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引用次数: 3

摘要

数值天气预报(NWP)模式的直接输出中没有预报不确定性信息。这些重要的信息在许多领域的最佳决策过程中都是必需的。预测区间是报告预测不确定性的重要形式。本文研究了一系列学习方法,通过统计后处理程序获得NWP系统历史性能的预测区间模型。本文研究了一些不同的分位数回归算法的应用,包括核分位数回归,以计算目标天气属性的预测区间。这些分位数回归方法以及最近提出的基于模糊聚类的分布拟合模型在一组涉及长达三年的每小时NWP预测和观测记录数据库的实验中进行了实际基准测试。研究了不同特征集和参数在模型中的作用。所得预测区间的预测技能不仅通过经典的交叉折叠验证试验进行评估,而且还采用了一种新的抽样变异过程来评估技能得分测量的不确定性。结果还显示了不同方法在预测区间预测的清晰度和可靠性等各个质量方面的比较。
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Learning uncertainty models from weather forecast performance databases using quantile regression
Forecast uncertainty information is not available in the immediate output of Numerical weather prediction (NWP) models. Such important information is required for optimal decision making processes in many domains. Prediction intervals are a prominent form of reporting the forecast uncertainty. In this paper, a series of learning methods are investigated to obtain prediction interval models by a statistical post-processing procedure involving the historical performance of an NWP system. The article investigates the application of a number of different quantile regression algorithms, including kernel quantile regression, to compute prediction intervals for target weather attributes. These quantile regression methods along with a recently proposed fuzzy clustering-based distribution fitting model are practically benchmarked in a set of experiments involving a three years long database of hourly NWP forecast and observation records. The role of different feature sets and parameters in the models are studied as well. The forecast skills of the obtained prediction intervals are evaluated not only by means of classical cross fold validation test experiments, but also subject to a new sampling variation process to assess the uncertainty of skill score measurements. The results show also how the different methods compare in terms of various quality aspects of prediction interval forecasts such as sharpness and reliability.
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