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首页> 外文会议>2013 Joint IFSA World Congress and NAFIPS Annual Meeting >Non-parametric interval forecast models from fuzzy clustering of Numerical Weather Predictions
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Non-parametric interval forecast models from fuzzy clustering of Numerical Weather Predictions

机译：基于数值天气预报的模糊聚类的非参数区间预报模型

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摘要

Clustering methods are proposed and evaluated as post-processing techniques that can model the uncertainty of forecasts provided by Numerical Weather Prediction (NWP) systems. These techniques try to discover relevant information about forecast uncertainty that is inherent in the performance records of the system. We investigate the application of Fuzzy C-means clustering as a powerful unsupervised learning method to discover fuzzy sets of weather forecast situations which represent different forecast uncertainty patterns. These patterns are then utilized by different distribution fitting methods to obtain statistical prediction intervals which can express the expected accuracy of the NWP system output. Three years of weather forecast records in two weather stations are used in a set of experiments to empirically study the application of the proposed approach. Skills of the probabilistic forecasts obtained by these post-processing methods are investigated by considering cross fold validation and sampling variations. Results demonstrate that the Prediction intervals generated by the proposed procedure have a higher skill compared to baseline methods.
机译：提出了聚类方法并将其作为后处理技术进行评估，可以对数值天气预报（NWP）系统提供的不确定性进行建模。这些技术试图发现有关系统性能记录中固有的预测不确定性的相关信息。我们研究了模糊C均值聚类作为一种强大的无监督学习方法的应用，以发现代表不同预测不确定性模式的天气预报情况的模糊集。然后，通过不同的分布拟合方法利用这些模式来获得统计预测间隔，该统计预测间隔可以表示NWP系统输出的预期精度。在一组实验中使用了两个气象站中的三年天气预报记录，以实证研究该方法的应用。通过考虑交叉折叠验证和样本变异，研究了通过这些后处理方法获得的概率预测的技巧。结果表明，与基线方法相比，所提出的程序生成的预测间隔具有更高的技能。

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