美国东北部阵风预测的不确定性量化：基于证据神经网络与可解释人工智能的方法
Uncertainty Quantification of Wind Gust Predictions in the Northeast United States: An Evidential Neural Network and Explainable Artificial Intelligence Approach
AI总结：
针对阵风预测低估高阵风的问题，提出证据神经网络（ENN）结合WRF模型进行不确定性量化，相较WRF使RMSE降低47%且无需集合即可构建预测区间，并利用可解释AI揭示不确定性来源，提升了极端阵风预报的业务应用价值。
摘要：
机器学习算法在减少阵风预测偏差方面展现出潜力，但仍低估高阵风。不确定性量化（UQ）通过识别预测何时可靠或需谨慎解读来解决此问题。利用美国东北部61个温带风暴的数据，我们引入证据神经网络（ENN）作为阵风预测UQ的新方法，利用天气研究与预报（WRF）模型的大气变量。可解释AI技术表明，关键预测特征导致了更高的不确定性，这与风暴强度和空间阵风梯度强相关。与WRF相比，ENN的RMSE降低了47%，并允许在无集合的情况下构建阵风预测区间，在266个站点中的179个成功捕获了至少95%的观测阵风。从业务角度看，提供带有量化不确定性的阵风预报可增强利益相关者对极端阵风事件风险评估和响应规划的信心。
英文摘要：
Machine learning algorithms have shown promise in reducing bias in wind gust predictions, while still underpredicting high gusts. Uncertainty quantification (UQ) supports this issue by identifying when predictions are reliable or need cautious interpretation. Using data from 61 extratropical storms in the Northeastern USA, we introduce evidential neural network (ENN) as a novel approach for UQ in gust predictions, leveraging atmospheric variables from the Weather Research and Forecasting (WRF) model. Explainable AI techniques suggested that key predictive features contributed to higher uncertainty, which correlated strongly with storm intensity and spatial gust gradients. Compared to WRF, ENN demonstrated a 47% reduction in RMSE and allowed the construction of gust prediction intervals without an ensemble, successfully capturing at least 95% of observed gusts at 179 out of 266 stations. From an operational perspective, providing gust forecasts with quantified uncertainty enhances stakeholders' confidence in risk assessment and response planning for extreme gust events.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
