To mitigate the impacts associated with adverse weather conditions, meteorological services issue weather warnings to the general public. These warnings rely heavily on forecasts issued by underlying prediction systems. When deciding which prediction system(s) to utilise to construct warnings, it is important to compare systems in their ability to forecast the occurrence and severity of extreme weather events. However, evaluating forecasts for extreme events is known to be a challenging task. This is exacerbated further by the fact that high-impact weather often manifests as a result of several confounding features, a realisation that has led to considerable research on so-called compound weather events. Both univariate and multivariate methods are therefore required to evaluate forecasts for high-impact weather. In this paper, we discuss weighted verification tools, which allow particular outcomes to be emphasised during forecast evaluation. We review and compare different approaches to construct weighted scoring rules, both in a univariate and multivariate setting, and we leverage existing results on weighted scores to introduce weighted probability integral transform (PIT) histograms, allowing forecast calibration to be assessed conditionally on particular outcomes having occurred. To illustrate the practical benefit afforded by these weighted verification tools, they are employed in a case study to evaluate forecasts for extreme heat events issued by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss).
翻译:为了减轻与恶劣天气条件有关的影响,气象部门向公众发出气象警告,这些警告主要依赖基础预报系统发布的预报。在决定使用哪些预报系统来建立警报时,必须比较能够预测极端天气事件的发生和严重程度的系统;然而,对极端事件的预测进行评估是一项具有挑战性的任务,而高影响天气往往由于若干混杂的特征而显现出来,从而导致对所谓的复合天气事件进行大量研究,使这种情况更加恶化。因此,需要采用单独和多种变异的方法来评价高影响天气的预报。在本文件中,我们讨论加权核查工具,以便在预测评价期间强调特定结果。我们审查并比较制定加权评分规则的不同方法,无论是在单一天气和多变情况下,我们利用加权计分的现有结果来引入加权概率整体变换(PIT)直图,从而能够对所谓的复合天气事件进行大量研究。因此,评价高影响天气预报校准的两种方法都需要用来评价高影响天气预报的预测。为了说明这些加权核查工具所提供的实际好处,在预测评价期间可以强调特定的结果。我们审查并比较不同的方法,以便用瑞士气象局的极端气象学案例研究评估。