用单次预测实现风暴事件不确定性量化，降低计算成本。
Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events
- 基于证据深度学习，仅需一次预测即可获取置信度。
- 相比传统方法，计算开销减少且不确定性估计更精准。
- 适合气候风险评估等需量化未来不确定性的研究场景。
不确定性量化（UQ）在降低天气预报误差方面至关重要。传统天气预报的UQ依赖于物理模拟生成多个预报结果来估算不确定性，但实时预测极端天气事件时需大量预报，计算成本高昂。证据深度学习（EDL）是一种具备不确定性感知能力的深度学习方法，仅需一次预测即可提供预测置信度。该方法将学习视为证据积累过程，证据越多，预测置信度越高。我们利用真实气象数据集将EDL应用于风暴预报，并与传统方法对比。结果表明，EDL不仅显著降低计算开销，还能提升预测不确定性估计性能。该方法为气候风险评估等关键领域提供了新可能，其中对未来气候不确定性的量化至关重要。
原文摘要 · Abstract (English)
Uncertainty quantification (UQ) methods play an important role in reducing errors in weather forecasting. Conventional approaches in UQ for weather forecasting rely on generating an ensemble of forecasts from physics-based simulations to estimate the uncertainty. However, it is computationally expensive to generate many forecasts to predict real-time extreme weather events. Evidential Deep Learning (EDL) is an uncertainty-aware deep learning approach designed to provide confidence about its predictions using only one forecast. It treats learning as an evidence acquisition process where more evidence is interpreted as increased predictive confidence. We apply EDL to storm forecasting using real-world weather datasets and compare its performance with traditional methods. Our findings indicate that EDL not only reduces computational overhead but also enhances predictive uncertainty. This method opens up novel opportunities in research areas such as climate risk assessment, where quantifying the uncertainty about future climate is crucial.
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