The Skill of Probabilistic Precipitation Forecasts under Observational Uncertainties within the Generalized Likelihood Uncertainty Estimation Framework for Hydrological Applications
The Skill of Probabilistic Precipitation Forecasts under Observational Uncertainties within the Generalized Likelihood Uncertainty Estimation Framework for Hydrological Applications
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水文应用广义似然不确定性估计框架内观测不确定性下的概率降水预报技巧
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
K. Bódis
中科院分区：
文献类型：
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作者：
F. Pappenberger;A. Ghelli;R. Buizza;K. Bódis
摘要本文介绍了一种评估集合预报的方法，该方法考虑了基于流域的降水平均值的观测不确定性。平均流域降水量的概率分布与广义李克森不确定性估计（GLUE）的方法。观测不确定性包括测量误差、雨量站网不均匀性引起的不确定性和插值方法引起的代表性误差。预测概率分布与观测场的接近程度使用Brier技能得分、秩直方图、相对熵以及集合传播与集合中值预测误差之间的比率（传播误差比）来衡量。已使用四种不同的方法对集水区的观测值进行插值。43天期间（2002年7月20日至8月31日）的结果显示，所使用的插值方法的敏感性很小。秩直方图和相对...
Abstract A methodology for evaluating ensemble forecasts, taking into account observational uncertainties for catchment-based precipitation averages, is introduced. Probability distributions for mean catchment precipitation are derived with the Generalized Likelihood Uncertainty Estimation (GLUE) method. The observation uncertainty includes errors in the measurements, uncertainty as a result of the inhomogeneities in the rain gauge network, and representativeness errors introduced by the interpolation methods. The closeness of the forecast probability distribution to the observed fields is measured using the Brier skill score, rank histograms, relative entropy, and the ratio between the ensemble spread and the error of the ensemble-median forecast (spread–error ratio). Four different methods have been used to interpolate observations on the catchment regions. Results from a 43-day period (20 July–31 August 2002) show little sensitivity to the interpolation method used. The rank histograms and the relative...