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.

翻译：不确定性量化方法在减少天气预报误差方面发挥着重要作用。天气预报中传统的不确定性量化方法依赖于基于物理模拟生成预报集合来估计不确定性。然而，为预测实时极端天气事件生成大量预报的计算成本高昂。证据深度学习是一种不确定性感知的深度学习方法，旨在仅使用一次预报即可提供其预测的置信度。该方法将学习视为证据获取过程，其中更多证据被解释为预测置信度的提升。我们使用真实世界气象数据集将证据深度学习应用于风暴预报，并将其性能与传统方法进行比较。我们的研究结果表明，证据深度学习不仅能降低计算开销，还能提升预测不确定性的表征能力。该方法为气候风险评估等研究领域开辟了新的机遇，在这些领域中量化未来气候的不确定性至关重要。