特定地点大气变量阈值超出的预测
Forecasting threshold exceedance of atmospheric variables at a specific location
- Laboratoire Sciences Pour L’Environnement, UMR 6134, CNRS Université de Corse（环境科学实验室，UMR 6134，CNRS科西嘉大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究比较了直接概率法和全分布概率法在预测特定地点大气变量（如温度和风速）阈值超出时的表现，发现全分布方法在极端事件中更优，并指出其优势源于对条件分布整体特征的准确捕捉。
AI中文摘要：
本研究比较了两种方法论方法，用于在给定地点预测大气变量（如温度和风速）的阈值超出：（i）直接概率法，将超出视为二元分类问题；（ii）全分布概率法，对目标变量的完整条件概率律进行建模。通过理论分析和在玩具模型上的数值模拟，以及来自法国东南部MeteoNet数据集（2016-2018）的真实数据，我们证明全分布方法在罕见极端事件中始终优于直接方法。这一优势源于全分布方法能够从中等和轻度强度事件中有效学习条件分布的参数，从而在尾部实现更好的校准和区分。我们发现，所选分布的具体参数形状相对于准确捕捉其整体属性（即均值和方差）的可预测变化而言，起次要作用。这种经验上的不可区分性也揭示了驱动大气极值的物理机制，表明极端超出主要由整个分布的显著条件位移驱动，而非静态气候学中不可预测的肥尾异常。我们的结果在强地表风速和强小时降雨量上均得到验证，并使用适当评分规则（Brier分数、对数分数）和确定性技能分数（Peirce技能分数、CSI、HSS）评估性能。这些发现强调了全概率分布建模在罕见事件预测中的关键重要性，并为改进业务气象学中的极端天气预测提供了实用指导。
英文摘要：
Accurate short-term forecasting of extreme weather events is important for early warning and risk mitigation. We compare two approaches for predicting site-specific threshold exceedances of weather variables: direct binary probabilistic models trained on thresholded outcomes and full-distribution parametric models trained on the continuous target. Using an analytically tractable Gaussian random-location model, in which the distribution is predictably shifted by the covariates, we quantify the consequences of the information loss induced by thresholding and derive the rare-event behavior of prediction errors and forecast skill. The analysis predicts an increasing relative advantage of the full-distribution approach as event probability decreases, because binarization progressively discards information contained in the continuous response. We then compare the two approaches to forecast wind speed and accumulated rainfall at various weather station sites over southeastern France using the same hybrid neural-network architecture. Although wind speed and rainfall depart from the toy-model assumptions, its main qualitative predictions are recovered for both variables: the relative advantage of distributional modeling increases toward rarer thresholds. This agreement further suggests that a substantial fraction of the forecastable signal associated with extreme events arises from predictable shifts in the conditional distribution. For the reasonably suitable parametric families examined, the results show limited sensitivity to the selected class of distribution. Overall, the results highlight the statistical advantage of training on continuous observations rather than on thresholded binary outcomes when forecasting rare threshold exceedances.
