用幂均值聚合提升极端高温预测准确率
Power Ensemble Aggregation for Improved Extreme Event AI Prediction
- 采用幂均值非线性聚合多个模型预测结果
- 在高分位阈值下预测准确率显著提升
- 适合关注极端气候事件的气象与AI研究者
本文针对机器学习方法在极端气候事件（特别是热浪）预测中的挑战，将问题建模为分类任务：判断地表气温是否在指定时间段内超过局部第q分位数。核心发现是，使用幂均值聚合集成预测结果能显著提升分类器性能。通过使基于机器学习的天气预报模型具备生成能力，并结合该非线性聚合方法，其对极端高温事件的预测精度优于同一模型的常规均值预测。该幂聚合方法表现出良好适应性，其最优性能随分位数阈值变化，在更高极端值预测中效果更优。
原文摘要 · Abstract (English)
This paper addresses the critical challenge of improving predictions of climate extreme events, specifically heat waves, using machine learning methods. Our work is framed as a classification problem in which we try to predict whether surface air temperature will exceed its q-th local quantile within a specified timeframe. Our key finding is that aggregating ensemble predictions using a power mean significantly enhances the classifier's performance. By making a machine-learning based weather forecasting model generative and applying this non-linear aggregation method, we achieve better accuracy in predicting extreme heat events than with the typical mean prediction from the same model. Our power aggregation method shows promise and adaptability, as its optimal performance varies with the quantile threshold chosen, demonstrating increased effectiveness for higher extremes prediction.
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