of clouds, but care must be taken in choosing the appropriate metric of skill to use due to the non-Gaussian nature of cloud-fraction distributions. We compare the properties of a number of different verification measures and conclude that of existing measures the Log of Odds Ratio is the most suitable for cloud fraction. We also propose a new measure, the Symmetric Extreme Dependency Score, which has very attractive properties, being equitable (for large samples), difficult to hedge and independent of the frequency of occurrence of the quantity being verified. We then use data from five European ground-based sites and seven forecast models, processed using the 'Cloudnet' analysis system, to investigate the dependence of forecast skill on cloud fraction threshold (for binary skill scores), height, horizontal scale and (for the Met Office and German Weather Service models) forecast lead time. The models are found to be least skillful at predicting the timing and placement of boundary-layer clouds and most skilful at predicting mid-level clouds, although in the latter case they tend to underestimate mean cloud fraction when cloud is present. It is found that skill decreases approximately inverse-exponentially with forecast lead time, enabling a forecast 'half-life' to be estimated. When considering the skill of instantaneous model snapshots, we find typical values ranging between 2.5 and 4.5 days. Copyright (C) 2009 Royal Meteorological Society