概率预报的序贯聚合--在风速极值预报中的应用
Sequential Aggregation of Probabilistic Forecasts -- Applicaton to Wind Speed Ensemble Forecasts
摘要：
在数值天气预报（NWP）领域中，大气未来状态的概率分布是用类似蒙特卡罗的模拟进行采样的，称为集合。这些集合有缺陷（如条件偏差），可以通过统计后处理方法进行纠正。存在几个集合，可以用不同的统计方法进行校正。进一步的步骤是组合这些原始或后处理的集合。专家建议的预测理论使我们能够构建具有预测性能理论保证的组合算法。本文将这一理论应用于以逐步累积分布函数（CDF）形式发布的概率预测。该理论应用于风速预测，通过结合几个原始或后处理的合奏，被认为是CDF。本研究的第二个目标是探索两个预测性能标准的使用：连续排名概率得分（CRPS）和Jolliffe-Primo检验。比较这两个标准得到的结果导致重新考虑通常的方式来建立熟练的概率预测，最小化的CRPS的基础上。根据Jolliffe-Primo检验，最小化CRPS并不一定会产生可靠的预测。Jolliffe-Primo检验通常选择可靠的预测，但可能导致发布CRPS方面的次优预测。建议使用这两个标准，以实现可靠和熟练的概率预测。
英文摘要：
In the field of numerical weather prediction (NWP), the probabilistic distribution of the future state of the atmosphere is sampled with Monte-Carlo-like simulations, called ensembles. These ensembles have deficiencies (such as conditional biases) that can be corrected thanks to statistical post-processing methods. Several ensembles exist and may be corrected with different statistiscal methods. A further step is to combine these raw or post-processed ensembles. The theory of prediction with expert advice allows us to build combination algorithms with theoretical guarantees on the forecast performance. This article adapts this theory to the case of probabilistic forecasts issued as step-wise cumulative distribution functions (CDF). The theory is applied to wind speed forecasting, by combining several raw or post-processed ensembles, considered as CDFs. The second goal of this study is to explore the use of two forecast performance criteria: the Continous ranked probability score (CRPS) and the Jolliffe-Primo test. Comparing the results obtained with both criteria leads to reconsidering the usual way to build skillful probabilistic forecasts, based on the minimization of the CRPS. Minimizing the CRPS does not necessarily produce reliable forecasts according to the Jolliffe-Primo test. The Jolliffe-Primo test generally selects reliable forecasts, but could lead to issuing suboptimal forecasts in terms of CRPS. It is proposed to use both criterion to achieve reliable and skillful probabilistic forecasts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
